Broccoli seedling pest damage degree evaluation based on machine learning combined with color and shape features
The degree of pest damage evaluation on corps in the field environment is very important for precision spraying pesticides. In this paper, we proposed an image processing method to identify the wormholes in the image of broccoli seedlings, and then to evaluate the damage of the broccoli seedlings by...
Saved in:
| Published in | Information processing in agriculture Vol. 8; no. 4; pp. 505 - 514 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.12.2021
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | The degree of pest damage evaluation on corps in the field environment is very important for precision spraying pesticides. In this paper, we proposed an image processing method to identify the wormholes in the image of broccoli seedlings, and then to evaluate the damage of the broccoli seedlings by pests. The broccoli seedlings were taken as the research object. The ratio of wormhole areas to broccoli seedling leaves areas (Rw) was used to describe the pest damage degree. An algorithm was developed to calculate the ratio of wormhole areas to broccoli seedling leaves areas. Firstly, broccoli seedling leaves were segmented from the background and the area of the leaves was obtained. There were some holes in segmentation results due to pest damage and other reasons. Then, a classifier based on machine learning was developed to classify the wormholes and other holes. Twenty-four features, including color features and shape features of the holes, were used to develop classifiers. After identifying wormholes from images, the area of the wormholes was obtained and the degree of pest damage to broccoli seedling was calculated. The determination coefficient (R2) between the algorithm calculated pest damage degree and manually labeled pest damage degree was 0.85. The root-mean-square error (δ) was 0.02. Results demonstrated that the color and shape were able to effectively segment wormholes from leaves of broccoli seedlings and evaluate the degree of pest damage. This method could provide references for precision spraying pesticides. |
|---|---|
| AbstractList | The degree of pest damage evaluation on corps in the field environment is very important for precision spraying pesticides. In this paper, we proposed an image processing method to identify the wormholes in the image of broccoli seedlings, and then to evaluate the damage of the broccoli seedlings by pests. The broccoli seedlings were taken as the research object. The ratio of wormhole areas to broccoli seedling leaves areas (Rw) was used to describe the pest damage degree. An algorithm was developed to calculate the ratio of wormhole areas to broccoli seedling leaves areas. Firstly, broccoli seedling leaves were segmented from the background and the area of the leaves was obtained. There were some holes in segmentation results due to pest damage and other reasons. Then, a classifier based on machine learning was developed to classify the wormholes and other holes. Twenty-four features, including color features and shape features of the holes, were used to develop classifiers. After identifying