Identification and recognition of rice diseases and pests using convolutional neural networks
Accurate and timely detection of diseases and pests in rice plants can help farmers in applying timely treatment on the plants and thereby can reduce the economic losses substantially. Recent developments in deep learning-based convolutional neural networks (CNN) have greatly improved image classifi...
Saved in:
Published in | Biosystems engineering Vol. 194; pp. 112 - 120 |
---|---|
Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
01.06.2020
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Accurate and timely detection of diseases and pests in rice plants can help farmers in applying timely treatment on the plants and thereby can reduce the economic losses substantially. Recent developments in deep learning-based convolutional neural networks (CNN) have greatly improved image classification accuracy. Being motivated by the success of CNNs in image classification, deep learning-based approaches have been developed in this paper for detecting diseases and pests from rice plant images. The contribution of this paper is two fold: (i) State-of-the-art large scale architectures such as VGG16 and InceptionV3 have been adopted and fine tuned for detecting and recognising rice diseases and pests. Experimental results show the effectiveness of these models with real datasets. (ii) Since large scale architectures are not suitable for mobile devices, a two-stage small CNN architecture has been proposed, and compared with the state-of-the-art memory efficient CNN architectures such as MobileNet, NasNet Mobile and SqueezeNet. Experimental results show that the proposed architecture can achieve the desired accuracy of 93.3% with a significantly reduced model size (e.g., 99% smaller than VGG16).
•Rice disease dataset (1426 images, nine classes) collected in real life scenario.•Three different training methods compared on state-of-the-art CNN architectures.•Two stage training concept implemented on memory efficient Simple CNN.•Simple CNN performance comparison with state-of-the-art memory efficient CNNs. |
---|---|
AbstractList | Accurate and timely detection of diseases and pests in rice plants can help farmers in applying timely treatment on the plants and thereby can reduce the economic losses substantially. Recent developments in deep learning-based convolutional neural networks (CNN) have greatly improved image classification accuracy. Being motivated by the success of CNNs in image classification, deep learning-based approaches have been developed in this paper for detecting diseases and pests from rice plant images. The contribution of this paper is two fold: (i) State-of-the-art large scale architectures such as VGG16 and InceptionV3 have been adopted and fine tuned for detecting and recognising rice diseases and pests. Experimental results show the effectiveness of these models with real datasets. (ii) Since large scale architectures are not suitable for mobile devices, a two-stage small CNN architecture has been proposed, and compared with the state-of-the-art memory efficient CNN architectures such as MobileNet, NasNet Mobile and SqueezeNet. Experimental results show that the proposed architecture can achieve the desired accuracy of 93.3% with a significantly reduced model size (e.g., 99% smaller than VGG16).
•Rice disease dataset (1426 images, nine classes) collected in real life scenario.•Three different training methods compared on state-of-the-art CNN architectures.•Two stage training concept implemented on memory efficient Simple CNN.•Simple CNN performance comparison with state-of-the-art memory efficient CNNs. |
Author | Iqbal Khan, Mohammad A. Apon, Sajid H. Wasif, Abu Nowrin, Farzana Rahman, Chowdhury R. Ali, Mohammed E. Arko, Preetom S. |
