{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T21:07:43Z","timestamp":1783026463148,"version":"3.54.6"},"reference-count":79,"publisher":"Springer Science and Business Media LLC","issue":"27","license":[{"start":{"date-parts":[[2023,4,11]],"date-time":"2023-04-11T00:00:00Z","timestamp":1681171200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,11]],"date-time":"2023-04-11T00:00:00Z","timestamp":1681171200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s11042-023-15221-3","type":"journal-article","created":{"date-parts":[[2023,4,11]],"date-time":"2023-04-11T10:02:52Z","timestamp":1681207372000},"page":"42277-42310","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":114,"title":["Survey on crop pest detection using deep learning and machine learning approaches"],"prefix":"10.1007","volume":"82","author":[{"given":"M.","family":"Chithambarathanu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. K.","family":"Jeyakumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,4,11]]},"reference":[{"key":"15221_CR1","doi-asserted-by":"crossref","first-page":"140565","DOI":"10.1109\/ACCESS.2021.3119655","volume":"9","author":"M Ahmad","year":"2021","unstructured":"Ahmad M, Abdullah M, Moon H, Han D (2021) Plant disease detection in imbalanced datasets using efficient convolutional neural networks with stepwise transfer learning. IEEE Access 9:140565\u2013140580","journal-title":"IEEE Access"},{"key":"15221_CR2","doi-asserted-by":"crossref","first-page":"171686","DOI":"10.1109\/ACCESS.2020.3025325","volume":"8","author":"Y Ai","year":"2020","unstructured":"Ai Y, Sun C, Tie J, Cai X (2020) Research on recognition model of crop diseases and insect pests based on deep learning in harsh environments. IEEE Access 8:171686\u2013171693","journal-title":"IEEE Access"},{"key":"15221_CR3","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.compag.2017.04.008","volume":"138","author":"H Ali","year":"2017","unstructured":"Ali H, Lali MI, Nawaz MZ, Sharif M, Saleem BA (2017) Symptom based automated detection of citrus diseases using color histogram and textural descriptors. Comput Electron Agric 138:92\u2013104","journal-title":"Comput Electron Agric"},{"key":"15221_CR4","doi-asserted-by":"crossref","unstructured":"Alok N, Krishan K, Chauhan P (2021) Deep learning-based image classifier for malaria cell detection. Machine learning for healthcare applications, pp 187\u2013197","DOI":"10.1002\/9781119792611.ch12"},{"key":"15221_CR5","doi-asserted-by":"crossref","first-page":"105221","DOI":"10.1016\/j.compag.2020.105221","volume":"169","author":"AD Amirruddin","year":"2020","unstructured":"Amirruddin AD, Muharam FM, Ismail MH, Ismail MF, Tan NP, Karam DS (2020) Hyperspectral remote sensing for assessment of chlorophyll sufficiency levels in mature oil palm (Elaeis guineensis) based on frond numbers: analysis of decision tree and random forest. Comput Electron Agric 169:105221","journal-title":"Comput Electron Agric"},{"key":"15221_CR6","doi-asserted-by":"crossref","first-page":"105809","DOI":"10.1016\/j.compag.2020.105809","volume":"179","author":"E Ayan","year":"2020","unstructured":"Ayan E, Erbay H, Var\u00e7\u0131n F (2020) Crop pest classification with a genetic algorithm-based weighted ensemble of deep convolutional neural networks. Comput Electron Agric 179:105809","journal-title":"Comput Electron Agric"},{"key":"15221_CR7","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.biosystemseng.2019.02.002","volume":"180","author":"JGA Barbedo","year":"2019","unstructured":"Barbedo JGA (2019) Plant disease identification from individual lesions and spots using deep learning. Biosyst Eng 180:96\u2013107","journal-title":"Biosyst Eng"},{"key":"15221_CR8","doi-asserted-by":"crossref","first-page":"105661","DOI":"10.1016\/j.compag.2020.105661","volume":"177","author":"U Barman","year":"2020","unstructured":"Barman U, Choudhury RD, Sahu D, Barman GG (2020) Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease. Comput Electron Agric 177:105661","journal-title":"Comput Electron Agric"},{"key":"15221_CR9","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.compag.2018.07.002","volume":"152","author":"D Chen","year":"2018","unstructured":"Chen D, Shi Y, Huang W, Zhang J, Wu K (2018) Mapping wheat rust based on high spatial resolution satellite imagery. Comput Electron Agric 152:109\u2013116","journal-title":"Comput Electron Agric"},{"key":"15221_CR10","doi-asserted-by":"crossref","first-page":"180750","DOI":"10.1109\/ACCESS.2020.3024891","volume":"8","author":"CJ Chen","year":"2020","unstructured":"Chen CJ, Huang YY, Li YS, Chang CY, Huang YM (2020) An AIoT based smart agricultural system for pests detection. IEEE Access 8:180750\u2013180761","journal-title":"IEEE Access"},{"key":"15221_CR11","doi-asserted-by":"crossref","first-page":"105612","DOI":"10.1016\/j.compag.2020.105612","volume":"176","author":"P Chen","year":"2020","unstructured":"Chen P, Xiao Q, Zhang J, Xie C, Wang B (2020) Occurrence prediction of cotton pests and diseases by bidirectional long short-term memory networks with climate and atmosphere circulation. Comput Electron Agric 176:105612","journal-title":"Comput Electron Agric"},{"key":"15221_CR12","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.compag.2017.08.005","volume":"141","author":"X Cheng","year":"2017","unstructured":"Cheng X, Zhang Y, Chen Y, Wu Y, Yue Y (2017) Pest identification via deep residual learning in complex background. Comput Electron Agric 141:351\u2013356","journal-title":"Comput Electron Agric"},{"issue":"7","key":"15221_CR13","doi-asserted-by":"crossref","first-page":"4993","DOI":"10.1007\/s10462-020-09813-w","volume":"53","author":"R Cristin","year":"2020","unstructured":"Cristin R, Kumar BS, Priya C, Karthick K (2020) Deep neural network-based Rider-Cuckoo search algorithm for plant disease detection. Artif Intell Rev 53(7):4993\u20135018","journal-title":"Artif Intell Rev"},{"key":"15221_CR14","first-page":"101983","volume":"85","author":"J da Rocha Miranda","year":"2020","unstructured":"da Rocha Miranda J, de Carvalho Alves M, Pozza EA, Neto HS (2020) Detection of coffee berry necrosis by digital image processing of landsat 8 oli satellite imagery. Int J Appl Earth Obs Geoinf 85:101983","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"15221_CR15","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.biosystemseng.2018.02.008","volume":"169","author":"L Deng","year":"2018","unstructured":"Deng L, Wang Y, Han Z, Yu R (2018) Research on insect pest image detection and recognition based on bio-inspired methods. Biosyst Eng 169:139\u2013148","journal-title":"Biosyst Eng"},{"key":"15221_CR16","doi-asserted-by":"crossref","first-page":"105006","DOI":"10.1016\/j.compag.2019.105006","volume":"167","author":"X Deng","year":"2019","unstructured":"Deng X, Huang Z, Zheng Z, Lan Y, Dai F (2019) Field detection and classification of citrus huanglongbing based on hyperspectral reflectance. Comput Electron Agric 167:105006","journal-title":"Comput Electron Agric"},{"key":"15221_CR17","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1016\/j.measurement.2018.12.027","volume":"135","author":"G Dhingra","year":"2019","unstructured":"Dhingra G, Kumar V, Joshi HD (2019) A novel computer vision based neutrosophic approach for leaf disease identification and classification. Measurement 135:782\u2013794","journal-title":"Measurement"},{"issue":"4","key":"15221_CR18","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1109\/JIOT.2016.2517405","volume":"3","author":"M Dong","year":"2016","unstructured":"Dong M, Ota K, Liu A (2016) Reliable and energy-efficient data collection for large-scale wireless sensor networks. IEEE Internet Things J 3(4):511\u2013519","journal-title":"IEEE Internet Things J"},{"key":"15221_CR19","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.compag.2017.03.016","volume":"137","author":"MA