wormholes from images, the area of the wormholes was obtained and the degree of pest damage to broccoli seedling was calculated. The determination coefficient (R2) between the algorithm calculated pest damage degree and manually labeled pest damage degree was 0.85. The root-mean-square error (δ) was 0.02. Results demonstrated that the color and shape were able to effectively segment wormholes from leaves of broccoli seedlings and evaluate the degree of pest damage. This method could provide references for precision spraying pesticides. |
| Author | Li, Wei Zou, Kunlin Ge, Luzhen Zhang, Chunlong Zhou, Hang |
| Author_xml | – sequence: 1 givenname: Kunlin surname: Zou fullname: Zou, Kunlin – sequence: 2 givenname: Luzhen surname: Ge fullname: Ge, Luzhen – sequence: 3 givenname: Hang surname: Zhou fullname: Zhou, Hang – sequence: 4 givenname: Chunlong surname: Zhang fullname: Zhang, Chunlong email: zcl1515@cau.edu.cn – sequence: 5 givenname: Wei surname: Li fullname: Li, Wei |
| BookMark | eNp9kMtqwzAQRUVJoWmaH-hKP2BXkpXIhm7a0BcEumnXYiyNEwVbMpKT0r-vTbroqqu5c-EMw7kmMx88EnLLWc4ZX98dcud7yAUTYyFyxooLMheCy6zgqpj9yVdkmdKBMcbVupCMzUn_GIMxoXU0IdrW-R3tMQ3UQgc7pBZ3EZHiCdojDC54WkNCS8fQgdk7j7RFiH7iTOjqsbD0yw37cWtDpOAtTXvokTYIwzFiuiGXDbQJl79zQT6fnz42r9n2_eVt87DNTCHlkJW2XllhQGJZIrKVqo1EKKwEgKaRHFUFSqCQ68oWZcmaWgkjlG1KVVUWTbEg4nzXxJBSxEb30XUQvzVnetKmD3rSpidtmgs9ahuh-zOE42cnh1En49AbtC6iGbQN7j_8B9JKesE |
| CitedBy_id | crossref_primary_10_15832_ankutbd_1308406 crossref_primary_10_1007_s42464_022_00155_6 |
| Cites_doi | 10.1002/fes3.108 10.1007/s40626-016-0072-8 10.1016/S0168-1923(99)00091-X 10.1109/ACCESS.2019.2954587 10.1016/j.jksuci.2015.12.004 10.1016/j.compag.2018.11.040 10.3844/jcssp.2007.430.435 10.17582/journal.pjz/20180721120723 10.13031/2013.27838 10.1109/ACCESS.2019.2891749 10.1093/jee/70.5.659 10.1016/j.compind.2018.03.001 10.13031/2013.17244 10.1186/s42483-020-00049-8 10.1016/j.inpa.2019.02.001 10.1016/j.compag.2008.03.009 10.1016/j.inpa.2020.09.004 10.1007/s11042-018-6748-0 10.1007/978-3-319-24574-4_28 10.1016/j.biosystemseng.2008.09.030 10.1016/j.inpa.2018.05.002 10.1007/s00779-019-01268-3 10.1017/S1742758417000200 10.1094/PHP-RS-15-0024 10.3390/agronomy10070972 10.1111/ijfs.13256 10.1016/j.inpa.2019.07.003 10.1016/j.compag.2016.09.007 10.1109/ACCESS.2019.2942158 10.1016/S0034-4257(00)00197-8 10.1007/s10586-018-1844-5 10.1186/1746-4811-7-28 |
| ContentType | Journal Article |
| Copyright | 2021 China Agricultural University |
| Copyright_xml | – notice: 2021 China Agricultural University |
| DBID | 6I. AAFTH AAYXX CITATION |
| DOI | 10.1016/j.inpa.2020.12.003 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Agriculture |
| EISSN | 2214-3173 |
| EndPage | 514 |
| ExternalDocumentID | 10_1016_j_inpa_2020_12_003 S2214317320302328 |
| GroupedDBID | 0R~ 0SF 4.4 457 5VS 6I. AACTN AAEDT AAEDW AAFTH AAIKJ AALRI AAXUO ABMAC ACGFS ADBBV ADEZE AEXQZ AFTJW AGHFR AITUG ALMA_UNASSIGNED_HOLDINGS AMRAJ BCNDV EBS EJD FDB GROUPED_DOAJ HZ~ IPNFZ IXB KQ8 M41 NCXOZ O9- OK1 RIG ROL SSZ AAHBH AAYXX ADVLN AKRWK CITATION |
| ID | FETCH-LOGICAL-c344t-8db5d2ca4e88ee057bc4ea3d4aaaff41e79a72e2469d3880fb72c27df8799dec3 |
| IEDL.DBID | IXB |
| ISSN | 2214-3173 |