Author_xml | – sequence: 1 givenname: Chowdhury R. surname: Rahman fullname: Rahman, Chowdhury R. email: rafeed.rahman015@gmail.com organization: United International University, Dhaka, Bangladesh – sequence: 2 givenname: Preetom S. orcidid: 0000-0002-2271-144X surname: Arko fullname: Arko, Preetom S. organization: Bangladesh University of Engineering and Technology, Dhaka, Bangladesh – sequence: 3 givenname: Mohammed E. surname: Ali fullname: Ali, Mohammed E. organization: Bangladesh University of Engineering and Technology, Dhaka, Bangladesh – sequence: 4 givenname: Mohammad A. surname: Iqbal Khan fullname: Iqbal Khan, Mohammad A. organization: Bangladesh Rice Research Institute, Gazipur, Bangladesh – sequence: 5 givenname: Sajid H. surname: Apon fullname: Apon, Sajid H. organization: Bangladesh University of Engineering and Technology, Dhaka, Bangladesh – sequence: 6 givenname: Farzana surname: Nowrin fullname: Nowrin, Farzana organization: Bangladesh Rice Research Institute, Gazipur, Bangladesh – sequence: 7 givenname: Abu surname: Wasif fullname: Wasif, Abu organization: Bangladesh University of Engineering and Technology, Dhaka, Bangladesh |
BookMark | eNqNkEFLAzEQhYNUsK3-hwXPuybZTbLFk5RqCwUvepSQTWZLapuUzLbSf--2FcGbp28G3nvMvBEZhBiAkHtGC0aZfFgXjY94xA62CGFVcMppQcuixxUZMlGqXDA-GfzOjN6QEeKaUiZUJYfkY-EgdL711nQ-hswElyWwcRX8eY9tlryFzHkEg4BnwQ6ww2yPPqwyG8MhbvYnsdlkAfbpjO4rpk-8Jdet2SDc_XBM3p9nb9N5vnx9WUyflrktuepyIRtQUlZGyUoqbiijFOq65EKZmpm6YcB460oHbtLU1KimZVVjmBB1Y_tPyjF5vOTaFBETtHqX_Nako2ZUn5rSa_2nKX1qStNS9-jds4sb-hMPHpJG6yFYcL6votMu-n_lfAOs4H4q |
CitedBy_id | crossref_primary_10_1016_j_biosystemseng_2022_11_007 crossref_primary_10_1016_j_biosystemseng_2024_05_014 crossref_primary_10_3389_fpls_2021_789630 crossref_primary_10_1016_j_compag_2022_106780 crossref_primary_10_3389_fpls_2021_701038 crossref_primary_10_1088_1755_1315_1032_1_012017 crossref_primary_10_3103_S1060992X2301006X crossref_primary_10_24018_ejeng_2023_8_2_2773 crossref_primary_10_3233_JIFS_230655 crossref_primary_10_3390_app13031346 crossref_primary_10_1016_j_bspc_2023_104710 crossref_primary_10_3390_electronics12030508 crossref_primary_10_3103_S8756699022030074 crossref_primary_10_1007_s11042_023_15220_4 crossref_primary_10_1016_j_engappai_2023_107060 crossref_primary_10_1155_2022_9179998 crossref_primary_10_1016_j_ecoinf_2022_101556 crossref_primary_10_1371_journal_pone_0257008 crossref_primary_10_1109_JSEN_2022_3182304 crossref_primary_10_1155_2022_1757888 crossref_primary_10_1007_s11042_023_14884_2 crossref_primary_10_1088_1757_899X_1125_1_012021 crossref_primary_10_3389_fpls_2023_1180716 crossref_primary_10_1080_03772063_2023_2181229 crossref_primary_10_3390_app12094795 crossref_primary_10_2166_wst_2024_122 crossref_primary_10_1016_j_compag_2022_106703 crossref_primary_10_3389_fpls_2023_1269371 crossref_primary_10_3390_agronomy13040961 crossref_primary_10_3390_s22197384 crossref_primary_10_1016_j_array_2024_100353 crossref_primary_10_1016_j_aiia_2023_09_001 crossref_primary_10_1142_S0219691321500430 crossref_primary_10_1186_s13007_021_00770_1 crossref_primary_10_1016_j_cropro_2023_106488 crossref_primary_10_1109_ACCESS_2022_3140815 crossref_primary_10_1016_j_compag_2022_107340 crossref_primary_10_3390_agriculture13020442 crossref_primary_10_32604_csse_2022_022017 crossref_primary_10_3390_agriculture13061155 crossref_primary_10_1007_s40858_021_00459_9 crossref_primary_10_3389_fpls_2021_671134 crossref_primary_10_1007_s11760_021_01909_2 crossref_primary_10_7717_peerj_cs_1384 crossref_primary_10_1016_j_engappai_2023_106020 crossref_primary_10_3390_agronomy12092121 crossref_primary_10_1016_j_compeleceng_2022_108492 crossref_primary_10_1016_j_bcab_2023_102726 crossref_primary_10_3390_electronics11142110 crossref_primary_10_1016_j_image_2022_116857 crossref_primary_10_1016_j_micpro_2022_104687 