LiDAR applications in precision agriculture for cultivating crops: A review of recent advances
In recent years, Light Detection and Ranging (LiDAR) technology has been one of the most innovative subjects in laser scanning, remote sensing, and object detection systems. This technology may be popular because it can pinpoint structures or zones of interest in millimetre detail. It can also highl...
Saved in:
Published in | Computers and electronics in agriculture Vol. 207; p. 107737 |
---|---|
Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.04.2023
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | In recent years, Light Detection and Ranging (LiDAR) technology has been one of the most innovative subjects in laser scanning, remote sensing, and object detection systems. This technology may be popular because it can pinpoint structures or zones of interest in millimetre detail. It can also highlight variations and irregularities, such as surface degradation and vegetation growth. This paper presents a review of the specialised literature on LiDAR systems applied to precision agriculture; specifically, in cultivating crops. First, some preliminaries of LiDAR systems according to the mode of transport used, considering terrestrial, mobile, and aerial laser scanners, are given. Subsequently, a well-organised taxonomy of recent LiDAR applications based on the activity being performed is presented. Here, the following four categories are considered: (1) crop-related metric estimation, (2) tree and plant digitisation, (3) vision systems for object detection and navigation, and (4) planning and decision support. Lastly, we discuss some current trends and research challenges in applying LiDAR technology to cultivation activities in accordance with the state-of-the-art literature.
•Compared with photogrammetry, LiDAR technology often obtains more precise 3D models.•Voxelisation is reasonably accurate when applied to tree digitisation.•The crop’s health status is often monitored by estimating the NDVI, LAI or height.•The sunlight index and wood volume are adequate for evaluating pruning structures.•The most used LiDAR sensors are VLP-16, LMS400/111/511, UTM-30LX, and Focus X330. |
---|---|
AbstractList | In recent years, Light Detection and Ranging (LiDAR) technology has been one of the most innovative subjects in laser scanning, remote sensing, and object detection systems. This technology may be popular because it can pinpoint structures or zones of interest in millimetre detail. It can also highlight variations and irregularities, such as surface degradation and vegetation growth. This paper presents a review of the specialised literature on LiDAR systems applied to precision agriculture; specifically, in cultivating crops. First, some preliminaries of LiDAR systems according to the mode of transport used, considering terrestrial, mobile, and aerial laser scanners, are given. Subsequently, a well-organised taxonomy of recent LiDAR applications based on the activity being performed is presented. Here, the following four categories are considered: (1) crop-related metric estimation, (2) tree and plant digitisation, (3) vision systems for object detection and navigation, and (4) planning and decision support. Lastly, we discuss some current trends and research challenges in applying LiDAR technology to cultivation activities in accordance with the state-of-the-art literature. In recent years, Light Detection and Ranging (LiDAR) technology has been one of the most innovative subjects in laser scanning, remote sensing, and object detection systems. This technology may be popular because it can pinpoint structures or zones of interest in millimetre detail. It can also highlight variations and irregularities, such as surface degradation and vegetation growth. This paper presents a review of the specialised literature on LiDAR systems applied to precision agriculture; specifically, in cultivating crops. First, some preliminaries of LiDAR systems according to the mode of transport used, considering terrestrial, mobile, and aerial laser scanners, are given. Subsequently, a well-organised taxonomy of recent LiDAR applications based on the activity being performed is presented. Here, the following four categories are considered: (1) crop-related metric estimation, (2) tree and plant digitisation, (3) vision systems for object detection and navigation, and (4) planning and decision support. Lastly, we discuss some current trends and research challenges in applying LiDAR technology to cultivation activities in accordance with the state-of-the-art literature. •Compared with photogrammetry, LiDAR technology often obtains more precise 3D models.•Voxelisation is reasonably accurate when applied to tree digitisation.•The crop’s health status is often monitored by estimating the NDVI, LAI or height.•The sunlight index and wood volume are adequate for evaluating pruning structures.•The most used LiDAR sensors are VLP-16, LMS400/111/511, UTM-30LX, and Focus X330. |
ArticleNumber | 107737 |
Author | Florencia, Rogelio Sánchez-Solís, J. Patricia Porras, Raúl Rivera, Gilberto |
Author_xml | – sequence: 1 givenname: Gilberto orcidid: 0000-0002-2365-4651 surname: Rivera fullname: Rivera, Gilberto email: gilberto.rivera@uacj.mx – sequence: 2 givenname: Raúl orcidid: 0000-0002-6772-5351 surname: Porras fullname: Porras, Raúl email: raul.porras@uacj.mx – sequence: 3 givenname: Rogelio orcidid: 0000-0002-5208-6577 surname: Florencia fullname: Florencia, Rogelio email: rogelio.florencia@uacj.mx – sequence: 4 givenname: J. Patricia orcidid: 0000-0001-5514-5061 surname: Sánchez-Solís fullname: Sánchez-Solís, J. Patricia email: julia.sanchez@uacj.mx |
BookMark | eNqFkM1OwzAQhC0EEm3hDTj4yCXFjp3E6QGpKr9SJSTUM5bjrCtXaRzspIi3xyWcOMBpZ1Yzq9U3RaetawGhK0rmlND8ZjfXbt-p7TwlKYuromDFCZpQUaRJEe0pmsSYSGheludoGsKORF-KYoLe1vZu-YpV1zVWq966NmDb4s6DtiE6rLbe6qHpBw_YOI-P2h5ist1i7V0XFniJPRwsfGBnotLQ9ljVB9VqCBfozKgmwOXPnKHNw_1m9ZSsXx6fV8t1ohkr-4RyrgzNtQBQFXCqOFQVhxSIKEyqGM2yQhuVpaKuSiZUZjgxGSO8ImWeUTZD1-PZzrv3AUIv9zZoaBrVghuCTAXjKeGciBjlYzQ-H4IHIztv98p_SkrkkabcyZGmPNKUI81YW_yqadt_8-q9ss1_5duxDBFBJOVl0BYin9pGXr2snf37wBf1VZXa |
CitedBy_id | crossref_primary_10_3390_agronomy14112473 crossref_primary_10_1016_j_compag_2024_108807 crossref_primary_10_1364_OE_533280 crossref_primary_10_7744_kjoas_510106 crossref_primary_10_3390_agriengineering6040225 crossref_primary_10_1016_j_atech_2025_100897 crossref_primary_10_1016_j_scienta_2024_113250 crossref_primary_10_1109_ACCESS_2024_3428401 crossref_primary_10_3390_agriculture15030243 crossref_primary_10_1109_ACCESS_2025_3526202 crossref_primary_10_3390_agriculture14111954 crossref_primary_10_3390_s24165409 crossref_primary_10_1016_j_aiia_2025_01_006 crossref_primary_10_3390_agronomy14061279 crossref_primary_10_1016_j_measurement_2024_114351 crossref_primary_10_1109_TIM_2024_3391816 crossref_primary_10_3390_s24061717 crossref_primary_10_1109_JSTARS_2024_3370508 crossref_primary_10_1051_e3sconf_202447104018 crossref_primary_10_3390_s24247894 crossref_primary_10_1016_j_culher_2024_07_007 crossref_primary_10_1016_j_rsase_2024_101418 crossref_primary_10_1016_j_softx_2023_101618 crossref_primary_10_1016_j_dib_2023_109686 crossref_primary_10_3390_agriculture14081378 crossref_primary_10_1016_j_jenvman_2024_122539 crossref_primary_10_1038_s41598_023_40128_2 crossref_primary_10_3390_agronomy14061181 crossref_primary_10_3390_earth5030027 crossref_primary_10_3390_agronomy13092312 crossref_primary_10_1016_j_eswa_2024_125145 crossref_primary_10_1371_journal_pone_0297153 crossref_primary_10_3390_land14010171 crossref_primary_10_3390_rs16040699 crossref_primary_10_1016_j_atech_2024_100718 crossref_primary_10_1016_j_compag_2024_108629 crossref_primary_10_29133_yyutbd_1517109 crossref_primary_10_3390_agronomy14081824 crossref_primary_10_1016_j_biosystemseng_2024_07_002 crossref_primary_10_1007_s44279_024_00078_3 crossref_primary_10_1016_j_atech_2025_100910 crossref_primary_10_3389_fsoil_2023_1305105 crossref_primary_10_1016_j_compag_2025_110023 crossref_primary_10_3390_machines12110750 crossref_primary_10_3390_rs16122191 crossref_primary_10_3390_s25061660 crossref_primary_10_3389_fpls_2023_1215899 crossref_primary_10_3390_agriculture15020175 crossref_primary_10_1109_JSTARS_2023_3312815 crossref_primary_10_3390_agronomy14102446 crossref_primary_10_1007_s43926_024_00066_5 crossref_primary_10_3390_s24206743 crossref_primary_10_1016_j_atech_2023_100344 crossref_primary_10_1016_j_eja_2024_127477 crossref_primary_10_1016_j_envsoft_2025_106404 crossref_primary_10_3390_rs16244623 crossref_primary_10_3389_fsufs_2023_1201994 crossref_primary_10_1109_TGRS_2024_3467674 crossref_primary_10_3390_agriculture14071122 crossref_primary_10_1016_j_apenergy_2024_122679 crossref_primary_10_3390_agriculture14020299 crossref_primary_10_3390_rs17020279 crossref_primary_10_1016_j_compag_2024_109187 crossref_primary_10_1109_JSEN_2024_3369657 crossref_primary_10_26848_rbgf_v17_6_p4761_4812 crossref_primary_10_17584_rcch_2024v18i2_17428 crossref_primary_10_3390_agriculture15060581 crossref_primary_10_1017_S0263574724000845 crossref_primary_10_3390_s23167212 crossref_primary_10_1016_j_compag_2025_110070 crossref_primary_10_19206_CE_202210 crossref_primary_10_3390_s24248035 crossref_primary_10_3390_rs16060985 crossref_primary_10_5897_AJAR2024_16714 crossref_primary_10_1016_j_compag_2024_109229 |
Cites_doi | 10.33584/jnzg.2019.81.414 10.1016/j.geoderma.2018.09.046 10.1016/j.compag.2016.09.014 10.1016/j.enggeo.2022.106615 10.1007/s11119-019-09672-8 10.1016/j.compag.2011.11.010 10.1016/j.cag.2017.04.004 10.5194/isprs-archives-XLI-B7-171-2016 10.3389/fpls.2018.00016 10.1016/j.compag.2017.02.006 10.3390/s20041102 10.3390/rs10122007 10.1016/j.scitotenv.2019.05.453 10.3390/rs14020431 10.1080/10106049.2017.1377774 10.1021/es980176p 10.3390/rs14030675 10.1016/j.compag.2018.08.020 10.3389/fpls.2022.815218 10.1109/OJIA.2020.3015253 10.1016/j.isprsjprs.2018.12.015 10.1016/j.geomorph.2021.108081 10.1016/j.geomorph.2021.107803 10.3390/rs12152481 10.1007/s11119-016-9474-5 10.3390/f13020285 10.1016/j.cj.2021.10.010 10.3390/s17122703 10.1080/01431161.2019.1584929 10.3390/agriculture12091450 10.1016/j.compag.2019.105121 10.1007/s11119-012-9295-0 10.3390/ijgi6080255 10.1038/s41598-020-62275-6 10.1016/j.compag.2018.08.034 10.3390/rs13173538 10.3390/rs13010020 10.1109/MRA.2020.3012492 10.3390/rs12213587 10.1016/j.biosystemseng.2019.08.017 10.3390/rs14051145 10.3390/agronomy12102409 10.3390/rs13163218 10.1016/j.compag.2018.01.022 10.1007/s10457-021-00697-5 10.1007/s00468-018-1704-1 10.25046/aj0506127 10.1109/JSTARS.2017.2781132 10.1016/j.ecolind.2022.109243 10.1029/2020EA001600 10.3390/f10020148 10.3390/horticulturae8020090 10.3390/agronomy12051074 10.1016/S0924-2716(99)00003-9 10.1002/agj2.20632 10.1109/WACV51458.2022.00178 10.1016/j.compag.2019.105158 10.1109/TGRS.2018.2866056 10.3390/rs14030642 10.1080/01431161.2020.1811917 10.3390/agriculture10050146 10.3390/rs14030585 10.1016/j.isprsjprs.2015.03.003 10.1109/LRA.2018.2849499 10.3390/agronomy10020197 10.3390/rs14102292 10.3390/rs14041048 10.1175/MWR-D-21-0166.1 10.1109/JSTARS.2022.3172491 10.1016/j.compag.2021.106274 10.3390/agriculture12081241 10.1364/AO.399766 10.1080/01431161.2021.2018149 10.3390/agronomy12102509 10.1785/0220210234 10.3390/s22051844 10.5194/isprs-annals-V-3-2022-193-2022 10.3390/s141224212 10.1016/j.compind.2018.03.023 10.31577/congeo.2021.51.4.3 10.1093/jxb/erl142 10.3390/s22041379 10.1371/journal.pone.0130479 10.2480/agrmet.D-18-00012 10.3390/agronomy9070403 10.1007/s12665-021-09869-z 10.1016/j.biosystemseng.2020.07.017 10.3390/rs12101647 10.3389/frobt.2022.832165 10.1016/j.biosystemseng.2008.10.009 10.3390/s18113731 10.1016/j.jhydrol.2020.124573 10.5194/isprs-archives-XLI-B8-365-2016 10.1007/s11119-019-09676-4 |