| IngestDate | Fri Aug 23 00:44:13 EDT 2024 Wed May 17 00:08:11 EDT 2023 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | Shape features Color features Wormhole segmentation Machine learning Pest damage evaluation |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c344t-8db5d2ca4e88ee057bc4ea3d4aaaff41e79a72e2469d3880fb72c27df8799dec3 |
| OpenAccessLink | https://www.sciencedirect.com/science/article/pii/S2214317320302328 |
| PageCount | 10 |
| ParticipantIDs | crossref_primary_10_1016_j_inpa_2020_12_003 elsevier_sciencedirect_doi_10_1016_j_inpa_2020_12_003 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-12-01 |
| PublicationDateYYYYMMDD | 2021-12-01 |
| PublicationDate_xml | – month: 12 year: 2021 text: 2021-12-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | Information processing in agriculture |
| PublicationYear | 2021 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | da Silva LA, Bressan PO, Gonçalves DN, Freitas DM, Machado BB, Gonçalves WNJC. Agriculture E i. Estimating soybean leaf defoliation using convolutional neural networks and synthetic images. 2019;156:360–368. Thaseen IS, Kumar C A J J o K S U-C, Sciences I. Intrusion detection model using fusion of chi-square feature selection and multi class SVM. 2017; 29(4):462–472. Camargo A, Smith JJBe. An image-processing based algorithm to automatically identify plant disease visual symptoms. 2009;102(1):9–21. Golhani K, Balasundram SK, Vadamalai G, Pradhan BJIPiA. A review of neural networks in plant disease detection using hyperspectral data. 2018;5(3):354–371. Sabzi S, Abbaspour-Gilandeh Y, García-Mateos GJCiI. A fast and accurate expert system for weed identification in potato crops using metaheuristic algorithms. 2018;98:80–89. Jiang D, Zheng Z, Li G, Sun Y, Kong J, Jiang G et al. Gesture recognition based on binocular vision. 2019;22(6):13261–13271. Tripathi MK, Maktedar DDJI PiA. A role of computer vision in fruits and vegetables among various horticulture products of agriculture fields: a survey. 2020;7(2):183–203. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer. 2015. p. 234–241. Drury B, Fernandes R, Moura M-F, de Andrade Lopes AJIPiA. A survey of semantic web technology for agriculture. 2019;6(4):487–501. Kogan M, Turnipseed S, Shepard M, De Oliveira E, Borgo A JJoEE. Pilot insect pest management program for soybean in southern Brazil. 1977;70(5):659–663. Meyer GE, Neto JCJC, agriculture e i. Verification of color vegetation indices for automated crop imaging applications. 2008; 63(2):282–293. Moh'd A Mesleh AJJoCS. Chi square feature extraction based svms arabic language text categorization system. 2007;3(6):430–435. Carvalho FPJF, Security E. Pesticides, environment, and food safety. 2017;6(2):48–60. Woebbecke DM, Meyer GE, Von Bargen K, Mortensen DAJTotA. Color indices for weed identification under various soil, residue, and lighting conditions. 1995;38(1):259–269. Aires A, Carvalho R, Saavedra M J J I j o f s, technology. Reuse potential of vegetable wastes (broccoli, green bean and tomato) for the recovery of antioxidant phenolic acids and flavonoids. 