Ebrahimi","year":"2017","unstructured":"Ebrahimi MA, Khoshtaghaza MH, Minaei S, Jamshidi B (2017) Vision-based pest detection based on SVM classification method. Comput Electron Agric 137:52\u201358","journal-title":"Comput Electron Agric"},{"key":"15221_CR20","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.compag.2018.01.009","volume":"145","author":"KP Ferentinos","year":"2018","unstructured":"Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311\u2013318","journal-title":"Comput Electron Agric"},{"key":"15221_CR21","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.biosystemseng.2018.03.006","volume":"170","author":"J Gao","year":"2018","unstructured":"Gao J, Nuyttens D, Lootens P, He Y, Pieters JG (2018) Recognising weeds in a maize crop using a random forest machine-learning algorithm and near-infrared snapshot mosaic hyperspectral imagery. Biosyst Eng 170:39\u201350","journal-title":"Biosyst Eng"},{"key":"15221_CR22","doi-asserted-by":"crossref","first-page":"5390","DOI":"10.1109\/ACCESS.2022.3141371","volume":"10","author":"SM Hassan","year":"2022","unstructured":"Hassan SM, Maji AK (2022) Plant disease identification using a novel convolutional neural network. IEEE Access 10:5390\u20135401","journal-title":"IEEE Access"},{"key":"15221_CR23","first-page":"100154","volume":"5","author":"C Hou","year":"2021","unstructured":"Hou C, Zhuang J, Tang Y, He Y, Miao A, Huang H, Luo S (2021) Recognition of early blight and late blight diseases on potato leaves based on graph cut segmentation. J Agric Food Res 5:100154","journal-title":"J Agric Food Res"},{"key":"15221_CR24","doi-asserted-by":"crossref","first-page":"104852","DOI":"10.1016\/j.compag.2019.104852","volume":"163","author":"G Hu","year":"2019","unstructured":"Hu G, Wu H, Zhang Y, Wan M (2019) A low shot learning method for tea leaf\u2019s disease identification. Comput Electron Agric 163:104852","journal-title":"Comput Electron Agric"},{"key":"15221_CR25","doi-asserted-by":"crossref","first-page":"162588","DOI":"10.1109\/ACCESS.2020.3021487","volume":"8","author":"S Janarthan","year":"2020","unstructured":"Janarthan S, Thuseethan S, Rajasegarar S, Lyu Q, Zheng Y, Yearwood J (2020) Deep metric learning based citrus disease classification with sparse data. IEEE Access 8:162588\u2013162600","journal-title":"IEEE Access"},{"key":"15221_CR26","doi-asserted-by":"crossref","first-page":"105824","DOI":"10.1016\/j.compag.2020.105824","volume":"179","author":"F Jiang","year":"2020","unstructured":"Jiang F, Lu Y, Chen Y, Cai D, Li G (2020) Image recognition of four rice leaf diseases based on deep learning and support vector machine. Comput Electron Agric 179:105824","journal-title":"Comput Electron Agric"},{"key":"15221_CR27","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.agsy.2019.102672","volume":"176","author":"LK Johnson","year":"2019","unstructured":"Johnson LK, Bloom JD, Dunning RD, Gunter CC, Boyette MD, Creamer NG (2019) Farmer harvest decisions and vegetable loss in primary production. Agric Syst 176:102\u2013672","journal-title":"Agric Syst"},{"issue":"5","key":"15221_CR28","doi-asserted-by":"crossref","first-page":"4423","DOI":"10.1016\/j.aej.2021.03.009","volume":"60","author":"ME Karar","year":"2021","unstructured":"Karar ME, Alsunaydi F, Albusaymi S, Alotaibi S (2021) A new mobile application of agricultural pests recognition using deep learning in cloud computing system. Alexandria Eng J 60(5):4423\u20134432","journal-title":"Alexandria Eng J"},{"key":"15221_CR29","doi-asserted-by":"crossref","unstructured":"Kaur S, Sikka G, Awasthi LK (2018) Sentiment analysis approach based on N-gram and KNN classifier. In: 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC). IEEE, pp 1\u20134","DOI":"10.1109\/ICSCCC.2018.8703350"},{"key":"15221_CR30","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.compag.2017.11.012","volume":"144","author":"AY