crossref_primary_10_1016_j_eswa_2020_114514 crossref_primary_10_1016_j_cropro_2024_106816 crossref_primary_10_3390_s22155550 crossref_primary_10_1016_j_compag_2022_107175 crossref_primary_10_1007_s00521_022_07793_2 crossref_primary_10_1007_s11042_023_18099_3 crossref_primary_10_1016_j_fraope_2023_100024 crossref_primary_10_47836_pjst_31_6_13 crossref_primary_10_1145_3587466 crossref_primary_10_3103_S8756699024700109 crossref_primary_10_3390_agriculture13030713 crossref_primary_10_1016_j_inpa_2021_10_002 crossref_primary_10_3390_plants11070970 crossref_primary_10_1088_1755_1315_1097_1_012042 crossref_primary_10_3390_app13084928 crossref_primary_10_1016_j_heliyon_2024_e32400 crossref_primary_10_46481_jnsps_2021_217 crossref_primary_10_3390_plants12112225 crossref_primary_10_37391_ijeer_100405 crossref_primary_10_1007_s00521_023_09173_w crossref_primary_10_1016_j_jia_2024_03_075 crossref_primary_10_3390_agronomy13112731 crossref_primary_10_1007_s11042_023_14994_x crossref_primary_10_4081_jae_2023_1544 crossref_primary_10_3390_su15021233 crossref_primary_10_1016_j_micpro_2020_103607 crossref_primary_10_3390_ijpb14040087 crossref_primary_10_3390_sym13030511 crossref_primary_10_1016_j_eswa_2022_118117 crossref_primary_10_1080_03235408_2021_2015866 crossref_primary_10_3390_agriculture13051066 crossref_primary_10_1016_j_ecoinf_2021_101460 crossref_primary_10_1002_ps_7209 crossref_primary_10_1109_TCBB_2022_3229114 crossref_primary_10_1615_JFlowVisImageProc_2023047476 crossref_primary_10_1186_s42400_023_00156_x crossref_primary_10_3390_agronomy13082139 crossref_primary_10_1007_s10661_024_12504_6 crossref_primary_10_1038_s41598_022_10140_z crossref_primary_10_1016_j_compag_2023_108408 crossref_primary_10_1109_ACCESS_2022_3194925 crossref_primary_10_1088_1742_6596_1911_1_012004 crossref_primary_10_1016_j_asoc_2021_107901 crossref_primary_10_17221_158_2022_HORTSCI crossref_primary_10_1080_01969722_2022_2122001 crossref_primary_10_3389_fpls_2022_1010981 crossref_primary_10_2139_ssrn_4135061 crossref_primary_10_3389_fpls_2022_1077568 crossref_primary_10_3390_agriengineering6010018 crossref_primary_10_3390_agronomy13092242 crossref_primary_10_1016_j_biosystemseng_2022_04_005 crossref_primary_10_7717_peerj_cs_432 crossref_primary_10_1155_2022_5771148 crossref_primary_10_1111_ppa_13866 crossref_primary_10_1016_j_aiia_2023_11_001 crossref_primary_10_3390_f14071290 crossref_primary_10_1515_ijfe_2023_0055 crossref_primary_10_1007_s11277_023_10333_3 crossref_primary_10_34133_plantphenomics_0013 crossref_primary_10_1016_j_compag_2021_106625 crossref_primary_10_3390_su142013610 crossref_primary_10_1142_S0218001424540089 crossref_primary_10_1038_s41598_021_95240_y crossref_primary_10_1007_s11042_023_15393_y crossref_primary_10_1007_s42979_024_02816_2 crossref_primary_10_32604_cmc_2023_027269 crossref_primary_10_1038_s41598_023_43465_4 crossref_primary_10_1109_ACCESS_2023_3281508 crossref_primary_10_1016_j_compag_2021_106192 crossref_primary_10_3390_agriculture13020364 crossref_primary_10_1109_ACCESS_2024_3371511 crossref_primary_10_1007_s11119_022_09927_x crossref_primary_10_3390_insects12080705 crossref_primary_10_1109_ACCESS_2021_3130472 crossref_primary_10_1016_j_aiia_2023_08_005 crossref_primary_10_1016_j_ecoinf_2022_101620 crossref_primary_10_3390_agronomy13092232 crossref_primary_10_1016_j_heliyon_2024_e33328 crossref_primary_10_1016_j_inpa_2024_04_006 crossref_primary_10_3390_agronomy14040864 crossref_primary_10_1007_s42161_020_00683_3 crossref_primary_10_3390_fi15030086 crossref_primary_10_2139_ssrn_4188680 crossref_primary_10_1007_s11042_024_18730_x crossref_primary_10_3390_plants11202668 crossref_primary_10_1016_j_sna_2024_115127 crossref_primary_10_3390_agronomy13061633 crossref_primary_10_31642_JoKMC_2018_100114 crossref_primary_10_1002_jsfa_13636 crossref_primary_10_3390_foods11233914 