2017; 52(1):98–107. Bock CH, Barbedo JG, Del Ponte EM, Bohnenkamp D, Mahlein A-KJPR. From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy. 2020;2:1–30. Zou K, Ge L, Zhang C, Yuan T, Li WJIA. Broccoli seedling segmentation based on support vector machine combined with color texture features 2019;7:168565–168574. Jiang D, Li G, Sun Y, Kong J, Tao B J M T, Applications. Gesture recognition based on skeletonization algorithm and CNN with ASL database. 2019; 78(21):29953–29970. Machado BB, Orue JP, Arruda MS, Santos CV, Sarath DS, Goncalves WN et al. BioLeaf: A professional mobile application to measure foliar damage caused by insect herbivory. 2016; 129:44–55. Zhang C, Zou K, Pan YJA. A method of apple image segmentation based on color-texture fusion feature and machine learning. 2020;10(7):972. Palumbo JC, Carrière YJPHP. Association between Bagrada hilaris density and feeding damage in broccoli: implications for pest management. 2015;16(4):158–162. Labou B, Brévault T, Sylla S, Diatte M, Bordat D, Diarra KJIJoTIS. Spatial and temporal incidence of insect pests in farmers’ cabbage fields in Senegal. 2017;37(4):225–233. Kvet J, Marshall JJSZ. Plant photosynthetic production. Assessment of leaf area and other assimilating plant surfaces; 1971. Ullah MI, Arshad M, Ali S, Abdullah A, Khalid S, Aatif HM et al. Using smartphone application to estimate the defoliation caused by insect herbivory in various crops. 2020; 52(3). Broge NH, Leblanc EJRsoe. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. 2001;76(2):156–172. Li N, Zhang X, Zhang C, Guo H, Sun Z, Wu XJIA. Real-time crop recognition in transplanted fields with prominent weed growth: a visual-attention-based approach. 2019;7:185310–185321. Nazaré-Jr A, Menotti D, Neves J, Sediyama T. Automatic detection of the damaged leaf area in digital images of soybean. In: 17th International Conference on Systems, Signals and Image Processing. IEEE Computer Society. 2010. p. 499-503. Jiang D, Li G, Sun Y, Kong J, Tao B, Chen DJP, Computing U. Grip strength forecast and rehabilitative guidance based on adaptive neural fuzzy inference system using sEMG. 2019:1–10. Li G, Jiang D, Zhou Y, Jiang G, Kong J, Manogaran GJIA. Human lesion detection method based on image information and brain signal. 2019;7:11533–11542. Golzarian MR, Frick RAJPM. Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysis. 2011;7(1):28. Barclay H, Trofymow J, Leach R J A, Meteorology F. Assessing bias from boles in calculating leaf area index in immature Douglas-fir with the LI-COR canopy analyzer. 2000;100(2-3):255–260. Kambli A, McGarvey RGJIPiA. Network design for local agriculture using robust optimization; 2020. Meyer G, Mehta T, Kocher M, Mortensen D, Samal AJTotA. Textural imaging and discriminant analysis for distinguishing weeds for spot spraying. 1998; 41(4):1189. Shahbandeh M. Global production of vegetables in 2017. link: https://www.statista.com/statistics/264065/global-production-of-vegetables-by-type/. 2019. dos Santos JCC, Costa RN, Silva DMR, de Souza AA, Moura FdBP, da Silva Junior JM, et al. Physiology E P. use of allometric models to estimate leaf area in Hymenaeacourbaril L. 2016;28(4):357–369. Buitinck L, Louppe G, Blondel M, Pedregosa F, Mueller A, Grisel O, et al. API design for machine learning software: experiences from the scikit-learn project. 