Khaled","year":"2018","unstructured":"Khaled AY, Abd Aziz S, Bejo SK, Nawi NM, Seman IA (2018) Spectral features selection and classification of oil palm leaves infected by basal stem rot (BSR) disease using dielectric spectroscopy. Comput Electron Agric 144:297\u2013309","journal-title":"Comput Electron Agric"},{"key":"15221_CR31","doi-asserted-by":"crossref","first-page":"106192","DOI":"10.1016\/j.compag.2021.106192","volume":"186","author":"M Khanramaki","year":"2021","unstructured":"Khanramaki M, Asli-Ardeh EA, Kozegar E (2021) Citrus pests classification using an ensemble of deep learning models. Comput Electron Agric 186:106192","journal-title":"Comput Electron Agric"},{"key":"15221_CR32","doi-asserted-by":"crossref","first-page":"111275","DOI":"10.1016\/j.envres.2021.111275","volume":"198","author":"N Krishnamoorthy","year":"2021","unstructured":"Krishnamoorthy N, Prasad LN, Kumar CP, Subedi B, Abraha HB, Sathishkumar VE (2021) Rice leaf diseases prediction using deep neural networks with transfer learning. Environ Res 198:111275","journal-title":"Environ Res"},{"issue":"2","key":"15221_CR33","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1109\/TMM.2017.2741423","volume":"20","author":"K Kumar","year":"2017","unstructured":"Kumar K, Shrimankar DD (2017) F-DES: fast and deep event summarization. IEEE Trans Multimedia 20(2):323\u2013334","journal-title":"IEEE Trans Multimedia"},{"issue":"20","key":"15221_CR34","doi-asserted-by":"crossref","first-page":"26635","DOI":"10.1007\/s11042-018-5882-z","volume":"77","author":"K Kumar","year":"2018","unstructured":"Kumar K, Shrimankar DD (2018) Deep event learning boost-up approach: Delta. Multimedia Tools and Applications 77(20):26635\u201326655","journal-title":"Multimedia Tools and Applications"},{"key":"15221_CR35","doi-asserted-by":"crossref","unstructured":"Kumari S, Singh M, Kumar K (2019) Prediction of liver disease using grouping of machine learning classifiers. In: International conference on deep learning, artificial intelligence and robotics. Springer, Cham, pp 339\u2013349","DOI":"10.1007\/978-3-030-67187-7_35"},{"key":"15221_CR36","doi-asserted-by":"crossref","first-page":"105240","DOI":"10.1016\/j.compag.2020.105240","volume":"169","author":"Y Li","year":"2020","unstructured":"Li Y, Yang J (2020) Few-shot cotton pest recognition and terminal realization. Comput Electron Agric 169:105240","journal-title":"Comput Electron Agric"},{"key":"15221_CR37","doi-asserted-by":"crossref","first-page":"160274","DOI":"10.1109\/ACCESS.2019.2949852","volume":"7","author":"R Li","year":"2019","unstructured":"Li R, Wang R, Zhang J, Xie C, Liu L, Wang F, Chen H, Chen T, Hu H, Jia X, Hu M (2019) An effective data augmentation strategy for CNN-based pest localization and recognition in the field. IEEE Access 7:160274\u2013160283","journal-title":"IEEE Access"},{"key":"15221_CR38","doi-asserted-by":"crossref","first-page":"105174","DOI":"10.1016\/j.compag.2019.105174","volume":"169","author":"Y Li","year":"2020","unstructured":"Li Y, Wang H, Dang LM, Sadeghi-Niaraki A, Moon H (2020) Crop pest recognition in natural scenes using convolutional neural networks. Comput Electron Agric 169:105174","journal-title":"Comput Electron Agric"},{"key":"15221_CR39","doi-asserted-by":"crossref","first-page":"105803","DOI":"10.1016\/j.compag.2020.105803","volume":"178","author":"Y Li","year":"2020","unstructured":"Li Y, Nie J, Chao X (2020) Do we really need deep CNN for plant diseases identification? Comput Electron Agric 178:105803","journal-title":"Comput Electron Agric"},{"key":"15221_CR40","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1109\/TIP.2021.3049334","volume":"30","author":"X Liu","year":"2021","unstructured":"Liu X, Min W, Mei S, Wang L, Jiang S (2021) Plant disease recognition: a large-scale benchmark dataset and a visual region and loss reweighting approach. IEEE