crossref_primary_10_1007_s11042_021_10599_4 crossref_primary_10_3389_fpls_2022_829479 crossref_primary_10_32604_cmc_2023_038446 crossref_primary_10_3233_JIFS_213388 crossref_primary_10_3390_agriculture11050460 crossref_primary_10_3390_rs13224587 crossref_primary_10_3103_S1060992X2104007X crossref_primary_10_1093_comjnl_bxab022 crossref_primary_10_3390_s23063147 crossref_primary_10_3390_su15086815 crossref_primary_10_32604_csse_2022_022206 crossref_primary_10_1016_j_susoc_2023_03_001 crossref_primary_10_1016_j_atech_2024_100480 crossref_primary_10_3390_agronomy12092096 crossref_primary_10_12720_jait_14_5_907_917 crossref_primary_10_1016_j_compag_2023_108342 crossref_primary_10_3390_agriculture10100436 crossref_primary_10_3390_agriculture13010069 crossref_primary_10_1007_s41348_023_00803_y crossref_primary_10_3390_insects13110978 crossref_primary_10_1515_biol_2022_0689 crossref_primary_10_1016_j_compag_2023_107657 crossref_primary_10_1080_03235408_2023_2183792 crossref_primary_10_3390_plants12030524 crossref_primary_10_3103_S8756699021040117 crossref_primary_10_3390_agriculture13122253 crossref_primary_10_3390_agronomy13061659 crossref_primary_10_3390_rs16122047 crossref_primary_10_32628_IJSRST523103150 crossref_primary_10_1007_s00521_022_07722_3 crossref_primary_10_1371_journal_pone_0301174 crossref_primary_10_21605_cukurovaumfd_1377763 crossref_primary_10_1016_j_aiia_2023_03_001 crossref_primary_10_36548_jaicn_2023_3_003 crossref_primary_10_1016_j_measen_2023_100966 crossref_primary_10_1016_j_jer_2023_09_033 crossref_primary_10_1016_j_biosystemseng_2021_06_008 crossref_primary_10_1007_s10343_022_00765_5 crossref_primary_10_32604_cmc_2022_028504 crossref_primary_10_1109_ACCESS_2023_3284760 crossref_primary_10_17798_bitlisfen_1014393 crossref_primary_10_1016_j_compag_2022_107407 crossref_primary_10_3389_fbioe_2022_855667 |
Cites_doi | 10.1080/08839514.2017.1315516 10.1016/j.compag.2018.08.013 10.1155/2016/3289801 10.1016/j.neucom.2017.06.023 10.3389/fpls.2016.01419 10.3389/fpls.2017.01741 10.3390/s17092022 10.1094/PHYTO-11-16-0417-R 10.3390/sym10010011 10.1016/j.biosystemseng.2019.02.002 10.1155/2017/2917536 10.1016/j.compag.2018.01.009 10.1111/j.1477-9552.2002.tb00040.x |
ContentType | Journal Article |
Copyright | 2020 IAgrE |
Copyright_xml | – notice: 2020 IAgrE |
DBID | AAYXX CITATION |
DOI | 10.1016/j.biosystemseng.2020.03.020 |
DatabaseName | CrossRef |
DatabaseTitle | CrossRef |
DatabaseTitleList | |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Engineering Agriculture |
EISSN | 1537-5129 |
EndPage | 120 |
ExternalDocumentID | 10_1016_j_biosystemseng_2020_03_020 S1537511020300830 |
GroupedDBID | --K --M .~1 0R~ 1B1 1RT 1~. 1~5 23N 4.4 457 4G. 53G 5GY 5VS 6J9 7-5 71M 8P~ AABVA AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALCJ AALRI AAOAW AAQFI AATLK AAXUO ABFNM ABFRF ABGRD ABJNI ABMAC ABXDB ABYKQ ACDAQ ACGFO ACGFS ACNNM ACRLP ADBBV ADEZE ADMUD ADQTV ADTZH AEBSH AECPX AEFWE AEKER AENEX AEQOU AFKWA AFTJW AFXIZ AGHFR AGUBO AGYEJ AHJVU AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BJAXD BKOJK BLXMC CAG CBWCG COF CS3 DM4 DU5 EBS EFBJH EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FIRID FNPLU FYGXN G-Q GBLVA HVGLF HZ~ IHE J1W JJJVA K-O KOM LG5 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 PC. Q38 RIG ROL RPZ SDF SDG SDP SES SEW SPC SPCBC SSA SST SSZ T5K UHS UNMZH ~G- ~KM AAHBH AAXKI AAYXX AFJKZ AKRWK CITATION |
ID | FETCH-LOGICAL-c327t-56be7664a764672a0100e883257a81a8b1e12fd3ded9b80a7bf14ba1558bc0153 |
IEDL.DBID | AIKHN |
ISSN | 1537-5110 |
IngestDate | Thu Sep 26 17:16:56 EDT 2024 Fri Feb 23 02:47:14 EST 2024 |
IsPeerReviewed | true |
IsScholarly | true |
Keywords | Pest Memory efficient Two stage training Rice disease Convolutional neural network Dataset |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c327t-56be7664a764672a0100e883257a81a8b1e12fd3ded9b80a7bf14ba1558bc0153 |