ContentType | Journal Article |
Copyright | 2023 Elsevier B.V. |
Copyright_xml | – notice: 2023 Elsevier B.V. |
DBID | AAYXX CITATION 7S9 L.6 |
DOI | 10.1016/j.compag.2023.107737 |
DatabaseName | CrossRef AGRICOLA AGRICOLA - Academic |
DatabaseTitle | CrossRef AGRICOLA AGRICOLA - Academic |
DatabaseTitleList | AGRICOLA |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Agriculture |
EISSN | 1872-7107 |
ExternalDocumentID | 10_1016_j_compag_2023_107737 S0168169923001254 |
GroupedDBID | --K --M .DC .~1 0R~ 1B1 1RT 1~. 1~5 29F 4.4 457 4G. 5GY 5VS 6J9 7-5 71M 8P~ 9JM 9JN AABVA AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALCJ AALRI AAOAW AAQFI AAQXK AATLK AAXUO AAYFN ABBOA ABBQC ABFNM ABFRF ABGRD ABJNI ABKYH ABLVK ABMAC ABMZM ABRWV ABXDB ABYKQ ACDAQ ACGFO ACGFS ACIUM ACIWK ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADQTV AEBSH AEFWE AEKER AENEX AEQOU AESVU AEXOQ AFKWA AFTJW AFXIZ AGHFR AGUBO AGYEJ AHHHB AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV AJRQY ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ANZVX AOUOD ASPBG AVWKF AXJTR AZFZN BKOJK BLXMC BNPGV CBWCG CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HLV HLZ HVGLF HZ~ IHE J1W KOM LCYCR LG9 LW9 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 QYZTP R2- RIG ROL RPZ SAB SBC SDF SDG SES SEW SNL SPC SPCBC SSA SSH SSV SSZ T5K UHS UNMZH WUQ Y6R ~G- ~KM AAHBH AATTM AAXKI AAYWO AAYXX ABWVN ACIEU ACMHX ACRPL ACVFH ADCNI ADNMO ADSLC AEIPS AEUPX AFJKZ AFPUW AGCQF AGQPQ AGRNS AGWPP AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION 7S9 L.6 |
ID | FETCH-LOGICAL-c339t-144af16c8eeabe41a4ebb4e2e087f2a31557cfa528db938a5f40f5304b096513 |
IEDL.DBID | .~1 |
ISSN | 0168-1699 |
IngestDate | Thu Jul 10 21:37:29 EDT 2025 Tue Jul 01 01:58:29 EDT 2025 Thu Apr 24 23:03:01 EDT 2025 Fri Feb 23 02:36:14 EST 2024 |
IsPeerReviewed | true |
IsScholarly | true |
Keywords | Agriculture 5.0 Food sustainability Light detection and ranging Point cloud processing Remote sensing |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c339t-144af16c8eeabe41a4ebb4e2e087f2a31557cfa528db938a5f40f5304b096513 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ORCID | 0000-0002-5208-6577 0000-0001-5514-5061 0000-0002-6772-5351 0000-0002-2365-4651 |
PQID | 2834204408 |
PQPubID | 24069 |
ParticipantIDs | proquest_miscellaneous_2834204408 crossref_primary_10_1016_j_compag_2023_107737 crossref_citationtrail_10_1016_j_compag_2023_107737 elsevier_sciencedirect_doi_10_1016_j_compag_2023_107737 |
ProviderPackageCode | CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | April 2023 2023-04-00 20230401 |
PublicationDateYYYYMMDD | 2023-04-01 |
PublicationDate_xml | – month: 04 year: 2023 text: April 2023 |
PublicationDecade | 2020 |
PublicationTitle | Computers and electronics in agriculture |
PublicationYear | 2023 |
Publisher | Elsevier B.V |
Publisher_xml | – name: Elsevier B.V |
References | Masjedi, Zhao, Thompson, Yang, Flatt, Crawford, Ebert, Tuinstra, Hammer, Chapman (b66) 2018 Stumvoll, Schmaltz, Glade (b90) 2021; 389 Underwood, Hung, Whelan, Sukkarieh (b101) 2016; 130 Huang, Zheng, Zhu (b39) 2022; 14 Gao, Yang, Wei, Liu (b25) 2022; 14 Gu, Zhao, Zou, Yang, Dou, Zhai (b31) 2022; 12 Shendryk, Sofonia, Garrard, Rist, Skocaj, Thorburn (b86) 2020; 92 Lau, Bentley, Martius, Shenkin, Bartholomeus, Raumonen, Malhi, Jackson, Herold (b52) 2018; 32 Malavazi, Guyonneau, Fasquel, Lagrange, Mercier (b62) 2018; 154 Chen, Yu, Tabish, Bansal, Liu, Abdelzaher, Sha (b14) 2021 Fernández-Álvarez, Armesto, Picos (b23) 2019; 10 Sun, Li, Paterson, Jiang, Xu, Robertson, Snider, Chee (b93) 2018; 9 Wang, Xing, He, Yu, Xu, Li (b108) 2022; 22 Velasquez, Higuti, Guerrero, Gasparino, Magalhães, Aroca, Becker (b102) 2020; 21 Zhou, Shi, Qiu, Yu, Zhang, Shen (b125) 2022; 400 Berk, Stajnko, Belsak, Hocevar (b6) 2020; 169 Dhami, Yu, Xu, Zhu, Dhakal, Friel, Li, Tokekar (b19) 2020 Tang, Jiang, Long, Fu, Sun (b94) 2022; 14 Jiang, Wang, Yi, Zhang, Lv (b46) 2022; 13 Tripathi, Mishra, Maurya, Singh, Wilson (b98) 2019 Zhang, Hassanzadeh, Kikkert, Pethybridge, van Aardt (b123) 2022; 15 Omasa, Hosoi, Konishi (b71) 2007; 58 Koenig, Höfle, Hämmerle, Jarmer, Siegmann, Lilienthal (b49) 2015; 104 Cruz Ulloa, Krus, Barrientos, Del Cerro, Valero (b17) 2021; 11 Hu, Lin, Peng, Wang, Zhai (b37) 2022; 12 Saha, Tsoulias, Weltzien, Zude-Sasse (b83) 2022; 8 Yuan, Li, Bhatta, Shi, Baenziger, Ge (b119) 2018; 18 Liu, Liu, He, Liu (b58) 2022; 12 Sofonia, Shendryk, Phinn, Roelfsema, Kendoul, Skocaj (b87) 2019; 82 Zhang, Hassanzadeh, Kikkert, Pethybridge, van Aardt (b122) 2020 Dilmurat, Sagan, Moose (b21) 2022; 3 Sun, Li (b92) 2017 Zhou, Zhang, Ge, Yu, Wang, Zhang (b126) 2021; 42 Lin, Liu (b56) 2021; 8 Trepekli, Friborg (b97) 2021; 13 Ghamkhar, Irie, Hagedorn (b30) 2019; 72 Itakura, Hosoi (b43) 2018; 74 Hadas, Jozkow, Walicka, Borkowski (b32) 2019; 82 Hu, Wang, Qian, Huang, Xia, Song (b38) 2018 Zhou, Gu, Cheng, Yang, Shu, Sun (b124) 2020; 10 Ziliani, Parkes, Hoteit, McCabe (b127) 2018; 10 Hu, Li, Zhang, He, Wimmer (b36) 2017; 67 Tiwari, Silver, Karnieli (b95) 2020; 198 Weiss, Biber, Laible, Bohlmann, Zell (b109) 2010 Gené-Mola, Gregorio, Guevara, Auat, Sanz-Cortiella, Escolà, Llorens, Morros, Ruiz-Hidalgo, Vilaplana, Rosell-Polo (b28) 2019; 187 Lin, Hu, Peng, Wang, Zhai (b55) 2022; 12 Wu, Zhu, Lawes, Dunkerley, Zhang (b116) 2019; 40 Wang, Peethambaran, Chen (b107) 2018; 11 Malambo, Popescu, Horne, Pugh, Rooney (b61) 2019; 149 Maimaitijiang, Sagan, Erkbol, Adrian, Newcomb, LeBauer, Pauli, Shakoor, Mockler (b60) 2020; 3 George, Barrett, Ghamkhar, Whyatt (b29) 2019; 81 Palacios-Rodríguez, Quinto, Lara-Gómez, Pérez-Romero, Recio, Álvarez-Romero, Cachinero-Vivar, Hernández-Navarro, Navarro-Cerrillo (b73) 2022; 13 Wu, Xue, Zhang, Qin, Chen, Sun (b115) 2018; 1 Rosell Polo, Sanz Cortiella, Llorens Calveras, Arnó Satorra, Ribes Dasi, Masip Vilalta, Camp, Gràcia, Solanelles Batlle, Pallejà Cabrè (b81) 2009; 102 Xian, Xu, Long, Song, Yang (b117) 2020; 59 Borowiec, Marmol (b11) 2022; 14 Rincón, García (b79) 2019; 34 Moreno, Valero, Bengochea-Guevara, Ribeiro, Garrido-Izard, Andujar (b67) 2020; 20 Wachendorf, Irie, Astor (b104) 2019; 42 Specht, Wiśniewska, Stateczny, Specht, Szostak, Lewicka, Stateczny, Widźgowski, Halicki (b88) 2022; 22 Reiser, Vázquez-Arellano, Paraforos, Garrido-Izard, Griepentrog (b78) 2018; 99 Rodriguez Padilla, Quintana, Prado, Aguilar, Shea, Oskin, Garcia (b80) 2021; 93 You, Grimm, Silwal, Davidson (b118) 2021 Srinivas, Drewitz, Magner (b89) 2020; 583 Westling, Underwood, Örn (b111) 2018; 153 Cassidy, Thomas, Higgins, Bailey, Jordan (b12) 2019; 687 Persiano, Carisi, Wang, Luzzi, Mazzoli, Bagli, Castellarin (b75) 2021 Walsh (b106) 2022; 12 Digumarti, Nieto, Cadena, Siegwart, Beardsley (b20) 2018; 3 Mao, Liu, Hao, Yang, Liu (b63) 2022; 14 Tsolakis, Bechtsis, Bochtis (b99) 2019; 9 Ivushkin, Bartholomeus, Bregt, Pulatov, Franceschini, Kramer, van Loo, Roman, Finkers (b44) 2019; 338 Bohn Reckziegel, Sheppard, Kahle, Larysch, Spiecker, Seifert, Morhart (b10) 2022; 96 Jin, Su, Wu, Pang, Gao, Hu, Liu, Guo (b47) 2018; 57 Murray, Fennell, Blackburn, Whyatt, Li (b68) 2020; 21 Zhang, Craine, McGee, Vandemark, Davis, Brown, Hulbert, Sankaran (b121) 2021; 113 Westling, Underwood, Bryson (b110) 2021; 187 Walklate, Richardson, Baker, Richards, Cross (b105) 1997; 3059 Kulkarni, Honda (b51) 2020; 5 Tsoulias, Paraforos, Xanthopoulos, Zude-Sasse (b100) 2020; 12 Krus, van Apeldoorn, Valero, Ramirez (b50) 2020; 10 Roten, Fourie, Owens, Trethewey, Ekanayake, Werner, Irie, Hagedorn, Cameron (b82) 2017; 135 Berrino, Vajda, Zahorec, Camacho, De Novellis, Carlino, Papco, Belluci Sessa, Czikhardt (b7) 2021; 51 Wu, Johansen, Phinn, Robson (b114) 2020; 12 Yun, Cao, An, Chen, Xue, Li, Pincebourde, Smith, Eichhorn (b120) 2019; 276 Estrada, Sánchez, Hernanz, Checa, Roman (b22) 2017; 6 Kamble, Kharche (b48) 2021; 7 Torres-Sánchez, López-Granados, Serrano, Arquero, Peña (b96) 2015; 10 Bhat, M., Han, S., Porikli, F., 2021. Fast polar attentive 3D object detection on LiDAR point clouds. In: Machine Learning for Autonomous Driving Workshop At the 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Masjedi, Crawford, Carpenter, Tuinstra (b65) 2020; 12 Ilesanmi, Rogers, Oboh-Ikuenobe (b41) 2021; 80 Nguyen, Badenhorst, Shi, Spangenberg, Smith, Daetwyler (b69) 2021; 13 Nowak, Pędziwiatr, Bogawski (b70) 2022; 142 Liu, Dong, Qiu (b57) 2020; 3 Wijesingha, Moeckel, Hensgen, Wachendorf (b112) 2019; 78 Akiyama, Yamamoto, Kitatsuji (b2) 2022 Gaudet, García-Medina, Krishnamurthy, Shaw, Sheridan, Yang, Newsom, Pekour (b26) 2022 Blatrix, Aramayo, Zangerlé, Roux, Jouanne, Anselme, de Boisvilliers, Krasnopolski, Assenbaum, McKey (b9) 2022; 42 Holmgren, Lindberg, Olofsson, Persson (b35) 2022; 43 Husin, Khairunniza-Bejo, Abdullah (b40) 2020; 10 Sandonís-Pozo, Llorens, Escolà, Arnó, Pascual, Martínez-Casasnovas (b84) 2022 Pagliai, Ammoniaci, Sarri, Lisci, Perria, Vieri, D’Arcangelo, Storchi, Kartsiotis (b72) 2022; 14 Aguiar, Dos Santos, Sobreira, Boaventura-Cunha, Sousa (b1) 2022; 9 Pfeiffer, Guevara, Cheein, Sanz (b76) 2018; 146 Martínez-Casasnovas, Rufat, Arnó, Arbonés, Sebé, Pascual, Gregorio, Rosell-Polo (b64) 2017; 18 Vidoni, Gallo, Ristorto, Carabin, Mazzetto, Scalera, Gasparetto (b103) 2017; 58370 Florent, Bernadett, Janos, Imri, Attila (b24) 2019; 9 Gené-Mola, Gregorio, Auat Cheein, Guevara, Llorens, Sanz-Cortiella, Escolà, Rosell-Polo (b27) 2020; 168 Maderal, Valcarcel, Delgado, Sevilla, Ojeda (b59) 2016; XLI-B8 LeVoir, Farley, Sun, Xu (b53) 2020; 1 Irish, Lillycrop (b42) 1999; 54 Arnó, Vallès, Llorens, Sanz, Masip, Palacín, Rosell-Polo (b4) 2013; 14 Janowski, Wroblewski, Rucinska, Kubowicz-Grajewska, Tysiac (b45) 2022; 301 Cecchi, Pantani (b13) 1994; 2 Barragán, Campos, Sanchez (b5) 2016; XLI-B7 Ao, Wu, Hu, Sun, Su, Guo, Xin (b3) 2022; 10 Pretto, Aravecchia, Burgard, Chebrolu, Dornhege, Falck, Fleckenstein, Fontenla, Imperoli, Khanna (b77) 2021; 28 Sultan Mahmud, He (b91) 2020 Pan, L., Liu, L., Condon, A.G., Estavillo, G.M., Coe, R.A., Bull, G., Stone, E.A., Petersson, L., Rolland, V., 2022. Biomass prediction with 3D point clouds from LiDAR. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 1330–1340. Christiansen, Laursen, Jørgensen, Skovsen, Gislum (b15) 2017; 17 Dellaert, Yen-Chen (b18) 2020 Holmén, Eichinger, Flocchini (b34) 1998; 32 Willers, Wu, O’Hara, Jenkins (b113) 2012; 82 Li, Shamshiri, Schirrmann, Weltzien, Shafian, Laursen (b54) 2022; 14 Colaço, Schaefer, Bramley (b16) 2021; 13 Hämmerle, Höfle (b33) 2014; 14 Shemanin, A., E. (b85) 2021; 867 Pagliai (10.1016/j.compag.2023.107737_b72) 2022; 14 Ilesanmi (10.1016/j.compag.2023.107737_b41) 2021; 80 Zhang (10.1016/j.compag.2023.107737_b123) 2022; 15 Borowiec (10.1016/j.compag.2023.107737_b11) 2022; 14 Torres-Sánchez (10.1016/j.compag.2023.107737_b96) 2015; 10 Hämmerle (10.1016/j.compag.2023.107737_b33) 2014; 14 Holmgren (10.1016/j.compag.2023.107737_b35) 2022; 43 Sofonia (10.1016/j.compag.2023.107737_b87) 2019; 82 Shemanin (10.1016/j.compag.2023.107737_b85) 2021; 867 Zhang (10.1016/j.compag.2023.107737_b122) 2020 Koenig (10.1016/j.compag.2023.107737_b49) 2015; 104 Liu (10.1016/j.compag.2023.107737_b58) 2022; 12 Gené-Mola (10.1016/j.compag.2023.107737_b28) 2019; 187 10.1016/j.compag.2023.107737_b74 Florent (10.1016/j.compag.2023.107737_b24) 2019; 9 Wang (10.1016/j.compag.2023.107737_b108) 2022; 22 Lin (10.1016/j.compag.2023.107737_b56) 2021; 8 Gao (10.1016/j.compag.2023.107737_b25) 2022; 14 Hu (10.1016/j.compag.2023.107737_b36) 2017; 67 Masjedi (10.1016/j.compag.2023.107737_b65) 2020; 12 Rincón (10.1016/j.compag.2023.107737_b79) 2019; 34 Cecchi (10.1016/j.compag.2023.107737_b13) 1994; 2 Tiwari (10.1016/j.compag.2023.107737_b95) 2020; 198 Maderal (10.1016/j.compag.2023.107737_b59) 2016; XLI-B8 Velasquez (10.1016/j.compag.2023.107737_b102) 2020; 21 Estrada (10.1016/j.compag.2023.107737_b22) 2017; 6 Ivushkin (10.1016/j.compag.2023.107737_b44) 2019; 338 Wu (10.1016/j.compag.2023.107737_b115) 2018; 1 Weiss (10.1016/j.compag.2023.107737_b109) 2010 Zhang (10.1016/j.compag.2023.107737_b121) 2021; 113 Ao (10.1016/j.compag.2023.107737_b3) 2022; 10 Berrino (10.1016/j.compag.2023.107737_b7) 2021; 51 Rodriguez Padilla (10.1016/j.compag.2023.107737_b80) 2021; 93 Pfeiffer (10.1016/j.compag.2023.107737_b76) 2018; 146 Nowak (10.1016/j.compag.2023.107737_b70) 2022; 142 Sun (10.1016/j.compag.2023.107737_b93) 2018; 9 Shendryk (10.1016/j.compag.2023.107737_b86) 2020; 92 Reiser (10.1016/j.compag.2023.107737_b78) 2018; 99 Tripathi (10.1016/j.compag.2023.107737_b98) 2019 Trepekli (10.1016/j.compag.2023.107737_b97) 2021; 13 Persiano (10.1016/j.compag.2023.107737_b75) 2021 Wu (10.1016/j.compag.2023.107737_b116) 2019; 40 George (10.1016/j.compag.2023.107737_b29) 2019; 81 Aguiar (10.1016/j.compag.2023.107737_b1) 2022; 9 Westling (10.1016/j.compag.2023.107737_b110) 2021; 187 Xian (10.1016/j.compag.2023.107737_b117) 2020; 59 Sun (10.1016/j.compag.2023.107737_b92) 2017 Tang (10.1016/j.compag.2023.107737_b94) 2022; 14 Martínez-Casasnovas (10.1016/j.compag.2023.107737_b64) 2017; 18 Walklate (10.1016/j.compag.2023.107737_b105) 1997; 3059 Westling (10.1016/j.compag.2023.107737_b111) 2018; 153 Jin (10.1016/j.compag.2023.107737_b47) 2018; 57 Sandonís-Pozo (10.1016/j.compag.2023.107737_b84) 2022 Palacios-Rodríguez (10.1016/j.compag.2023.107737_b73) 2022; 13 Roten (10.1016/j.compag.2023.107737_b82) 2017; 135 Moreno (10.1016/j.compag.2023.107737_b67) 2020; 20 Zhou (10.1016/j.compag.2023.107737_b126) 2021; 42 Ziliani (10.1016/j.compag.2023.107737_b127) 2018; 10 Arnó (10.1016/j.compag.2023.107737_b4) 2013; 14 Chen (10.1016/j.compag.2023.107737_b14) 2021 Holmén (10.1016/j.compag.2023.107737_b34) 1998; 32 Dellaert (10.1016/j.compag.2023.107737_b18) 2020 Digumarti (10.1016/j.compag.2023.107737_b20) 2018; 3 Malavazi (10.1016/j.compag.2023.107737_b62) 2018; 154 Maimaitijiang (10.1016/j.compag.2023.107737_b60) 2020; 3 Nguyen (10.1016/j.compag.2023.107737_b69) 2021; 13 Underwood (10.1016/j.compag.2023.107737_b101) 2016; 130 Walsh (10.1016/j.compag.2023.107737_b106) 2022; 12 10.1016/j.compag.2023.107737_b8 Vidoni (10.1016/j.compag.2023.107737_b103) 2017; 58370 Gu (10.1016/j.compag.2023.107737_b31) 2022; 12 Saha (10.1016/j.compag.2023.107737_b83) 2022; 8 Hadas (10.1016/j.compag.2023.107737_b32) 2019; 82 Itakura (10.1016/j.compag.2023.107737_b43) 2018; 74 Jiang (10.1016/j.compag.2023.107737_b46) 2022; 13 Lin (10.1016/j.compag.2023.107737_b55) 2022; 12 Specht (10.1016/j.compag.2023.107737_b88) 2022; 22 Fernández-Álvarez (10.1016/j.compag.2023.107737_b23) 2019; 10 Li (10.1016/j.compag.2023.107737_b54) 2022; 14 Yun (10.1016/j.compag.2023.107737_b120) 2019; 276 Yuan (10.1016/j.compag.2023.107737_b119) 2018; 18 Stumvoll (10.1016/j.compag.2023.107737_b90) 2021; 389 Sultan Mahmud (10.1016/j.compag.2023.107737_b91) 2020 Tsoulias (10.1016/j.compag.2023.107737_b100) 2020; 12 Berk (10.1016/j.compag.2023.107737_b6) 2020; 169 Hu (10.1016/j.compag.2023.107737_b38) 2018 Janowski (10.1016/j.compag.2023.107737_b45) 2022; 301 Blatrix (10.1016/j.compag.2023.107737_b9) 2022; 42 Liu (10.1016/j.compag.2023.107737_b57) 2020; 3 LeVoir (10.1016/j.compag.2023.107737_b53) 2020; 1 Gené-Mola (10.1016/j.compag.2023.107737_b27) 2020; 168 Bohn Reckziegel (10.1016/j.compag.2023.107737_b10) 2022; 96 Ghamkhar (10.1016/j.compag.2023.107737_b30) 2019; 72 Willers (10.1016/j.compag.2023.107737_b113) 2012; 82 Zhou (10.1016/j.compag.2023.107737_b124) 2020; 10 Hu (10.1016/j.compag.2023.107737_b37) 2022; 12 Masjedi (10.1016/j.compag.2023.107737_b66) 2018 Cassidy (10.1016/j.compag.2023.107737_b12) 2019; 687 Mao (10.1016/j.compag.2023.107737_b63) 2022; 14 Wachendorf (10.1016/j.compag.2023.107737_b104) 2019; 42 Colaço (10.1016/j.compag.2023.107737_b16) 2021; 13 Irish (10.1016/j.compag.2023.107737_b42) 1999; 54 You (10.1016/j.compag.2023.107737_b118) 2021 Lau (10.1016/j.compag.2023.107737_b52) 2018; 32 Tsolakis (10.1016/j.compag.2023.107737_b99) 2019; 9 Huang (10.1016/j.compag.2023.107737_b39) 2022; 14 Zhou (10.1016/j.compag.2023.107737_b125) 2022; 400 Husin (10.1016/j.compag.2023.107737_b40) 2020; 10 Christiansen (10.1016/j.compag.2023.107737_b15) 2017; 17 Rosell Polo (10.1016/j.compag.2023.107737_b81) 2009; 102 Malambo (10.1016/j.compag.2023.107737_b61) 2019; 149 Kulkarni (10.1016/j.compag.2023.107737_b51) 2020; 5 Srinivas (10.1016/j.compag.2023.107737_b89) 2020; 583 Kamble (10.1016/j.compag.2023.107737_b48) 2021; 7 Krus (10.1016/j.compag.2023.107737_b50) 2020; 10 Dilmurat (10.1016/j.compag.2023.107737_b21) 2022; 3 Pretto (10.1016/j.compag.2023.107737_b77) 2021; 28 Gaudet (10.1016/j.compag.2023.107737_b26) 2022 Barragán (10.1016/j.compag.2023.107737_b5) 2016; XLI-B7 Omasa (10.1016/j.compag.2023.107737_b71) 2007; 58 Murray (10.1016/j.compag.2023.107737_b68) 2020; 21 Wijesingha (10.1016/j.compag.2023.107737_b112) 2019; 78 Akiyama (10.1016/j.compag.2023.107737_b2) 2022 Dhami (10.1016/j.compag.2023.107737_b19) 2020 Wang (10.1016/j.compag.2023.107737_b107) 2018; 11 Cruz Ulloa (10.1016/j.compag.2023.107737_b17) 2021; 11 Wu (10.1016/j.compag.2023.107737_b114) 2020; 12 |
References_xml | – volume: 6 start-page: 255 year: 2017 ident: b22 article-title: Enabling the use of Sentinel-2 and LiDAR data for common agriculture policy funds assignment publication-title: ISPRS Int. J. Geo-Inf. – start-page: 1 year: 2022 end-page: 6 ident: b2 article-title: Wide-area road surface condition observation system utilizing traveling sensing by LiDAR publication-title: 2022 IEEE International Conference on Consumer Electronics – volume: 9 start-page: 160 year: 2019 end-page: 173 ident: b24 article-title: Evaluation of soil water management properties based on LiDAR data and soil analyses, at farm level publication-title: Nat. Resour. Sustain. Dev. – volume: 104 start-page: 112 year: 2015 end-page: 125 ident: b49 article-title: Comparative classification analysis of post-harvest growth detection from terrestrial LiDAR point clouds in precision agriculture publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 82 year: 2019 ident: b87 article-title: Monitoring sugarcane growth response to varying nitrogen application rates: A comparison of UAV SLAM LiDAR and photogrammetry publication-title: Int. J. Appl. Earth Obs. Geoinf. – start-page: 2643 year: 2020 end-page: 2649 ident: b19 article-title: Crop height and plot estimation for phenotyping from unmanned aerial vehicles using 3D LiDAR publication-title: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems – volume: 149 start-page: 1 year: 2019 end-page: 13 ident: b61 article-title: Automated detection and measurement of individual sorghum panicles using density-based clustering of terrestrial LiDAR data publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 583 year: 2020 ident: b89 article-title: Evaluating watershed-based optimized decision support framework for conservation practice placement in Plum Creek Minnesota publication-title: J. Hydrol. – volume: 15 start-page: 4027 year: 2022 end-page: 4044 ident: b123 article-title: Evaluation of leaf area index (LAI) of broadacre crops using UAS-based LiDAR point clouds and multispectral imagery publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – volume: 12 year: 2022 ident: b106 article-title: In-field estimation of fruit quality and quantity publication-title: Agronomy – volume: 10 start-page: 1 year: 2015 end-page: 20 ident: b96 article-title: High-throughput 3D monitoring of agricultural-tree plantations with unmanned aerial vehicle (UAV) technology publication-title: PLOS ONE – volume: XLI-B7 start-page: 171 year: 2016 end-page: 176 ident: b5 article-title: Automatic generation of building mapping using digital, vertical and aerial high resolution photographs and LiDAR point clouds publication-title: Int. Arch. Photogramme. Remote Sens. Spatial Inf. Sci. – volume: 12 start-page: 1450 year: 2022 ident: b55 article-title: Segmentation and stratification methods of field maize terrestrial LiDAR point cloud publication-title: Agriculture – volume: 42 year: 2022 ident: b9 article-title: Interpreting landscapes of pre-columbian raised-field agriculture using high-resolution LiDAR topography publication-title: J. Archaeol. Sci.: Reports – volume: 14 start-page: 431 year: 2022 ident: b39 article-title: High-throughput legume seed phenotyping using a handheld 3D laser scanner publication-title: Remote Sens. – volume: 42 year: 2019 ident: b104 article-title: The benefit of spectral and point-cloud data for herbage yield and quality assessment of grasslands publication-title: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. – volume: 198 start-page: 91 year: 2020 end-page: 104 ident: b95 article-title: Developing object-based image procedures for classifying and characterising different protected agriculture structures using LiDAR and orthophoto publication-title: Biosyst. Eng. – volume: 10 year: 2019 ident: b23 article-title: LiDAR-based wildfire prevention in WUI: The automatic