Trans Image Process 30:2003\u20132015","journal-title":"IEEE Trans Image Process"},{"key":"15221_CR41","doi-asserted-by":"crossref","first-page":"57952","DOI":"10.1109\/ACCESS.2020.2982443","volume":"8","author":"M Lv","year":"2020","unstructured":"Lv M, Zhou G, He M, Chen A, Zhang W, Hu Y (2020) Maize leaf disease identification based on feature enhancement and dms-robust alexnet. IEEE Access 8:57952\u201357966","journal-title":"IEEE Access"},{"key":"15221_CR42","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.compag.2017.11.024","volume":"142","author":"D Mondal","year":"2017","unstructured":"Mondal D, Kole DK, Roy K (2017) Gradation of yellow mosaic virus disease of okra and bitter gourd based on entropy based binning and Naive Bayes classifier after identification of leaves. Comput Electron Agric 142:485\u2013493","journal-title":"Comput Electron Agric"},{"issue":"15","key":"15221_CR43","doi-asserted-by":"crossref","first-page":"11419","DOI":"10.1007\/s00521-019-04634-7","volume":"32","author":"MS Mustafa","year":"2020","unstructured":"Mustafa MS, Husin Z, Tan WK, Mavi MF, Farook RSM (2020) Development of automated hybrid intelligent system for herbs plant classification and early herbs plant disease detection. Neural Comput Appl 32(15):11419\u201311441","journal-title":"Neural Comput Appl"},{"key":"15221_CR44","doi-asserted-by":"crossref","unstructured":"Negi A, Chauhan P, Kumar K, Rajput RS (2020) Face mask detection classifier and model pruning with keras-surgeon. In: 2020 5th IEEE international conference on recent advances and innovations in engineering (ICRAIE). IEEE, pp 1\u20136","DOI":"10.1109\/ICRAIE51050.2020.9358337"},{"key":"15221_CR45","doi-asserted-by":"crossref","unstructured":"Negi A, Kumar K, Chauhan P (2021) Deep neural network-based multi\u2010class image classification for plant diseases. Automation using the IoT and machine learning, agricultural informatics, pp 117\u2013129","DOI":"10.1002\/9781119769231.ch6"},{"key":"15221_CR46","doi-asserted-by":"crossref","unstructured":"Negi A, Kumar K, Chaudhari NS, Singh N, Chauhan P (2021) Predictive analytics for recognizing human activities using residual network and fine-tuning. In: International conference on big data analytics. Springer, Cham, pp 296\u2013310","DOI":"10.1007\/978-3-030-93620-4_21"},{"key":"15221_CR47","doi-asserted-by":"crossref","unstructured":"Negi A, Kumar K, Chauhan P, Rajput RS (2021) Deep neural architecture for face mask detection on simulated masked face dataset against covid-19 pandemic. In: 2021 international conference on computing, communication, and intelligent systems (ICCCIS). IEEE, pp 595\u2013600","DOI":"10.1109\/ICCCIS51004.2021.9397196"},{"key":"15221_CR48","first-page":"4856","volume":"33","author":"A Nigam","year":"2020","unstructured":"Nigam A, Tiwari AK, Pandey A (2020) Paddy leaf diseases recognition and classification using PCA and BFO-DNN algorithm by image processing. Mater Today: Proc 33:4856\u20134862","journal-title":"Mater Today: Proc"},{"key":"15221_CR49","doi-asserted-by":"crossref","first-page":"87534","DOI":"10.1109\/ACCESS.2019.2924973","volume":"7","author":"W Pan","year":"2019","unstructured":"Pan W, Qin J, Xiang X, Wu Y, Tan Y, Xiang L (2019) A smart mobile diagnosis system for citrus diseases based on densely connected convolutional networks. IEEE Access 7:87534\u201387542","journal-title":"IEEE Access"},{"issue":"10","key":"15221_CR50","doi-asserted-by":"crossref","first-page":"2500","DOI":"10.1016\/S2095-3119(20)63168-9","volume":"19","author":"YAO Qing","year":"2020","unstructured":"Qing YAO, Jin FENG, Jian TANG, Xu WG, Zhu XH, Yang BJ, Jun L, Xie YZ, Bo YAO, Wu SZ, Kuai NY (2020) Development of an automatic monitoring system for rice light-trap pests based on machine vision. J Integr Agric 19(10):2500\u20132513","journal-title":"J Integr Agric"},{"key":"15221_CR51","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.postharvbio.2017.03.007","volume":"129","author":"A