ORCID | 0000-0002-2271-144X |
PageCount | 9 |
ParticipantIDs | crossref_primary_10_1016_j_biosystemseng_2020_03_020 elsevier_sciencedirect_doi_10_1016_j_biosystemseng_2020_03_020 |
PublicationCentury | 2000 |
PublicationDate | June 2020 2020-06-00 |
PublicationDateYYYYMMDD | 2020-06-01 |
PublicationDate_xml | – month: 06 year: 2020 text: June 2020 |
PublicationDecade | 2020 |
PublicationTitle | Biosystems engineering |
PublicationYear | 2020 |
Publisher | Elsevier Ltd |
Publisher_xml | – name: Elsevier Ltd |
References | Calpe (bib7) 2002 Babu, Rao (bib2) 2007 Simonyan, Zisserman (bib18) 2014 Fuentes, Yoon, Kim, Park (bib12) 2017; 17 Szegedy, Liu, Jia, Sermanet, Reed, Anguelov (bib20) 2015 Ferentinos (bib11) 2018; 145 Brahimi, Boukhalfa, Moussaoui (bib6) 2017; 31 Mahmud, Hossain, Ahmad (bib16) 2016 Karmokar, Ullah, Siddiquee, Alam (bib13) 2015; 114 Sladojevic, Arsenovic, Anderla, Culibrk, Stefanovic (bib19) 2016 Bhagawati, Bhagawati, Singh, Nongthombam, Sarmah, Bhagawati (bib5) 2015; 3 Atole, Park (bib1) 2018; 9 Cruz, Luvisi, De Bellis, Ampatzidis (bib9) 2017; 8 DeChant, Wiesner-Hanks, Chen, Stewart, Yosinski, Gore (bib10) 2017; 107 Liu, Zhang, He, Li (bib14) 2018; 10 Lu, Yi, Zeng, Liu, Zhang (bib15) 2017; 267 Mohanty, Hughes, Salathé (bib17) 2016; 7 Coelli, Rahman, Thirtle (bib8) 2002; 53 Barbedo (bib4) 2019; 180 Wang, Sun, Wang (bib21) 2017 Barbedo (bib3) 2018; 153 Barbedo (10.1016/j.biosystemseng.2020.03.020_bib4) 2019; 180 Simonyan (10.1016/j.biosystemseng.2020.03.020_bib18) 2014 Mohanty (10.1016/j.biosystemseng.2020.03.020_bib17) 2016; 7 Calpe (10.1016/j.biosystemseng.2020.03.020_bib7) 2002 Wang (10.1016/j.biosystemseng.2020.03.020_bib21) 2017 Szegedy (10.1016/j.biosystemseng.2020.03.020_bib20) 2015 Liu (10.1016/j.biosystemseng.2020.03.020_bib14) 2018; 10 Cruz (10.1016/j.biosystemseng.2020.03.020_bib9) 2017; 8 Barbedo (10.1016/j.biosystemseng.2020.03.020_bib3) 2018; 153 DeChant (10.1016/j.biosystemseng.2020.03.020_bib10) 2017; 107 Mahmud (10.1016/j.biosystemseng.2020.03.020_bib16) 2016 Lu (10.1016/j.biosystemseng.2020.03.020_bib15) 2017; 267 Atole (10.1016/j.biosystemseng.2020.03.020_bib1) 2018; 9 Sladojevic (10.1016/j.biosystemseng.2020.03.020_bib19) 2016 Brahimi (10.1016/j.biosystemseng.2020.03.020_bib6) 2017; 31 Karmokar (10.1016/j.biosystemseng.2020.03.020_bib13) 2015; 114 Coelli (10.1016/j.biosystemseng.2020.03.020_bib8) 2002; 53 Ferentinos (10.1016/j.biosystemseng.2020.03.020_bib11) 2018; 145 Babu (10.1016/j.biosystemseng.2020.03.020_bib2) 2007 Bhagawati (10.1016/j.biosystemseng.2020.03.020_bib5) 2015; 3 Fuentes (10.1016/j.biosystemseng.2020.03.020_bib12) 2017; 17 |
References_xml | – volume: 8 start-page: 1741 year: 2017 ident: bib9 article-title: X-fido: An effective application for detecting olive quick decline syndrome with deep learning and data fusion publication-title: Frontiers of Plant Science contributor: fullname: Ampatzidis – volume: 180 start-page: 96 year: 2019 end-page: 107 ident: bib4 article-title: Plant disease identification from individual lesions and spots using deep learning publication-title: Biosystems Engineering contributor: fullname: Barbedo – year: 2002 ident: bib7 article-title: Rice in world trade, part ii. status of the world rice market publication-title: Proceedings of the 20 th session of the international rice commission contributor: fullname: Calpe – volume: 31 start-page: 299 year: 2017 end-page: 315 ident: bib6 article-title: Deep learning for tomato diseases: Classification and symptoms visualization publication-title: Applied Artificial Intelligence contributor: fullname: Moussaoui – year: 2014 ident: bib18 article-title: Very deep convolutional networks for large-scale image recognition contributor: fullname: Zisserman – year: 2016 ident: bib19 article-title: Deep neural networks based recognition of plant diseases by leaf image classification publication-title: Computational Intelligence and Neuroscience contributor: fullname: Stefanovic – volume: 9 start-page: 6770 year: 2018 ident: bib1 article-title: A multiclass deep convolutional neural network classifier for detection of common rice plant anomalies publication-title: International Journal of Advanced Computer Science and Applications contributor: fullname: Park – volume: 7 year: 2016 ident: bib17 article-title: Using deep learning for image-based plant disease detection publication-title: Frontiers of Plant Science contributor: fullname: Salathé – year: 2017 ident: bib21 article-title: Automatic image-based plant disease severity estimation using deep learning publication-title: Computational Intelligence and Neuroscience contributor: fullname: Wang – volume: 267 start-page: 378 year: 2017 end-page: 384 ident: bib15 article-title: Identification of rice diseases using deep convolutional neural networks publication-title: Neurocomputing contributor: fullname: Zhang – volume: 10 start-page: 11 year: 2018 ident: bib14 article-title: Identification of apple leaf diseases based on deep convolutional neural networks publication-title: Symmetry contributor: fullname: Li – year: 2016 ident: bib16 article-title: Efficacy of bau-biofungicide on brown spot and bacterial leaf blight disease and vigour index of rice contributor: fullname: Ahmad – year: 2007 ident: bib2 article-title: Leaves recognition using back propagation neural network- advice for pest and disease control on crops publication-title: IndiaKisan. Net: Expert Advisory System contributor: fullname: Rao – volume: 145 start-page: 311 year: 2018 end-page: 318 ident: bib11 article-title: Deep learning models for plant disease detection and diagnosis publication-title: Computers and Electronics in Agriculture contributor: fullname: Ferentinos – volume: 3 start-page: 4168 year: 2015 end-page: 4173 ident: bib5 article-title: Artificial neural network assisted weather based plant disease forecasting system publication-title: International Journal on Recent and Innovation Trends in Computing and Communication contributor: fullname: Bhagawati – volume: 53 start-page: 607 year: 2002 end-page: 626 ident: bib8 article-title: Technical, allocative, cost and scale efficiencies in Bangladesh rice cultivation: A non-parametric approach publication-title: Journal of Agricultural Economics contributor: fullname: Thirtle – volume: 17 start-page: 2022 year: 2017 ident: bib12 article-title: A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition publication-title: Sensors contributor: fullname: Park – volume: 114 year: 2015 ident: bib13 article-title: Tea leaf diseases recognition using neural network ensemble publication-title: International Journal of Computer Application contributor: fullname: Alam – volume: 153 start-page: 46 year: 2018 end-page: 53 ident: bib3 article-title: Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification publication-title: Computers and Electronics in Agriculture contributor: fullname: Barbedo – volume: 107 start-page: 1426 year: 2017 end-page: 1432 ident: bib10 article-title: Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning publication-title: Phytopathology contributor: fullname: Gore – start-page: 19 year: 2015 ident: bib20 article-title: Going deeper with convolutions publication-title: Proceedings of the ieee conference on computer vision and pattern recognition contributor: fullname: Anguelov – volume: 114 issue: 17 year: 2015 ident: 10.1016/j.biosystemseng.2020.03.020_bib13 article-title: Tea leaf diseases recognition using neural network ensemble publication-title: International Journal of Computer Application contributor: fullname: Karmokar – volume: 31 start-page: 