detection, measurement and evaluation of forest fuels publication-title: Forests – volume: 14 start-page: 290 year: 2013 end-page: 306 ident: b4 article-title: Leaf area index estimation in vineyards using a ground-based LiDAR scanner publication-title: Precis. Agric. – year: 2020 ident: b91 article-title: Measuring tree canopy density using a LiDAR-guided system for precision spraying publication-title: ASABE – volume: 9 year: 2022 ident: b1 article-title: Localization and mapping on agriculture based on point-feature extraction and semiplanes segmentation from 3D LiDAR data publication-title: Front. Robotics and AI – volume: 10 year: 2020 ident: b40 article-title: Application of ground-based LiDAR for analysing oil palm canopy properties on the occurrence of basal stem rot (BSR) disease publication-title: Sci. Rep. – start-page: EGU21 year: 2021 end-page: 2894 ident: b75 article-title: Assessing fluvial flooding hazard with a DEM-based hierarchical filling-&-spilling algorithm: A case study in northern Italy publication-title: EGU General Assembly Conference Abstracts – volume: 102 start-page: 128 year: 2009 end-page: 134 ident: b81 article-title: A tractor-mounted scanning LIDAR for the non-destructive measurement of vegetative volume and surface area of tree-row plantations: A comparison with conventional destructive measurements publication-title: Biosyst. Eng. – volume: 10 start-page: 1239 year: 2022 end-page: 1250 ident: b3 article-title: Automatic segmentation of stem and leaf components and individual maize plants in field terrestrial LiDAR data using convolutional neural networks publication-title: The Crop J. – volume: 135 start-page: 128 year: 2017 end-page: 133 ident: b82 article-title: Urine patch detection using LiDAR technology to improve nitrogen use efficiency in grazed pastures publication-title: Comput. Electron. Agric. – volume: 3 year: 2020 ident: b60 article-title: UAV-based sorghum growth monitoring: A comparative analysis of LiDAR and photogrammetry publication-title: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences – volume: 42 start-page: 738 year: 2021 end-page: 755 ident: b126 article-title: Research on volume prediction of single tree canopy based on three-dimensional (3D) LiDAR and clustering segmentation publication-title: Int. J. Remote Sens. – volume: 169 year: 2020 ident: b6 article-title: Digital evaluation of leaf area of an individual tree canopy in the apple orchard using the LIDAR measurement system publication-title: Comput. Electron. Agric. – volume: 9 year: 2019 ident: b99 article-title: AgROS: A robot operating system based emulation tool for agricultural robotics publication-title: Agronomy – volume: 168 year: 2020 ident: b27 article-title: Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow publication-title: Comput. Electron. Agric. – volume: 153 start-page: 177 year: 2018 end-page: 187 ident: b111 article-title: Light interception modelling using unstructured LiDAR data in avocado orchards publication-title: Comput. Electron. Agric. – volume: 43 start-page: 299 year: 2022 end-page: 329 ident: b35 article-title: Tree crown segmentation in three dimensions using density models derived from airborne laser scanning publication-title: Int. J. Remote Sens. – volume: 18 start-page: 111 year: 2017 end-page: 132 ident: b64 article-title: Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds publication-title: Precis. Agric. – volume: 21 start-page: 349 year: 2020 end-page: 368 ident: b102 article-title: Reactive navigation system based on H publication-title: Precis. Agric. – volume: 154 start-page: 71 year: 2018 end-page: 79 ident: b62 article-title: LiDAR-only based navigation algorithm for an autonomous agricultural robot publication-title: Comput. Electron. Agric. – volume: 11 year: 2021 ident: b17 article-title: Robotic fertilisation using localisation systems based on point clouds in strip-cropping fields publication-title: Agronomy – volume: 13 start-page: 285 year: 2022 ident: b73 article-title: Carbon sequestration in carob (Ceratonia siliqua L.) plantations under the EU afforestation program in Southern Spain using low-density aerial laser scanning (ALS) data publication-title: Forests – volume: 1 start-page: 74 year: 2020 end-page: 94 ident: b53 article-title: High-accuracy adaptive low-cost location sensing subsystems for autonomous rover in precision agriculture publication-title: IEEE Open J. Ind. Appl. – volume: 28 start-page: 29 year: 2021 end-page: 49 ident: b77 article-title: Building an aerial-ground robotics system for precision farming: An adaptable solution publication-title: IEEE Robot. Autom. Mag. – volume: 14 start-page: 24212 year: 2014 end-page: 24230 ident: b33 article-title: Effects of reduced terrestrial LiDAR point density on high-resolution grain crop surface models in precision agriculture publication-title: Sensors – volume: 8 year: 2021 ident: b56 article-title: Martian atmospheric CO publication-title: Earth and Space Sci. – volume: 13 year: 2021 ident: b16 article-title: Broadacre mapping of wheat biomass using ground-based LiDAR technology publication-title: Remote Sens. – volume: 32 start-page: 3068 year: 1998 end-page: 3076 ident: b34 article-title: Application of elastic LiDAR to PM10 emissions from agricultural nonpoint sources publication-title: Environ. Sci. Technol. – volume: 276 year: 2019 ident: b120 article-title: Simulation of multi-platform LiDAR for assessing total leaf area in tree crowns publication-title: Agricult. Forest Meteorol. – volume: 72 year: 2019 ident: b30 article-title: Real-time, non-destructive and in-field foliage yield and growth rate measurement in perennial ryegrass (Lolium perenne L.) publication-title: Plant Methods – volume: 142 year: 2022 ident: b70 article-title: Hidden gaps under the canopy: Lidar-based detection and quantification of porosity in tree belts publication-title: Ecol. Indic. – volume: 58 start-page: 881 year: 2007 end-page: 898 ident: b71 article-title: 3D LiDAR imaging for detecting and understanding plant responses and canopy structure publication-title: J. Exp. Bot. – volume: 40 start-page: 5973 year: 2019 end-page: 5991 ident: b116 article-title: Comparison of machine learning algorithms for classification of LiDAR points for characterization of canola canopy structure publication-title: Int. J. Remote Sens. – volume: 12 start-page: 1241 year: 2022 ident: b31 article-title: Innovative leaf area detection models for orchard tree thick canopy based on LiDAR point cloud data publication-title: Agriculture – year: 2021 ident: b14 article-title: LiDAR cluster first and camera inference later: A new perspective towards autonomous driving – volume: 12 year: 2020 ident: b100 article-title: Apple shape detection based on geometric and radiometric features using a LiDAR laser scanner publication-title: Remote Sens. – volume: 22 year: 2022 ident: b108 article-title: Evaluation of a new lightweight UAV-borne topo-bathymetric LiDAR for shallow water bathymetry and object detection publication-title: Sensors – volume: 10 start-page: 2007 year: 2018 ident: b127 article-title: Intra-season crop height variability at commercial farm scales using a fixed-wing UAV publication-title: Remote Sens. – volume: 3 start-page: 193 year: 2022 end-page: 199 ident: b21 article-title: AI-driven maize yield forecasting using unmanned aerial vehicle-based hyperspectral and LiDAR data fusion publication-title: ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. – volume: 96 start-page: 89 year: 2022 end-page: 104 ident: b10 article-title: Virtual pruning of 3D trees as a tool for managing shading effects in agroforestry systems publication-title: Agrofor. Syst. – volume: 54 start-page: 123 year: 1999 end-page: 129 ident: b42 article-title: Scanning laser mapping of the coastal zone: the SHOALS system publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 10 year: 2020 ident: b124 article-title: Analysis of plant height changes of lodged maize using UAV-LiDAR data publication-title: Agriculture – volume: 3059 start-page: 143 year: 1997 end-page: 151 ident: b105 article-title: Short-range lidar measurement of top fruit tree canopies for pesticide applications research in the United Kingdom publication-title: Advances in Laser Remote Sensing for Terrestrial and Oceanographic Applications – volume: 5 start-page: 1047 year: 2020 end-page: 1053 ident: b51 article-title: Estimating LAI of rice using NDVI derived from MODIS surface reflectance publication-title: Adv. Sci. Technol. Eng. Syst. J. – volume: 12 start-page: 2409 year: 2022 ident: b37 article-title: Rapeseed leaf estimation methods at field scale by using terrestrial LiDAR point cloud publication-title: Agronomy – volume: 20 year: 2020 ident: b67 article-title: On-ground vineyard reconstruction using a LiDAR-based automated system publication-title: Sensors – volume: 8 start-page: 90 year: 2022 ident: b83 article-title: Estimation of vegetative growth in strawberry plants using mobile LiDAR laser scanner publication-title: Horticulturae – volume: 14 start-page: 1145 year: 2022 ident: b72 article-title: Comparison of aerial and ground 3D point clouds for canopy size assessment in precision viticulture publication-title: Remote Sens. – volume: 34 start-page: 164 year: 2019 end-page: 178 ident: b79 article-title: Synergetic efficiency of LiDAR and WorldView-2 for 3D urban cartography in Northeast Mexico publication-title: Geocarto Int. – volume: 1 year: 2018 ident: b115 article-title: Plant 3D reconstruction based on LiDAR and multi-view sequence images publication-title: Int. J. Precis. Agric. Aviat. – volume: 187 year: 2021 ident: b110 article-title: A procedure for automated tree pruning suggestion using LiDAR scans of fruit trees publication-title: Comput. Electron. Agric. – start-page: 465 year: 2020 end-page: 468 ident: b122 article-title: Toward a structural description of row crops using UAS-based LiDAR point clouds publication-title: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium – volume: 2 start-page: 979 year: 1994 end-page: 981 ident: b13 article-title: Fluorescence LiDAR remote sensing of vegetation: Research advances in Europe publication-title: Proceedings of IGARSS’94-1994 IEEE International Geoscience and Remote Sensing Symposium – volume: 146 start-page: 104 year: 2018 end-page: 113 ident: b76 article-title: Mechatronic terrestrial LiDAR for canopy porosity and crown surface estimation publication-title: Comput. Electron. Agric. – volume: 80 year: 2021 ident: b41 article-title: Evaluation of geohazards in the Cape Girardeau area using LiDAR and GIS, Southeast Missouri, USA publication-title: Environ. Earth Sci. – volume: 74 start-page: 109 year: 2018 end-page: 113 ident: b43 article-title: Automatic individual tree detection and canopy segmentation from three-dimensional point cloud images obtained from