Rady","year":"2017","unstructured":"Rady A, Ekramirad N, Adedeji AA, Li M, Alimardani R (2017) Hyperspectral imaging for detection of codling moth infestation in GoldRush apples. Postharvest Biol Technol 129:37\u201344","journal-title":"Postharvest Biol Technol"},{"issue":"3","key":"15221_CR52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12046-021-01652-x","volume":"46","author":"D Rajendran","year":"2021","unstructured":"Rajendran D, Vigneshwari S (2021) Design of agricultural ontology based on levy flight distributed optimization and Na\u00efve BaSyes classifier. S\u0101dhan\u0101 46(3):1\u201312","journal-title":"S\u0101dhan\u0101"},{"key":"15221_CR53","doi-asserted-by":"crossref","first-page":"105527","DOI":"10.1016\/j.compag.2020.105527","volume":"175","author":"PK Sethy","year":"2020","unstructured":"Sethy PK, Barpanda NK, Rath AK, Behera SK (2020) Deep featurebased rice leaf disease identification using support vector machine. Comput Electron Agric 175:105527","journal-title":"Comput Electron Agric"},{"key":"15221_CR54","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.compag.2018.04.023","volume":"150","author":"M Sharif","year":"2018","unstructured":"Sharif M, Khan MA, Iqbal Z, Azam MF, Lali MIU, Javed MY (2018) Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Comput Electron Agric 150:220\u2013234","journal-title":"Comput Electron Agric"},{"key":"15221_CR55","doi-asserted-by":"crossref","unstructured":"Sharma S, Kumar P, Kumar K (2017) LEXER: Lexicon based emotion analyzer. In: International conference on pattern recognition and machine intelligence. Springer, Cham, pp 373\u2013379","DOI":"10.1007\/978-3-319-69900-4_47"},{"key":"15221_CR56","doi-asserted-by":"crossref","first-page":"163703","DOI":"10.1109\/ACCESS.2020.3021830","volume":"8","author":"Z Shi","year":"2020","unstructured":"Shi Z, Dang H, Liu Z, Zhou X (2020) Detection and identification of stored-grain insects using deep learning: a more effective neural network. IEEE Access 8:163703\u2013163714","journal-title":"IEEE Access"},{"key":"15221_CR57","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.fcr.2017.12.002","volume":"217","author":"T Shiba","year":"2018","unstructured":"Shiba T, Hirae M, Hayano-Saito Y, Ohto Y, Uematsu H, Sugiyama A, Okuda M (2018) Spread and yield loss mechanisms of rice stripe disease in rice paddies. Field Crops Research 217:211\u2013217","journal-title":"Field Crops Research"},{"key":"15221_CR58","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.compag.2017.09.038","volume":"148","author":"M Shuaibu","year":"2018","unstructured":"Shuaibu M, Lee WS, Schueller J, Gader P, Hong YK, Kim S (2018) Unsupervised hyperspectral band selection for apple Marssonina blotch detection. Comput Electron Agric 148:45\u201353","journal-title":"Comput Electron Agric"},{"key":"15221_CR59","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.compag.2018.10.017","volume":"155","author":"J Su","year":"2018","unstructured":"Su J, Liu C, Coombes M, Hu X, Wang C, Xu X, Li Q, Guo L, Chen WH (2018) Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery. Comput Electron Agric 155:157\u2013166","journal-title":"Comput Electron Agric"},{"key":"15221_CR60","doi-asserted-by":"crossref","first-page":"104906","DOI":"10.1016\/j.compag.2019.104906","volume":"164","author":"K Thenmozhi","year":"2019","unstructured":"Thenmozhi K, Reddy US (2019) Crop pest classification based on deep convolutional neural network and transfer learning. Comput Electron Agric 164:104906","journal-title":"Comput Electron Agric"},{"key":"15221_CR61","doi-asserted-by":"crossref","first-page":"112350","DOI":"10.1016\/j.rse.2021.112350","volume":"257","author":"L Tian","year":"2021","unstructured":"Tian L, Xue B, Wang Z, Li D, Yao X, Cao Q, Zhu Y, Cao W, Cheng T (2021) Spectroscopic detection of rice leaf blast infection from asymptomatic