2013. 10.1016/j.inpa.2020.12.003_b0040 10.1016/j.inpa.2020.12.003_b0160 10.1016/j.inpa.2020.12.003_b0060 10.1016/j.inpa.2020.12.003_b0180 10.1016/j.inpa.2020.12.003_b0080 10.1016/j.inpa.2020.12.003_b0015 10.1016/j.inpa.2020.12.003_b0135 10.1016/j.inpa.2020.12.003_b0035 10.1016/j.inpa.2020.12.003_b0155 10.1016/j.inpa.2020.12.003_b0055 10.1016/j.inpa.2020.12.003_b0110 10.1016/j.inpa.2020.12.003_b0010 10.1016/j.inpa.2020.12.003_b0175 10.1016/j.inpa.2020.12.003_b0075 10.1016/j.inpa.2020.12.003_b0130 10.1016/j.inpa.2020.12.003_b0030 10.1016/j.inpa.2020.12.003_b0115 10.1016/j.inpa.2020.12.003_b0095 10.1016/j.inpa.2020.12.003_b0150 10.1016/j.inpa.2020.12.003_b0050 10.1016/j.inpa.2020.12.003_b0170 10.1016/j.inpa.2020.12.003_b0070 10.1016/j.inpa.2020.12.003_b0090 10.1016/j.inpa.2020.12.003_b0125 10.1016/j.inpa.2020.12.003_b0025 10.1016/j.inpa.2020.12.003_b0145 10.1016/j.inpa.2020.12.003_b0045 10.1016/j.inpa.2020.12.003_b0100 10.1016/j.inpa.2020.12.003_b0165 10.1016/j.inpa.2020.12.003_b0065 10.1016/j.inpa.2020.12.003_b0120 10.1016/j.inpa.2020.12.003_b0020 10.1016/j.inpa.2020.12.003_b0085 10.1016/j.inpa.2020.12.003_b0140 10.1016/j.inpa.2020.12.003_b0105 10.1016/j.inpa.2020.12.003_b0005 |
| References_xml | – ident: 10.1016/j.inpa.2020.12.003_b0025 doi: 10.1002/fes3.108 – ident: 10.1016/j.inpa.2020.12.003_b0045 doi: 10.1007/s40626-016-0072-8 – ident: 10.1016/j.inpa.2020.12.003_b0040 doi: 10.1016/S0168-1923(99)00091-X – ident: 10.1016/j.inpa.2020.12.003_b0125 doi: 10.1109/ACCESS.2019.2954587 – ident: 10.1016/j.inpa.2020.12.003_b0165 doi: 10.1016/j.jksuci.2015.12.004 – ident: 10.1016/j.inpa.2020.12.003_b0060 doi: 10.1016/j.compag.2018.11.040 – ident: 10.1016/j.inpa.2020.12.003_b0130 doi: 10.3844/jcssp.2007.430.435 – ident: 10.1016/j.inpa.2020.12.003_b0065 doi: 10.17582/journal.pjz/20180721120723 – ident: 10.1016/j.inpa.2020.12.003_b0155 doi: 10.13031/2013.27838 – ident: 10.1016/j.inpa.2020.12.003_b0100 doi: 10.1109/ACCESS.2019.2891749 – ident: 10.1016/j.inpa.2020.12.003_b0030 doi: 10.1093/jee/70.5.659 – ident: 10.1016/j.inpa.2020.12.003_b0110 doi: 10.1016/j.compind.2018.03.001 – ident: 10.1016/j.inpa.2020.12.003_b0145 doi: 10.13031/2013.17244 – ident: 10.1016/j.inpa.2020.12.003_b0090 doi: 10.1186/s42483-020-00049-8 – ident: 10.1016/j.inpa.2020.12.003_b0035 – ident: 10.1016/j.inpa.2020.12.003_b0075 doi: 10.1016/j.inpa.2019.02.001 – ident: 10.1016/j.inpa.2020.12.003_b0160 doi: 10.1016/j.compag.2008.03.009 – ident: 10.1016/j.inpa.2020.12.003_b0010 – ident: 10.1016/j.inpa.2020.12.003_b0105 doi: 10.1016/j.inpa.2020.09.004 – ident: 10.1016/j.inpa.2020.12.003_b0180 doi: 10.1007/s11042-018-6748-0 – ident: 10.1016/j.inpa.2020.12.003_b0175 doi: 10.1007/978-3-319-24574-4_28 – ident: 10.1016/j.inpa.2020.12.003_b0115 doi: 10.1016/j.biosystemseng.2008.09.030 – ident: 10.1016/j.inpa.2020.12.003_b0085 doi: 10.1016/j.inpa.2018.05.002 – ident: 10.1016/j.inpa.2020.12.003_b0095 doi: 10.1007/s00779-019-01268-3 – ident: 10.1016/j.inpa.2020.12.003_b0020 doi: 10.1017/S1742758417000200 – ident: 10.1016/j.inpa.2020.12.003_b0015 doi: 10.1094/PHP-RS-15-0024 – ident: 10.1016/j.inpa.2020.12.003_b0170 doi: 10.3390/agronomy10070972 – ident: 10.1016/j.inpa.2020.12.003_b0005 doi: 10.1111/ijfs.13256 – ident: 10.1016/j.inpa.2020.12.003_b0135 – ident: 10.1016/j.inpa.2020.12.003_b0070 doi: 10.1016/j.inpa.2019.07.003 – ident: 10.1016/j.inpa.2020.12.003_b0050 doi: 10.1016/j.compag.2016.09.007 – ident: 10.1016/j.inpa.2020.12.003_b0055 – ident: 10.1016/j.inpa.2020.12.003_b0120 doi: 10.1109/ACCESS.2019.2942158 – ident: 10.1016/j.inpa.2020.12.003_b0140 doi: 10.1016/S0034-4257(00)00197-8 – ident: 10.1016/j.inpa.2020.12.003_b0080 doi: 10.1007/s10586-018-1844-5 – ident: 10.1016/j.inpa.2020.12.003_b0150 doi: 10.1186/1746-4811-7-28 |
| SSID | ssj0001763400 |
| Score | 2.2556684 |
| Snippet | The degree of pest damage evaluation on corps in the field environment is very important for precision spraying pesticides. In this paper, we proposed an image... |
| SourceID | crossref elsevier |
| SourceType | Aggregation Database Publisher |
| StartPage | 505 |
| SubjectTerms | Color features Machine learning Pest damage evaluation Shape features Wormhole segmentation |
| Title | Broccoli seedling pest damage degree evaluation based on machine learning combined with color and shape features |
| URI | https://dx.doi.org/10.1016/j.inpa.2020.12.003 |
| Volume | 8 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV07T8MwELaqssCAeIryqDywoaip49TJWCqqiqEMUCliiZz4XILUELXl_3PnJCowMLBFSU6KPjv3sL77jrFbYYNIgK89I0fWk8Y3HpZgysPUWWUmC0VmnNrnfDRbyMckTDps0vbCEK2y8f21T3feurkzaNAcVEUxeBZiSNEvED4NvhHU8Eu0dGriS-535yz4A0nXiULve2TQ9M7UNK-irEh-SPjuVLCdnfU7Pn2LOdMjdtgki3xcf88x60B5wg7Gy3UjmAGnrMI6OsfFLPgG4xD1lvMKHT03eoWeghvAehr4TtObU9gyHC9WjkYJvJkbseQIAZbJ-JDOZjmpWa-5Lg3fvOkKuAUnAbo5Y4vpw8tk5jVTFLw8kHLrRYi4EbmWEEUAmJ5luQQdGKm1tlYOQcVaCRBYJxtShrGZErlQxkYqjg3kwTnrlh8lXDAemREahBaCQEkb5LGkGdUqw6InijBT6bG7Fru0qsUy0pZF9p4S0ikhnQ4FSZL2WNjCm_5Y8hS9-R92l_-0u2L7gggpjotyzbrb9SfcYEaxzfquEu-7jdNne_NJ8vT6BeMzzPY |
| link.rule.ids | 315,783,787,867,3514,27937,27938,45887 |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT8JAEN4gHtSD8RnxuQdvpgG2C7s9ItGAIhch4dZsu7NYE2oD-P-d6SOoBw_emjaTNN-2M_NNZr5h7FY4XwtoGc_KrvOkbVkPKZjyMHVWkY06IrK52ue4O5jKp1lnVmP9ahaG2ipL31_49Nxbl3eaJZrNLEmar0K0Kfr5okWLb4TeYts0dkkMbDi73xRa8A-S-SgKGXhkUQ7PFH1eSZqR_pBo5WXBannW7wD1Leg8HrD9MlvkveKFDlkN0iO215svS8UMOGYZEukYTzPhKwxENFzOM_T03JoFugpuAQk18I2oN6e4ZTleLPI-SuDl4og5RwyQJ-NDKs5ykrNecpNavnozGXAHuQbo6oRNHx8m_YFXrlHwYl_KtacRcitiI0FrAMzPoliC8a00xjgn26ACowQIJMqWpGFcpEQslHVaBYGF2D9l9fQjhTPGte2iQceB7yvp_DiQtKRaRch6tMZUpcHuKuzCrFDLCKs2sveQkA4J6bAtSJO0wToVvOGPMw_Rnf9hd_5Puxu2M5i8jMLRcPx8wXYFdafkjSmXrL5efsIVphfr6Dr_fL4AHqvNeg |
| 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=Broccoli+seedling+pest+damage+degree+evaluation+based+on+machine+learning+combined+with+color+and+shape+features&rft.jtitle=Information+processing+in+agriculture&rft.au=Zou%2C+Kunlin&rft.au=Ge%2C+Luzhen&rft.au=Zhou%2C+Hang&rft.au=Zhang%2C+Chunlong&rft.date=2021-12-01&rft.issn=2214-3173&rft.eissn=2214-3173&rft.volume=8&rft.issue=4&rft.spage=505&rft.epage=514&rft_id=info:doi/10.1016%2Fj.inpa.2020.12.003&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_inpa_2020_12_003 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2214-3173&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2214-3173&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2214-3173&client=summon |