ground-based LiDAR publication-title: J. Agric. Meteorol. – start-page: 7719 year: 2018 end-page: 7722 ident: b66 article-title: Sorghum biomass prediction using UAV-based remote sensing data and crop model simulation publication-title: IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium – volume: 14 start-page: 642 year: 2022 ident: b94 article-title: Identification of the yield of camellia oleifera based on color space by the optimized mean shift clustering algorithm using terrestrial laser scanning publication-title: Remote Sens. – volume: 14 start-page: 585 year: 2022 ident: b54 article-title: UAV oblique imagery with an adaptive micro-terrain model for estimation of leaf area index and height of maize canopy from 3D point clouds publication-title: Remote Sens. – volume: 78 start-page: 352 year: 2019 end-page: 359 ident: b112 article-title: Evaluation of 3D point cloud-based models for the prediction of grassland biomass publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 58370 year: 2017 ident: b103 article-title: ByeLab: An agricultural mobile robot prototype for proximal sensing and precision farming publication-title: ASME International Mechanical Engineering Congress and Exposition – year: 2022 ident: b26 article-title: Evaluation of coupled wind/wave model simulations of offshore winds in the Mid-Atlantic bight using LiDAR-equipped buoys publication-title: Mon. Weather Rev. – volume: 301 year: 2022 ident: b45 article-title: Automatic classification and mapping of the seabed using airborne LiDAR bathymetry publication-title: Eng. Geol. – volume: 14 start-page: 675 year: 2022 ident: b63 article-title: Development of a combined orchard harvesting robot navigation system publication-title: Remote Sens. – year: 2021 ident: b118 article-title: Semantics-guided skeletonization of sweet cherry trees for robotic pruning – reference: Bhat, M., Han, S., Porikli, F., 2021. Fast polar attentive 3D object detection on LiDAR point clouds. In: Machine Learning for Autonomous Driving Workshop At the 35th Conference on Neural Information Processing Systems (NeurIPS 2021). – volume: 22 year: 2022 ident: b88 article-title: Analysis of methods for determining shallow waterbody depths based on images taken by unmanned aerial vehicles publication-title: Sensors – volume: 113 start-page: 3269 year: 2021 end-page: 3280 ident: b121 article-title: High-throughput phenotyping of canopy height in cool-season crops using sensing techniques publication-title: Agron. J. – volume: 81 start-page: 55 year: 2019 end-page: 60 ident: b29 article-title: Evaluation of LiDAR scanning for measurement of yield in perennial ryegrass publication-title: J. New Zealand Grasslands – volume: 51 start-page: 345 year: 2021 end-page: 371 ident: b7 article-title: Interpretation of spatiotemporal gravity changes accompanying the earthquake of 21 August 2017 on Ischia (Italy) publication-title: Contrib. Geophys. Geodesy – volume: 11 start-page: 606 year: 2018 end-page: 627 ident: b107 article-title: LiDAR point clouds to 3-D urban models publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – volume: 82 year: 2019 ident: b32 article-title: Apple orchard inventory with a LiDAR equipped unmanned aerial system publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 7 start-page: 2537 year: 2021 end-page: 2542 ident: b48 article-title: Obstacle detection using LiDAR publication-title: Int. J. Sci. Res. Eng. Trends – volume: 867 year: 2021 ident: b85 article-title: LiDAR measurements of hydrogen fluoride concentration in atmospheric boundary layer publication-title: IOP Conference Series: Earth and Environmental Science – volume: 57 start-page: 1336 year: 2018 end-page: 1346 ident: b47 article-title: Stem–leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 32 start-page: 1219 year: 2018 end-page: 1231 ident: b52 article-title: Quantifying branch architecture of tropical trees using terrestrial LiDAR and 3D modelling publication-title: Trees – volume: 13 year: 2021 ident: b69 article-title: Design of an unmanned ground vehicle and LiDAR pipeline for the high-throughput phenotyping of biomass in perennial ryegrass publication-title: Remote Sens. – start-page: 1 year: 2022 end-page: 23 ident: b84 article-title: Satellite multispectral indices to estimate canopy parameters and within-field management zones in super-intensive almond orchards publication-title: Precis. Agric. – volume: 21 start-page: 473 year: 2020 end-page: 483 ident: b68 article-title: The novel use of proximal photogrammetry and terrestrial LiDAR to quantify the structural complexity of orchard trees publication-title: Precis. Agric. – volume: 14 start-page: 2292 year: 2022 ident: b25 article-title: Individual maize location and height estimation in field from UAV-Borne LiDAR and RGB images publication-title: Remote Sens. – volume: 13 start-page: 815218 year: 2022 ident: b46 article-title: Autonomous navigation system of greenhouse mobile robot based on 3D Lidar and 2D LiDAR SLAM publication-title: Front. Plant Sci. – volume: 92 year: 2020 ident: b86 article-title: Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging publication-title: Int. J. Appl. Earth Obs. Geoinf. – year: 2020 ident: b18 article-title: Neural volume rendering: NeRF and beyond – volume: 99 start-page: 42 year: 2018 end-page: 52 ident: b78 article-title: Iterative individual plant clustering in maize with assembled 2D LiDAR data publication-title: Comput. Ind. – volume: 10 year: 2020 ident: b50 article-title: Acquiring plant features with optical sensing devices in an organic strip-cropping system publication-title: Agronomy – volume: 400 year: 2022 ident: b125 article-title: Experimental study on morphological characteristics of landslide dams in different shaped valleys publication-title: Geomorphology – volume: 338 start-page: 502 year: 2019 end-page: 512 ident: b44 article-title: UAV based soil salinity assessment of cropland publication-title: Geoderma – volume: 187 start-page: 171 year: 2019 end-page: 184 ident: b28 article-title: Fruit detection in an apple orchard using a mobile terrestrial laser scanner publication-title: Biosyst. Eng. – volume: 13 year: 2021 ident: b97 article-title: Deriving aerodynamic roughness length at ultra-high resolution in agricultural areas using UAV-borne LiDAR publication-title: Remote Sens. – volume: 9 start-page: 16 year: 2018 ident: b93 article-title: In-field high throughput phenotyping and phenotype data analysis for cotton plant growth using LiDAR publication-title: Front. Plant Sci. – volume: 12 start-page: 2509 year: 2022 ident: b58 article-title: Precision variable-rate spraying robot by using single 3D LIDAR in orchards publication-title: Agronomy – start-page: 1 year: 2018 end-page: 6 ident: b38 article-title: LiDAR-based SLAM and autonomous navigation for forestry quadrotors publication-title: 2018 IEEE CSAA Guidance, Navigation and Control Conference – volume: 389 year: 2021 ident: b90 article-title: Dynamic characterization of a slow-moving landslide system: Assessing the challenges of small process scales utilizing multi-temporal TLS data publication-title: Geomorphology – volume: 12 year: 2020 ident: b114 article-title: Suitability of airborne and terrestrial laser scanning for mapping tree crop structural metrics for improved orchard management publication-title: Remote Sens. – volume: 59 start-page: 8638 year: 2020 end-page: 8644 ident: b117 article-title: Early forest-fire detection using scanning polarization LiDAR publication-title: Appl. Opt. – volume: 3 start-page: 3043 year: 2018 end-page: 3050 ident: b20 article-title: Automatic segmentation of tree structure from point cloud data publication-title: IEEE Robot. Autom. Lett. – volume: 3 start-page: 72 year: 2020 end-page: 76 ident: b57 article-title: Analysis of cotton height spatial variability based on UAV-LIDAR publication-title: Precis. Agric. Aviat. – volume: 93 start-page: 494 year: 2021 end-page: 499 ident: b80 article-title: Near-field high-resolution maps of the ridgecrest earthquakes from aerial imagery publication-title: Seismol. Res. Lett. – start-page: 339 year: 2010 end-page: 345 ident: b109 article-title: Plant species classification using a 3D LiDAR sensor and machine learning publication-title: 2010 Ninth International Conference on Machine Learning and Applications – volume: 82 start-page: 15 year: 2012 end-page: 22 ident: b113 article-title: A categorical, improper probability method for combining NDVI and LiDAR elevation information for potential cotton precision agricultural applications publication-title: Comput. Electron. Agric. – volume: 687 start-page: 277 year: 2019 end-page: 286 ident: b12 article-title: A carrying capacity framework for soil phosphorus and hydrological sensitivity from farm to catchment scales publication-title: Sci. Total Environ. – volume: 67 start-page: 1 year: 2017 end-page: 13 ident: b36 article-title: Efficient tree modeling from airborne LiDAR point clouds publication-title: Comput. Graph. – volume: 18 start-page: 3731 year: 2018 ident: b119 article-title: Wheat height estimation using LiDAR in comparison to ultrasonic sensor and UAS publication-title: Sensors – volume: 17 start-page: 2703 year: 2017 ident: b15 article-title: Designing and testing a UAV mapping system for agricultural field surveying publication-title: Sensors – start-page: 3 year: 2019 end-page: 24 ident: b98 article-title: Estimates for world population and global food availability for global health publication-title: The Role of Functional Food Security in Global Health – volume: 14 start-page: 1048 year: 2022 ident: b11 article-title: Using LiDAR system as a data source for agricultural land boundaries publication-title: Remote Sens. – start-page: 1 year: 2017 ident: b92 article-title: In-field high throughput phenotyping and phenotype data analysis for cotton plant growth using LiDAR publication-title: 2017 ASABE Annual International Meeting – volume: 130 start-page: 83 year: 2016 end-page: 96 ident: b101 article-title: Mapping almond orchard canopy volume, flowers, fruit and yield using lidar and vision sensors publication-title: Comput. Electron. Agric. – volume: 12 year: 2020 ident: b65 article-title: Multi-temporal predictive modelling of sorghum biomass using UAV-based hyperspectral and LiDAR data publication-title: Remote Sens. – reference: Pan, L., Liu, L., Condon, A.G., Estavillo, G.M., Coe, R.A., Bull, G., Stone, E.A., Petersson, L., Rolland, V., 2022. Biomass prediction with 3D point clouds from LiDAR. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 1330–1340. – volume: XLI-B8 start-page: 365 year: 2016 end-page: 372 ident: b59 article-title: Automatic river network extraction from LiDAR data publication-title: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. – volume: 81 start-page: 55 year: 2019 ident: 10.1016/j.compag.2023.107737_b29 article-title: Evaluation of LiDAR scanning for measurement of yield in perennial ryegrass publication-title: J. New Zealand Grasslands doi: 10.33584/jnzg.2019.81.414 – volume: 338 start-page: 502 year: 2019 ident: 10.1016/j.compag.2023.107737_b44 article-title: UAV based soil salinity assessment of cropland publication-title: Geoderma doi: 10.1016/j.geoderma.2018.09.046 – start-page: 1 year: 2022 ident: 10.1016/j.compag.2023.107737_b84 article-title: Satellite multispectral indices to estimate canopy parameters and within-field management zones in super-intensive almond orchards publication-title: Precis. Agric. – volume: 130 start-page: 83 year: 2016 ident: 10.1016/j.compag.2023.107737_b101 article-title: Mapping almond orchard canopy volume, flowers, fruit and yield using lidar and vision sensors publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2016.09.014 – start-page: 3 year: 2019 ident: 10.1016/j.compag.2023.107737_b98 article-title: Estimates for world population and global food availability for global health – volume: 301 year: 2022 ident: 10.1016/j.compag.2023.107737_b45 article-title: Automatic classification and mapping of the seabed using airborne LiDAR bathymetry publication-title: Eng. Geol. doi: 10.1016/j.enggeo.2022.106615 – volume: 21 start-page: 349 issue: 2 year: 2020 ident: 10.1016/j.compag.2023.107737_b102 article-title: Reactive navigation system based on H∞ control system and LiDAR readings on corn crops publication-title: Precis. Agric. doi: 10.1007/s11119-019-09672-8 – volume: 82 start-page: 15 year: 2012 ident: 10.1016/j.compag.2023.107737_b113 article-title: A categorical, improper probability method for combining NDVI and LiDAR elevation information for potential cotton precision agricultural applications publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2011.11.010 – volume: 67 start-page: 1 year: 2017 ident: 10.1016/j.compag.2023.107737_b36 article-title: Efficient tree modeling from airborne LiDAR point clouds publication-title: Comput. Graph. doi: 10.1016/j.cag.2017.04.004 – volume: XLI-B7 start-page: 171 year: 2016 ident: 10.1016/j.compag.2023.107737_b5 article-title: Automatic generation of building mapping using digital, vertical and aerial high resolution photographs and LiDAR point clouds publication-title: Int. Arch. Photogramme. Remote Sens. Spatial Inf. Sci. doi: 10.5194/isprs-archives-XLI-B7-171-2016 – volume: 9 start-page: 16 year: 2018 ident: 10.1016/j.compag.2023.107737_b93 article-title: In-field high throughput phenotyping and phenotype data analysis for cotton plant growth using LiDAR publication-title: Front. Plant Sci. doi: 10.3389/fpls.2018.00016 – volume: 135 start-page: 128 year: 2017 ident: 10.1016/j.compag.2023.107737_b82 article-title: Urine patch detection using LiDAR technology to improve nitrogen use efficiency in grazed pastures publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2017.02.006 – volume: 20 issue: 4 year: 2020 ident: 10.1016/j.compag.2023.107737_b67 article-title: On-ground vineyard reconstruction using a LiDAR-based automated system publication-title: Sensors doi: 10.3390/s20041102 – volume: 10 start-page: 2007 issue: 12 year: 2018 ident: 10.1016/j.compag.2023.107737_b127 article-title: Intra-season crop height variability at commercial farm scales using a fixed-wing UAV publication-title: Remote Sens. doi: 10.3390/rs10122007 – volume: 687 start-page: 277 year: 2019 ident: 10.1016/j.compag.2023.107737_b12 article-title: A carrying capacity framework for soil phosphorus and hydrological sensitivity from farm to catchment scales publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2019.05.453 – volume: 14 start-page: 431 issue: 2 year: 2022 ident: 10.1016/j.compag.2023.107737_b39 article-title: High-throughput legume seed phenotyping using a handheld 3D laser scanner publication-title: Remote Sens. doi: 10.3390/rs14020431 – volume: 34 start-page: 164 issue: 2 year: 2019 ident: 10.1016/j.compag.2023.107737_b79 article-title: Synergetic efficiency of LiDAR and WorldView-2 for 3D urban cartography in Northeast Mexico publication-title: Geocarto Int. doi: 10.1080/10106049.2017.1377774 – volume: 32 start-page: 3068 issue: 20 year: 1998 ident: 10.1016/j.compag.2023.107737_b34 article-title: Application of elastic LiDAR to PM10 emissions from agricultural nonpoint sources publication-title: Environ. Sci. Technol. doi: 10.1021/es980176p – start-page: 1 year: 2022 ident: 10.1016/j.compag.2023.107737_b2 article-title: Wide-area road surface condition observation system utilizing traveling sensing by LiDAR – year: 2020 ident: 10.1016/j.compag.2023.107737_b18 – volume: 14 start-page: 675 issue: 3 year: 2022 ident: 10.1016/j.compag.2023.107737_b63 article-title: Development of a combined orchard harvesting robot navigation system publication-title: Remote Sens. doi: 10.3390/rs14030675 – volume: 82 year: 2019 ident: 10.1016/j.compag.2023.107737_b87 article-title: Monitoring sugarcane growth response to varying nitrogen application rates: A comparison of UAV SLAM LiDAR and photogrammetry publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 153 start-page: 177 year: 2018 ident: 10.1016/j.compag.2023.107737_b111 article-title: Light interception modelling using unstructured LiDAR data in avocado orchards publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2018.08.020 – volume: 13 start-page: 815218 year: 2022 ident: 10.1016/j.compag.2023.107737_b46 article-title: Autonomous navigation system of greenhouse mobile robot based on 3D Lidar and 2D LiDAR SLAM publication-title: Front. Plant Sci. doi: 10.3389/fpls.2022.815218 – volume: 92 year: 2020 ident: 10.1016/j.compag.2023.107737_b86 article-title: Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 1 start-page: 74 year: 2020 ident: 10.1016/j.compag.2023.107737_b53 article-title: High-accuracy adaptive low-cost location sensing subsystems for autonomous rover in precision agriculture publication-title: IEEE Open J. Ind. Appl. doi: 10.1109/OJIA.2020.3015253 – volume: 149 start-page: 1 year: 2019 ident: 10.1016/j.compag.2023.107737_b61 article-title: Automated detection and measurement of individual sorghum panicles using density-based clustering of terrestrial LiDAR data publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2018.12.015 – volume: 400 year: 2022 ident: 10.1016/j.compag.2023.107737_b125 article-title: Experimental study on morphological characteristics of landslide dams in different shaped valleys publication-title: Geomorphology doi: 10.1016/j.geomorph.2021.108081 – volume: 389 year: 2021 ident: 10.1016/j.compag.2023.107737_b90 article-title: Dynamic characterization of a slow-moving landslide system: Assessing the challenges of small process scales utilizing multi-temporal TLS data publication-title: Geomorphology doi: 10.1016/j.geomorph.2021.107803 – volume: 12 issue: 15 year: 2020 ident: 10.1016/j.compag.2023.107737_b100 article-title: Apple shape detection based on geometric and radiometric features using a LiDAR laser scanner publication-title: Remote Sens. doi: 10.3390/rs12152481 – volume: 18 start-page: 111 issue: 1 year: 2017 ident: 10.1016/j.compag.2023.107737_b64 article-title: Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds publication-title: Precis. Agric. doi: 10.1007/s11119-016-9474-5 – volume: 13 start-page: 285 issue: 2 year: 2022 ident: 10.1016/j.compag.2023.107737_b73 article-title: Carbon sequestration in carob (Ceratonia siliqua L.) plantations under the EU afforestation program in Southern Spain using low-density aerial laser scanning (ALS) data publication-title: Forests doi: 10.3390/f13020285 – volume: 10 start-page: 1239 issue: 5 year: 2022 ident: 10.1016/j.compag.2023.107737_b3 article-title: Automatic segmentation of stem and leaf components and individual maize plants in field terrestrial LiDAR data using convolutional neural networks publication-title: The Crop J. doi: 10.1016/j.cj.2021.10.010 – volume: 17 start-page: 2703 issue: 12 year: 2017 ident: 10.1016/j.compag.2023.107737_b15 article-title: Designing and testing a UAV mapping system for agricultural field surveying publication-title: Sensors doi: 10.3390/s17122703 – volume: 40 start-page: 5973 issue: 15 year: 2019 ident: 10.1016/j.compag.2023.107737_b116 article-title: Comparison of machine learning algorithms for classification of LiDAR points for characterization of canola canopy structure publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2019.1584929 – year: 2020 ident: 10.1016/j.compag.2023.107737_b91 article-title: Measuring tree canopy density using a LiDAR-guided system for precision spraying publication-title: ASABE – volume: 12 start-page: 1450 issue: 9 year: 2022 ident: 10.1016/j.compag.2023.107737_b55 article-title: Segmentation and stratification methods of field maize terrestrial LiDAR point cloud publication-title: Agriculture doi: 10.3390/agriculture12091450 – start-page: 2643 year: 2020 ident: 10.1016/j.compag.2023.107737_b19 article-title: Crop height and plot estimation for phenotyping from unmanned aerial vehicles using 3D LiDAR – start-page: EGU21 year: 2021 ident: 10.1016/j.compag.2023.107737_b75 article-title: Assessing fluvial flooding hazard with a DEM-based hierarchical filling-&-spilling algorithm: A case study in northern Italy – volume: 276 year: 2019 ident: 10.1016/j.compag.2023.107737_b120 article-title: Simulation of multi-platform LiDAR for assessing total leaf area in tree crowns publication-title: Agricult. Forest Meteorol. – volume: 168 year: 2020 ident: 10.1016/j.compag.2023.107737_b27 article-title: Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2019.105121 – volume: 14 start-page: 290 issue: 3 year: 2013 ident: 10.1016/j.compag.2023.107737_b4 article-title: Leaf area index estimation in vineyards using a ground-based LiDAR scanner publication-title: Precis. Agric. doi: 10.1007/s11119-012-9295-0 – volume: 6 start-page: 255 issue: 8 year: 2017 ident: 10.1016/j.compag.2023.107737_b22 article-title: Enabling the use of Sentinel-2 and LiDAR data for common agriculture policy funds assignment publication-title: ISPRS Int. J. Geo-Inf. doi: 10.3390/ijgi6080255 – volume: 10 year: 2020 ident: 10.1016/j.compag.2023.107737_b40 article-title: Application of ground-based LiDAR for analysing oil palm canopy properties on the occurrence of basal stem rot (BSR) disease publication-title: Sci. Rep. doi: 10.1038/s41598-020-62275-6 – volume: 154 start-page: 71 year: 2018 ident: 10.1016/j.compag.2023.107737_b62 article-title: LiDAR-only based navigation algorithm for an autonomous agricultural robot publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2018.08.034 – volume: 13 issue: 17 year: 2021 ident: 10.1016/j.compag.2023.107737_b97 article-title: Deriving aerodynamic roughness length at ultra-high resolution in agricultural areas using UAV-borne LiDAR publication-title: Remote Sens. doi: 10.3390/rs13173538 – volume: 13 issue: 