to mild stages with integrated machine learning and feature selection. Remote Sens Environ 257:112350","journal-title":"Remote Sens Environ"},{"key":"15221_CR62","doi-asserted-by":"crossref","unstructured":"Turkoglu M, Hanbay D, Sengur A (2019) Multi-model LSTM-based convolutional neural networks for detection of apple diseases and pests. J Ambient Intell Humaniz Comput:1\u201311","DOI":"10.1007\/s12652-019-01591-w"},{"issue":"21","key":"15221_CR63","doi-asserted-by":"crossref","first-page":"16347","DOI":"10.1007\/s00500-020-04946-0","volume":"24","author":"MP Vaishnnave","year":"2020","unstructured":"Vaishnnave MP, Suganya Devi K, Ganeshkumar P (2020) Automatic method for classification of groundnut diseases using deep convolutional neural network. Soft Comput 24(21):16347\u201316360","journal-title":"Soft Comput"},{"key":"15221_CR64","doi-asserted-by":"crossref","first-page":"105106","DOI":"10.1016\/j.compag.2019.105106","volume":"168","author":"R Van De Vijver","year":"2020","unstructured":"Van De Vijver R, Mertens K, Heungens K, Somers B, Nuyttens D, Borra-Serrano I, Lootens P, Rold\u00e1n-Ruiz I, Vangeyte J, Saeys W (2020) In-field detection of Alternaria solani in potato crops using hyperspectral imaging. Comput Electron Agric 168:105106","journal-title":"Comput Electron Agric"},{"issue":"1","key":"15221_CR65","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s41348-020-00368-0","volume":"128","author":"VK Vishnoi","year":"2021","unstructured":"Vishnoi VK, Kumar K, Kumar B (2021) Plant disease detection using computational intelligence and image processing. J Plant Dis Prot 128(1):19\u201353","journal-title":"J Plant Dis Prot"},{"issue":"4","key":"15221_CR66","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1007\/s11829-018-9604-2","volume":"12","author":"Q Wang","year":"2018","unstructured":"Wang Q, Bao W, Yang F, Yang Y, Lu Y (2018) A PCR-based analysis of plant DNA reveals the feeding preferences of Apolygus lucorum (Heteroptera: Miridae). Arthropod-Plant Interact 12(4):567\u2013574","journal-title":"Arthropod-Plant Interact"},{"key":"15221_CR67","doi-asserted-by":"crossref","first-page":"105834","DOI":"10.1016\/j.compag.2020.105834","volume":"179","author":"J Wang","year":"2020","unstructured":"Wang J, Li Y, Feng H, Ren L, Du X, Wu J (2020) Common pests image recognition based on deep convolutional neural network. Comput Electron Agric 179:105834","journal-title":"Comput Electron Agric"},{"key":"15221_CR68","doi-asserted-by":"crossref","first-page":"106268","DOI":"10.1016\/j.compag.2021.106268","volume":"187","author":"F Wang","year":"2021","unstructured":"Wang F, Wang R, Xie C, Zhang J, Li R, Liu L (2021) Convolutional neural network based automatic pest monitoring system using hand-held mobile image analysis towards non-site-specific wild environment. Comput Electron Agric 187:106268","journal-title":"Comput Electron Agric"},{"key":"15221_CR69","doi-asserted-by":"crossref","first-page":"106629","DOI":"10.1016\/j.compag.2021.106629","volume":"193","author":"T Wang","year":"2022","unstructured":"Wang T, Mei X, Thomasson JA, Yang C, Han X, Yadav PK, Shi Y (2022) GIS-based volunteer cotton habitat prediction and plant-level detection with UAV remote sensing. Comput Electron Agric 193:106629","journal-title":"Comput Electron Agric"},{"issue":"2","key":"15221_CR70","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.bbrc.2020.01.126","volume":"524","author":"C Wei","year":"2020","unstructured":"Wei C, Qin T, Li Y, Wang W, Dong T, Wang Q (2020) Host-induced gene silencing of the acetolactate synthases VdILV2 and VdILV6 confers resistance to Verticillium wilt in cotton (Gossypium hirsutum L). Biochem Biophys Res Commun 524(2):392\u2013397","journal-title":"Biochem Biophys Res Commun"},{"key":"15221_CR71","doi-asserted-by":"crossref","first-page":"41087","DOI":"10.1109\/ACCESS.2022.3167513","volume":"10","author":"Y Wu","year":"2022","unstructured":"Wu