299 issue: 4 year: 2017 ident: 10.1016/j.biosystemseng.2020.03.020_bib6 article-title: Deep learning for tomato diseases: Classification and symptoms visualization publication-title: Applied Artificial Intelligence doi: 10.1080/08839514.2017.1315516 contributor: fullname: Brahimi – volume: 153 start-page: 46 year: 2018 ident: 10.1016/j.biosystemseng.2020.03.020_bib3 article-title: Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2018.08.013 contributor: fullname: Barbedo – year: 2016 ident: 10.1016/j.biosystemseng.2020.03.020_bib19 article-title: Deep neural networks based recognition of plant diseases by leaf image classification publication-title: Computational Intelligence and Neuroscience doi: 10.1155/2016/3289801 contributor: fullname: Sladojevic – year: 2002 ident: 10.1016/j.biosystemseng.2020.03.020_bib7 article-title: Rice in world trade, part ii. status of the world rice market contributor: fullname: Calpe – year: 2016 ident: 10.1016/j.biosystemseng.2020.03.020_bib16 contributor: fullname: Mahmud – volume: 267 start-page: 378 year: 2017 ident: 10.1016/j.biosystemseng.2020.03.020_bib15 article-title: Identification of rice diseases using deep convolutional neural networks publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.06.023 contributor: fullname: Lu – volume: 7 year: 2016 ident: 10.1016/j.biosystemseng.2020.03.020_bib17 article-title: Using deep learning for image-based plant disease detection publication-title: Frontiers of Plant Science doi: 10.3389/fpls.2016.01419 contributor: fullname: Mohanty – year: 2007 ident: 10.1016/j.biosystemseng.2020.03.020_bib2 article-title: Leaves recognition using back propagation neural network- advice for pest and disease control on crops publication-title: IndiaKisan. Net: Expert Advisory System contributor: fullname: Babu – volume: 3 start-page: 4168 issue: 6 year: 2015 ident: 10.1016/j.biosystemseng.2020.03.020_bib5 article-title: Artificial neural network assisted weather based plant disease forecasting system publication-title: International Journal on Recent and Innovation Trends in Computing and Communication contributor: fullname: Bhagawati – start-page: 19 year: 2015 ident: 10.1016/j.biosystemseng.2020.03.020_bib20 article-title: Going deeper with convolutions contributor: fullname: Szegedy – volume: 9 start-page: 6770 issue: 1 year: 2018 ident: 10.1016/j.biosystemseng.2020.03.020_bib1 article-title: A multiclass deep convolutional neural network classifier for detection of common rice plant anomalies publication-title: International Journal of Advanced Computer Science and Applications contributor: fullname: Atole – volume: 8 start-page: 1741 year: 2017 ident: 10.1016/j.biosystemseng.2020.03.020_bib9 article-title: X-fido: An effective application for detecting olive quick decline syndrome with deep learning and data fusion publication-title: Frontiers of Plant Science doi: 10.3389/fpls.2017.01741 contributor: fullname: Cruz – volume: 17 start-page: 2022 issue: 9 year: 2017 ident: 10.1016/j.biosystemseng.2020.03.020_bib12 article-title: A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition publication-title: Sensors doi: 10.3390/s17092022 contributor: fullname: Fuentes – volume: 107 start-page: 1426 issue: 11 year: 2017 ident: 10.1016/j.biosystemseng.2020.03.020_bib10 article-title: Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning publication-title: Phytopathology doi: 10.1094/PHYTO-11-16-0417-R contributor: fullname: DeChant – volume: 10 start-page: 11 issue: 1 year: 2018 ident: 10.1016/j.biosystemseng.2020.03.020_bib14 article-title: Identification of apple leaf diseases based on deep convolutional neural networks publication-title: Symmetry doi: 10.3390/sym10010011 contributor: fullname: Liu – volume: 180 start-page: 96 year: 2019 ident: 10.1016/j.biosystemseng.2020.03.020_bib4 