1 year: 2021 ident: 10.1016/j.compag.2023.107737_b69 article-title: Design of an unmanned ground vehicle and LiDAR pipeline for the high-throughput phenotyping of biomass in perennial ryegrass publication-title: Remote Sens. doi: 10.3390/rs13010020 – volume: 28 start-page: 29 issue: 3 year: 2021 ident: 10.1016/j.compag.2023.107737_b77 article-title: Building an aerial-ground robotics system for precision farming: An adaptable solution publication-title: IEEE Robot. Autom. Mag. doi: 10.1109/MRA.2020.3012492 – start-page: 7719 year: 2018 ident: 10.1016/j.compag.2023.107737_b66 article-title: Sorghum biomass prediction using UAV-based remote sensing data and crop model simulation – volume: 12 issue: 21 year: 2020 ident: 10.1016/j.compag.2023.107737_b65 article-title: Multi-temporal predictive modelling of sorghum biomass using UAV-based hyperspectral and LiDAR data publication-title: Remote Sens. doi: 10.3390/rs12213587 – volume: 187 start-page: 171 year: 2019 ident: 10.1016/j.compag.2023.107737_b28 article-title: Fruit detection in an apple orchard using a mobile terrestrial laser scanner publication-title: Biosyst. Eng. doi: 10.1016/j.biosystemseng.2019.08.017 – year: 2021 ident: 10.1016/j.compag.2023.107737_b118 – volume: 14 start-page: 1145 issue: 5 year: 2022 ident: 10.1016/j.compag.2023.107737_b72 article-title: Comparison of aerial and ground 3D point clouds for canopy size assessment in precision viticulture publication-title: Remote Sens. doi: 10.3390/rs14051145 – volume: 12 start-page: 2409 issue: 10 year: 2022 ident: 10.1016/j.compag.2023.107737_b37 article-title: Rapeseed leaf estimation methods at field scale by using terrestrial LiDAR point cloud publication-title: Agronomy doi: 10.3390/agronomy12102409 – volume: 867 year: 2021 ident: 10.1016/j.compag.2023.107737_b85 article-title: LiDAR measurements of hydrogen fluoride concentration in atmospheric boundary layer – volume: 13 issue: 16 year: 2021 ident: 10.1016/j.compag.2023.107737_b16 article-title: Broadacre mapping of wheat biomass using ground-based LiDAR technology publication-title: Remote Sens. doi: 10.3390/rs13163218 – volume: 146 start-page: 104 year: 2018 ident: 10.1016/j.compag.2023.107737_b76 article-title: Mechatronic terrestrial LiDAR for canopy porosity and crown surface estimation publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2018.01.022 – volume: 96 start-page: 89 issue: 1 year: 2022 ident: 10.1016/j.compag.2023.107737_b10 article-title: Virtual pruning of 3D trees as a tool for managing shading effects in agroforestry systems publication-title: Agrofor. Syst. doi: 10.1007/s10457-021-00697-5 – volume: 32 start-page: 1219 issue: 5 year: 2018 ident: 10.1016/j.compag.2023.107737_b52 article-title: Quantifying branch architecture of tropical trees using terrestrial LiDAR and 3D modelling publication-title: Trees doi: 10.1007/s00468-018-1704-1 – volume: 5 start-page: 1047 year: 2020 ident: 10.1016/j.compag.2023.107737_b51 article-title: Estimating LAI of rice using NDVI derived from MODIS surface reflectance publication-title: Adv. Sci. Technol. Eng. Syst. J. doi: 10.25046/aj0506127 – volume: 11 start-page: 606 issue: 2 year: 2018 ident: 10.1016/j.compag.2023.107737_b107 article-title: LiDAR point clouds to 3-D urban models: A review publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2017.2781132 – volume: 3 start-page: 72 issue: 3 year: 2020 ident: 10.1016/j.compag.2023.107737_b57 article-title: Analysis of cotton height spatial variability based on UAV-LIDAR publication-title: Precis. Agric. Aviat. – volume: 142 year: 2022 ident: 10.1016/j.compag.2023.107737_b70 article-title: Hidden gaps under the canopy: Lidar-based detection and quantification of porosity in tree belts publication-title: Ecol. Indic. doi: 10.1016/j.ecolind.2022.109243 – volume: 8 issue: 11 year: 2021 ident: 10.1016/j.compag.2023.107737_b56 article-title: Martian atmospheric CO2 and pressure profiling with differential absorption LiDAR: System consideration and simulation results publication-title: Earth and Space Sci. doi: 10.1029/2020EA001600 – volume: 10 issue: 2 year: 2019 ident: 10.1016/j.compag.2023.107737_b23 article-title: LiDAR-based wildfire prevention in WUI: The automatic detection, measurement and evaluation of forest fuels publication-title: Forests doi: 10.3390/f10020148 – volume: 8 start-page: 90 issue: 2 year: 2022 ident: 10.1016/j.compag.2023.107737_b83 article-title: Estimation of vegetative growth in strawberry plants using mobile LiDAR laser scanner publication-title: Horticulturae doi: 10.3390/horticulturae8020090 – volume: 12 issue: 5 year: 2022 ident: 10.1016/j.compag.2023.107737_b106 article-title: In-field estimation of fruit quality and quantity publication-title: Agronomy doi: 10.3390/agronomy12051074 – start-page: 1 year: 2017 ident: 10.1016/j.compag.2023.107737_b92 article-title: In-field high throughput phenotyping and phenotype data analysis for cotton plant growth using LiDAR – volume: 2 start-page: 979 year: 1994 ident: 10.1016/j.compag.2023.107737_b13 article-title: Fluorescence LiDAR remote sensing of vegetation: Research advances in Europe – volume: 54 start-page: 123 issue: 2 year: 1999 ident: 10.1016/j.compag.2023.107737_b42 article-title: Scanning laser mapping of the coastal zone: the SHOALS system publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/S0924-2716(99)00003-9 – volume: 113 start-page: 3269 issue: 4 year: 2021 ident: 10.1016/j.compag.2023.107737_b121 article-title: High-throughput phenotyping of canopy height in cool-season crops using sensing techniques publication-title: Agron. J. doi: 10.1002/agj2.20632 – volume: 7 start-page: 2537 issue: 4 year: 2021 ident: 10.1016/j.compag.2023.107737_b48 article-title: Obstacle detection using LiDAR publication-title: Int. J. Sci. Res. Eng. Trends – ident: 10.1016/j.compag.2023.107737_b74 doi: 10.1109/WACV51458.2022.00178 – volume: 169 year: 2020 ident: 10.1016/j.compag.2023.107737_b6 article-title: Digital evaluation of leaf area of an individual tree canopy in the apple orchard using the LIDAR measurement system publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2019.105158 – ident: 10.1016/j.compag.2023.107737_b8 – volume: 57 start-page: 1336 issue: 3 year: 2018 ident: 10.1016/j.compag.2023.107737_b47 article-title: Stem–leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2018.2866056 – volume: 14 start-page: 642 issue: 3 year: 2022 ident: 10.1016/j.compag.2023.107737_b94 article-title: Identification of the yield of camellia oleifera based on color space by the optimized mean shift clustering algorithm using terrestrial laser scanning publication-title: Remote Sens. doi: 10.3390/rs14030642 – volume: 78 start-page: 352 year: 2019 ident: 10.1016/j.compag.2023.107737_b112 article-title: Evaluation of 3D point cloud-based models for the prediction of grassland biomass publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 42 start-page: 738 issue: 2 year: 2021 ident: 10.1016/j.compag.2023.107737_b126 article-title: Research on volume prediction of single tree canopy based on three-dimensional (3D) LiDAR and clustering segmentation publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2020.1811917 – volume: 11 issue: 1 year: 2021 ident: 10.1016/j.compag.2023.107737_b17 article-title: Robotic fertilisation using localisation systems based on point clouds in strip-cropping fields publication-title: Agronomy – volume: 10 issue: 5 year: 2020 ident: 10.1016/j.compag.2023.107737_b124 article-title: Analysis of plant height changes of lodged maize using UAV-LiDAR data publication-title: Agriculture doi: 10.3390/agriculture10050146 – volume: 14 start-page: 585 issue: 3 year: 2022 ident: 10.1016/j.compag.2023.107737_b54 article-title: UAV oblique imagery with an adaptive micro-terrain model for estimation of leaf area index and height of maize canopy from 3D point clouds publication-title: Remote Sens. doi: 10.3390/rs14030585 – volume: 104 start-page: 112 year: 2015 ident: 10.1016/j.compag.2023.107737_b49 article-title: Comparative classification analysis of post-harvest growth detection from terrestrial LiDAR point clouds in precision agriculture publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2015.03.003 – volume: 3 start-page: 3043 issue: 4 year: 2018 ident: 10.1016/j.compag.2023.107737_b20 article-title: Automatic segmentation of tree structure from point cloud data publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2018.2849499 – volume: 9 start-page: 160 issue: 2 year: 2019 ident: 10.1016/j.compag.2023.107737_b24 article-title: Evaluation of soil water management properties based on LiDAR data and soil analyses, at farm level publication-title: Nat. Resour. Sustain. Dev. – volume: 10 issue: 2 year: 2020 ident: 10.1016/j.compag.2023.107737_b50 article-title: Acquiring plant features with optical sensing devices in an organic strip-cropping system publication-title: Agronomy doi: 10.3390/agronomy10020197 – volume: 14 start-page: 2292 issue: 10 year: 2022 ident: 10.1016/j.compag.2023.107737_b25 article-title: Individual maize location and height estimation in field from UAV-Borne LiDAR and RGB images publication-title: Remote Sens. doi: 10.3390/rs14102292 – volume: 14 start-page: 1048 issue: 4 year: 2022 ident: 10.1016/j.compag.2023.107737_b11 article-title: Using LiDAR system as a data source for agricultural land boundaries publication-title: Remote Sens. doi: 10.3390/rs14041048 – year: 2022 ident: 10.1016/j.compag.2023.107737_b26 article-title: Evaluation of coupled wind/wave model simulations of offshore winds in the Mid-Atlantic bight using LiDAR-equipped buoys publication-title: Mon. Weather Rev. doi: 10.1175/MWR-D-21-0166.1 – volume: 3059 start-page: 143 year: 1997 ident: 10.1016/j.compag.2023.107737_b105 article-title: Short-range lidar measurement of top fruit tree canopies for pesticide applications research in the United Kingdom – volume: 15 start-page: 4027 year: 2022 ident: 10.1016/j.compag.2023.107737_b123 article-title: Evaluation of leaf area index (LAI) of broadacre crops using UAS-based LiDAR point clouds and multispectral imagery publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2022.3172491 – volume: 187 year: 2021 ident: 10.1016/j.compag.2023.107737_b110 article-title: A procedure for automated tree pruning suggestion using LiDAR scans of fruit trees publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2021.106274 – volume: 12 start-page: 1241 issue: 8 year: 2022 ident: 10.1016/j.compag.2023.107737_b31 article-title: Innovative leaf area detection models for orchard tree thick canopy based on LiDAR point cloud data publication-title: Agriculture doi: 10.3390/agriculture12081241 – volume: 59 start-page: 8638 issue: 28 year: 2020 ident: 10.1016/j.compag.2023.107737_b117 article-title: Early forest-fire detection using scanning polarization LiDAR publication-title: Appl. Opt. doi: 10.1364/AO.399766 – volume: 43 start-page: 299 issue: 1 year: 2022 ident: 10.1016/j.compag.2023.107737_b35 article-title: Tree crown segmentation in three dimensions using density models derived from airborne laser scanning publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2021.2018149 – volume: 1 issue: 1 year: 2018 ident: 10.1016/j.compag.2023.107737_b115 article-title: Plant 3D reconstruction based on LiDAR and multi-view sequence images publication-title: Int. J. Precis. Agric. Aviat. – volume: 42 year: 2022 ident: 10.1016/j.compag.2023.107737_b9 article-title: Interpreting landscapes of pre-columbian raised-field agriculture using high-resolution LiDAR topography publication-title: J. Archaeol. Sci.: Reports – volume: 42 issue: 2 year: 2019 ident: 10.1016/j.compag.2023.107737_b104 article-title: The benefit of spectral and point-cloud data for herbage yield and quality assessment of grasslands publication-title: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. – volume: 12 start-page: 2509 issue: 10 year: 2022 ident: 10.1016/j.compag.2023.107737_b58 article-title: Precision variable-rate spraying robot by using single 3D LIDAR in orchards publication-title: Agronomy doi: 10.3390/agronomy12102509 – year: 2021 ident: 10.1016/j.compag.2023.107737_b14 – volume: 82 year: 2019 ident: 10.1016/j.compag.2023.107737_b32 article-title: Apple orchard inventory with a LiDAR equipped unmanned aerial system publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 93 start-page: 494 issue: 1 year: 2021 ident: 10.1016/j.compag.2023.107737_b80 article-title: Near-field high-resolution maps of the ridgecrest earthquakes from aerial imagery publication-title: Seismol. Res. Lett. doi: 10.1785/0220210234 – start-page: 465 year: 2020 ident: 10.1016/j.compag.2023.107737_b122 article-title: Toward a structural description of row crops using UAS-based LiDAR point clouds – volume: 22 issue: 5 year: 2022 ident: 10.1016/j.compag.2023.107737_b88 article-title: Analysis of methods for determining shallow waterbody depths based on images taken by unmanned aerial vehicles publication-title: Sensors doi: 10.3390/s22051844 – volume: 72 issue: 15 year: 2019 ident: 10.1016/j.compag.2023.107737_b30 article-title: Real-time, non-destructive and in-field foliage yield and growth rate measurement in perennial ryegrass (Lolium perenne L.) publication-title: Plant Methods – volume: 3 start-page: 193 year: 2022 ident: 10.1016/j.compag.2023.107737_b21 article-title: AI-driven maize yield forecasting using unmanned aerial vehicle-based hyperspectral and LiDAR data fusion publication-title: ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. doi: 10.5194/isprs-annals-V-3-2022-193-2022 – start-page: 339 year: 2010 ident: 10.1016/j.compag.2023.107737_b109 article-title: Plant species classification using a 3D LiDAR sensor and machine learning – start-page: 1 year: 2018 ident: 10.1016/j.compag.2023.107737_b38 article-title: LiDAR-based SLAM and autonomous navigation for forestry quadrotors – volume: 3 year: 2020 ident: 10.1016/j.compag.2023.107737_b60 article-title: UAV-based sorghum growth monitoring: A comparative analysis of LiDAR and photogrammetry publication-title: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences – volume: 14 start-page: 24212 issue: 12 year: 2014 ident: 10.1016/j.compag.2023.107737_b33 article-title: Effects of reduced terrestrial LiDAR point density on high-resolution grain crop surface models in precision agriculture publication-title: Sensors doi: 10.3390/s141224212 – volume: 99 start-page: 42 year: 2018 ident: 10.1016/j.compag.2023.107737_b78 article-title: Iterative individual plant clustering in maize with assembled 2D LiDAR data publication-title: Comput. Ind. doi: 10.1016/j.compind.2018.03.023 – volume: 51 start-page: 345 issue: 4 year: 2021 ident: 10.1016/j.compag.2023.107737_b7 article-title: Interpretation of spatiotemporal gravity changes accompanying the earthquake of 21 August 2017 on Ischia (Italy) publication-title: Contrib. Geophys. Geodesy doi: 10.31577/congeo.2021.51.4.3 – volume: 58 start-page: 881 issue: 4 year: 2007 ident: 10.1016/j.compag.2023.107737_b71 article-title: 3D LiDAR imaging for detecting and understanding plant responses and canopy structure publication-title: J. Exp. Bot. doi: 10.1093/jxb/erl142 – volume: 22 issue: 4 year: 2022 ident: 10.1016/j.compag.2023.107737_b108 article-title: Evaluation of a new lightweight UAV-borne topo-bathymetric LiDAR for shallow water bathymetry and object detection publication-title: Sensors doi: 10.3390/s22041379 – volume: 10 start-page: 1 issue: 6 year: 2015 ident: 10.1016/j.compag.2023.107737_b96 article-title: High-throughput 3D monitoring of agricultural-tree plantations with unmanned aerial vehicle (UAV) technology publication-title: PLOS ONE doi: 10.1371/journal.pone.0130479 – volume: 58370 year: 2017 ident: 10.1016/j.compag.2023.107737_b103 article-title: ByeLab: An agricultural mobile robot prototype for proximal sensing and precision farming – volume: 74 start-page: 109 issue: 3 year: 2018 ident: 10.1016/j.compag.2023.107737_b43 article-title: Automatic individual tree detection and canopy segmentation from three-dimensional point cloud images obtained from ground-based LiDAR publication-title: J. Agric. Meteorol. doi: 10.2480/agrmet.D-18-00012 – volume: 9 issue: 7 year: 2019 ident: 10.1016/j.compag.2023.107737_b99 article-title: AgROS: A robot operating system based emulation tool for agricultural robotics publication-title: Agronomy doi: 10.3390/agronomy9070403 – volume: 80 issue: 17 year: 2021 ident: 10.1016/j.compag.2023.107737_b41 article-title: Evaluation of geohazards in the Cape Girardeau area using LiDAR and GIS, Southeast Missouri, USA publication-title: Environ. Earth Sci. doi: 10.1007/s12665-021-09869-z – volume: 198 start-page: 91 year: 2020 ident: 10.1016/j.compag.2023.107737_b95 article-title: Developing object-based image procedures for classifying and characterising different protected agriculture structures using LiDAR and orthophoto publication-title: Biosyst. Eng. doi: 10.1016/j.biosystemseng.2020.07.017 – volume: 12 issue: 10 year: 2020 ident: 10.1016/j.compag.2023.107737_b114 article-title: Suitability of airborne and terrestrial laser scanning for mapping tree crop structural metrics for improved orchard management publication-title: Remote Sens. doi: 10.3390/rs12101647 – volume: 9 year: 2022 ident: 10.1016/j.compag.2023.107737_b1 article-title: Localization and mapping on agriculture based on point-feature extraction and semiplanes segmentation from 3D LiDAR data publication-title: Front. Robotics and AI doi: 10.3389/frobt.2022.832165 – volume: 102 start-page: 128 issue: 2 year: 2009 ident: 10.1016/j.compag.2023.107737_b81 article-title: A tractor-mounted scanning LIDAR for the non-destructive measurement of vegetative volume and surface area of tree-row plantations: A comparison with conventional destructive measurements publication-title: Biosyst. Eng. doi: 10.1016/j.biosystemseng.2008.10.009 – volume: 18 start-page: 3731 issue: 11 year: 2018 ident: 10.1016/j.compag.2023.107737_b119 article-title: Wheat height estimation using LiDAR in comparison to ultrasonic sensor and UAS publication-title: Sensors doi: 10.3390/s18113731 – volume: 583 year: 2020 ident: 10.1016/j.compag.2023.107737_b89 article-title: Evaluating watershed-based optimized decision support framework for conservation practice placement in Plum Creek Minnesota publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2020.124573 – volume: XLI-B8 start-page: 365 year: 2016 ident: 10.1016/j.compag.2023.107737_b59 article-title: Automatic river network extraction from LiDAR data publication-title: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. doi: 10.5194/isprs-archives-XLI-B8-365-2016 – volume: 21 start-page: 473 issue: 3 year: 2020 ident: 10.1016/j.compag.2023.107737_b68 article-title: The novel use of proximal photogrammetry and terrestrial LiDAR to quantify the structural complexity of orchard trees publication-title: Precis. Agric. doi: 10.1007/s11119-019-09676-4 |
SSID | ssj0016987 |
Score | 2.6144826 |
SecondaryResourceType | review_article |
Snippet | In recent years, Light Detection and Ranging (LiDAR) technology has been one of the most innovative subjects in laser scanning, remote sensing, and object... |
SourceID | proquest crossref elsevier |
SourceType | Aggregation Database Enrichment Source Index Database Publisher |
StartPage | 107737 |
SubjectTerms | agriculture Agriculture 5.0 electronics Food sustainability lidar Light detection and ranging Point cloud processing precision agriculture Remote sensing taxonomy trees vegetation vision |
Title | LiDAR applications in precision agriculture for cultivating crops: A review of recent advances |
URI | https://dx.doi.org/10.1016/j.compag.2023.107737 https://www.proquest.com/docview/2834204408 |
Volume | 207 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1La9wwEBZhe0kPpU1amrQNCuTqXVsPS-7NbLNsHs2h3UJOFbI9WhyCd9nHNb89I1tOmhAI9CaLkTEjefQJfTMfISfWJVnJCh1ZlVSR4HEZ6UrKiFmtnAYWp5XPd_55lU7_iPNreb1Dxn0ujKdVhtjfxfQ2WoeeUfDmaFnXo98IVnSSZohQ_J4tfU1QIZRf5cO7B5oHGuguZTrF0xJa9-lzLcer5XnPh15CHLuU8mroL29PzwJ1u_tM3pN3ATbSvPuyD2QHmj3yNp-vQukM2Cd_L-sf-S_67400rRu6XAUVHWofrSlCVerbrbZZM6deyGv9nea0S2WhC4ctT9ykgSSw_khmk9PZeBoF9YSo5DzbRHhSwolISw1gCxCJFVAUAhjEOAvMcgQSqnRWMl0VGddWOhE7yWNR-IIwCf9EBs2igc-EKlcCQ1xVStCi0DZjIAFiZXklHDhxQHjvM1OGyuJe4OLW9BSyG9N52nhPm87TByR6GLXsKmu8Yq_66TBPVojB4P_KyON-9gz-PP5GxDaw2K4NYivBWtHtw_9--xey6586Ns9XMtistvANgcqmOGpX4hF5k59dTK_uAcMT6W4 |
linkProvider | Elsevier |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT-MwEB5BOSwcEI9dLW8jcQ1N_EgcbhEPFSg9QFfihOUk46orlFZt-f_YiQO7CAmJm-XYUTTjjD_L38wHcKJNlBY0l4FOojLgLCwCWQoRUC0TI5GGcenyne8Gce8Pv3kUj0tw3ubCOFqlj_1NTK-jte_pemt2p-Nx98GCFRnFqUUobs8WfBlWXHUq0YGV7Pq2N3i7TIhT2WRNx_bAZCe0GXQ1zaumeo9OnYq47UoSJ4j--Q71IVbXG9DVBqx75Eiy5uM2YQmrLVjLRjNfPQO34ak_vsjuyb-X0mRckenMC-kQ_T6aWLRKXLuWN6tGxGl5zc9IRppsFjIxtuW4m8TzBOY_YXh1OTzvBV5AISgYSxeBPSxZX8SFRNQ58khzzHOOFEPrCKqZxRJJYbSgssxTJrUwPDSChTx3NWEi9gs61aTC30ASUyC10KoQKHkudUpRIIaJZiU3aPgOsNZmqvDFxZ3GxbNqWWR_VWNp5SytGkvvQPA2a9oU1_hifNK6Q_23SJSN_1_MPG69p-z_4y5FdIWTl7my8IrTWnd799tvP4IfveFdX_WvB7d7sOqeNOSefegsZi94YHHLIj_06_IVxRXsHw |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=LiDAR+applications+in+precision+agriculture+for+cultivating+crops%3A+A+review+of+recent+advances&rft.jtitle=Computers+and+electronics+in+agriculture&rft.au=Rivera%2C+Gilberto&rft.au=Porras%2C+Ra%C3%BAl&rft.au=Florencia%2C+Rogelio&rft.au=S%C3%A1nchez-Sol%C3%ADs%2C+J.+Patricia&rft.date=2023-04-01&rft.issn=0168-1699&rft.volume=207+p.107737-&rft_id=info:doi/10.1016%2Fj.compag.2023.107737&rft.externalDBID=NO_FULL_TEXT |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0168-1699&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0168-1699&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0168-1699&client=summon |