Y, Feng X, Chen G (2022) Plant Leaf Diseases Fine-Grained categorization using Convolutional neural networks. IEEE Access 10:41087\u201341096","journal-title":"IEEE Access"},{"key":"15221_CR72","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.compag.2018.08.028","volume":"154","author":"M Xiao","year":"2018","unstructured":"Xiao M, Ma Y, Feng Z, Deng Z, Hou S, Shu L, Lu Z (2018) Rice blast recognition based on principal component analysis and neural network. Comput Electron Agric 154:482\u2013490","journal-title":"Comput Electron Agric"},{"issue":"1","key":"15221_CR73","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s11676-018-0832-1","volume":"31","author":"Z Xu","year":"2020","unstructured":"Xu Z, Huang X, Lin L, Wang Q, Liu J, Yu K, Chen C (2020) BP neural networks and random forest models to detect damage by Dendrolimus punctatus Walker. J Forestry Res 31(1):107\u2013121","journal-title":"J Forestry Res"},{"key":"15221_CR74","doi-asserted-by":"crossref","unstructured":"Yadav A, Verma HK, Awasthi LK (2021) Voting classification method with PCA and K-means for diabetic prediction. In: Innovations in computer science and engineering. Springer, Singapore, pp 651\u2013656","DOI":"10.1007\/978-981-33-4543-0_69"},{"issue":"8","key":"15221_CR75","doi-asserted-by":"crossref","first-page":"1800","DOI":"10.1016\/S2095-3119(18)61915-X","volume":"17","author":"JH Zhang","year":"2018","unstructured":"Zhang JH, Kong FT, Wu JZ, Han SQ, Zhai ZF (2018) Automatic image segmentation method for cotton leaves with disease under natural environment. J Integr Agric 17(8):1800\u20131814","journal-title":"J Integr Agric"},{"key":"15221_CR76","doi-asserted-by":"crossref","first-page":"30370","DOI":"10.1109\/ACCESS.2018.2844405","volume":"6","author":"X Zhang","year":"2018","unstructured":"Zhang X, Qiao Y, Meng F, Fan C, Zhang M (2018) Identification of maize leaf diseases using improved deep convolutional neural networks. Ieee Access 6:30370\u201330377","journal-title":"Ieee Access"},{"key":"15221_CR77","doi-asserted-by":"crossref","first-page":"105588","DOI":"10.1016\/j.compag.2020.105588","volume":"175","author":"DY Zhang","year":"2020","unstructured":"Zhang DY, Chen G, Yin X, Hu RJ, Gu CY, Pan ZG, Zhou XG, Chen Y (2020) Integrating spectral and image data to detect Fusarium head blight of wheat. Comput Electron Agric 175:105588","journal-title":"Comput Electron Agric"},{"key":"15221_CR78","doi-asserted-by":"crossref","first-page":"109876","DOI":"10.1109\/ACCESS.2020.3001652","volume":"8","author":"D Zhang","year":"2020","unstructured":"Zhang D, Wang Z, Jin N, Gu C, Chen Y, Huang Y (2020) Evaluation of efficacy of fungicides for control of wheat fusarium head blight based on digital imaging. IEEE Access 8:109876\u2013109890","journal-title":"IEEE Access"},{"key":"15221_CR79","doi-asserted-by":"crossref","first-page":"143190","DOI":"10.1109\/ACCESS.2019.2943454","volume":"7","author":"G Zhou","year":"2019","unstructured":"Zhou G, Zhang W, Chen A, He M, Ma X (2019) Rapid detection of rice disease based on FCM-KM and faster R-CNN fusion. IEEE Access 7:143190\u2013143206","journal-title":"IEEE Access"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15221-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-15221-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15221-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T15:44:21Z","timestamp":1744213461000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-15221-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,11]]},"references-count":79,"journal-issue":{"issue":"27","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["15221"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-15221-3","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,11]]},"assertion":[{"value":"14 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 April 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}