article-title: Plant disease identification from individual lesions and spots using deep learning publication-title: Biosystems Engineering doi: 10.1016/j.biosystemseng.2019.02.002 contributor: fullname: Barbedo – year: 2014 ident: 10.1016/j.biosystemseng.2020.03.020_bib18 contributor: fullname: Simonyan – year: 2017 ident: 10.1016/j.biosystemseng.2020.03.020_bib21 article-title: Automatic image-based plant disease severity estimation using deep learning publication-title: Computational Intelligence and Neuroscience doi: 10.1155/2017/2917536 contributor: fullname: Wang – volume: 145 start-page: 311 year: 2018 ident: 10.1016/j.biosystemseng.2020.03.020_bib11 article-title: Deep learning models for plant disease detection and diagnosis publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2018.01.009 contributor: fullname: Ferentinos – volume: 53 start-page: 607 issue: 3 year: 2002 ident: 10.1016/j.biosystemseng.2020.03.020_bib8 article-title: Technical, allocative, cost and scale efficiencies in Bangladesh rice cultivation: A non-parametric approach publication-title: Journal of Agricultural Economics doi: 10.1111/j.1477-9552.2002.tb00040.x contributor: fullname: Coelli |
SSID | ssj0015746 |
Score | 2.692531 |
Snippet | Accurate and timely detection of diseases and pests in rice plants can help farmers in applying timely treatment on the plants and thereby can reduce the... |
SourceID | crossref elsevier |
SourceType | Aggregation Database Publisher |
StartPage | 112 |
SubjectTerms | Convolutional neural network Dataset Memory efficient Pest Rice disease Two stage training |
Title | Identification and recognition of rice diseases and pests using convolutional neural networks |
URI | https://dx.doi.org/10.1016/j.biosystemseng.2020.03.020 |
Volume | 194 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NS8MwFH_MDUQP4ifOj1HQa12Tpk17EcZwTMVddLCLlKRJxzx0Y51X_3Zf2nRu4EHwFFLStLwm76Pv934BuOXCTzPhx65SVLhMSeFKypUrBRUkY-iQlLVVL6NwOGZPk2DSgH5dC2NglVb3Vzq91Nb2StdKs7uYzbqvuFc5ugsmlWYcCYzbW2iOGGtCq_f4PBytkwkBr4qMcLxrbtiFmx-Yl5zNK87kQudTjBepV5KemvO_fzNUG8ZncAgH1mt0etWLHUFD58ew35suLXOGxt4Gs-AJvFcFuJn9I-eIXDlrrBD255lj2IQcm58pygELtBCFY5DwU8eg0e2qxAcb1suyKTHjxSmMBw9v_aFrT1JwU5_ylRuEUvMwZIKHqBipwCDM0xFu5oCLiIhIEk1opnylVSwjT3CZESYF-hqRTFGA_hk083muz8GhJE25Immkfc6YJpFgsVLMFzGXAZVhG1gttmRREWYkNZLsI9mSdmKknXh-gk0b7msRJ1vfP0HV_pcJLv47wSXsmV4FA7uC5mr5qa_R4VjJDuzcfZGOXVbfzaDZ-g |
link.rule.ids | 315,786,790,4521,24144,27955,27956,45618,45712 |
linkProvider | Elsevier |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3Na8IwFH-Iwj4OY5_MfRa2a9GkaVMvA5GJzo_LFLyMkDSpuEMVdf__XtroFHYY7BTSpkl5TV7e6_u9XwCeuQySVAYNX2sqfaaV9BXl2leSSpIyNEjy3KrBMOqM2dsknJSgtcmFsbBKp_sLnZ5ra3el5qRZW8xmtXdcqxzNBRtKs4YE-u0VFnJCy1Bpdnud4TaYEPIiyQjb-_aBA3j6gXmp2bzgTF6ZbIr-Iq3npKf2_O_fNqqdzad9CifOavSaxYudQclk53DcnC4dc4bB2g6z4AV8FAm4qfsj58lMe1usENbnqWfZhDwXn1nlDRa4Q6w8i4SfehaN7mYlDmxZL_Mix4yvLmHcfh21Or47ScFPAsrXfhgpw6OISR6hYqQSnbC6iXExh1zGRMaKGEJTHWijGyquS65SwpREWyNWCQowuIJyNs_MNXiUJAnXJIlNwBkzJJasoTULZIOrkKqoCmwjNrEoCDPEBkn2KfakLay0RT0QWFThZSNisff9Bar2v3Rw898OHuGwMxr0Rb877N3Ckb1TQMLuoLxefpl7ND7W6sFNrm8Mkdvq |
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=Identification+and+recognition+of+rice+diseases+and+pests+using+convolutional+neural+networks&rft.jtitle=Biosystems+engineering&rft.au=Rahman%2C+Chowdhury+R.&rft.au=Arko%2C+Preetom+S.&rft.au=Ali%2C+Mohammed+E.&rft.au=Iqbal+Khan%2C+Mohammad+A.&rft.date=2020-06-01&rft.pub=Elsevier+Ltd&rft.issn=1537-5110&rft.eissn=1537-5129&rft.volume=194&rft.spage=112&rft.epage=120&rft_id=info:doi/10.1016%2Fj.biosystemseng.2020.03.020&rft.externalDocID=S1537511020300830 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1537-5110&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1537-5110&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1537-5110&client=summon |