ARTIFICIAL INTELLIGENCE AND ITS TOOLS IN PEST CONTROL FOR AGRICULTURAL PRODUCTION: A REVIEW
DOI:
https://doi.org/10.47820/recima21.v5i5.5277Keywords:
Deep Learning, CNN, Intelligence Agricultural, Pests, ImageAbstract
Artificial Intelligence (AI) and its tools are being widely used worldwide. In the area of agriculture, AI is being widely studied and expanding. The use of AI in agriculture is being widely studied and expanding from pre-harvest to post-harvest. The increase in world population has triggered the need to increase food production. This need has triggered a search for solutions that promote increased food production and quality. One way to increase food production and quality is pest control. AI and its tools have proven to be a growing and rising solution in controlling and combating pests. This research focuses on reviewing and demonstrating the advances in combating and controlling pests using AI tools and images. It stands out: the classification of pests; insect identification; use and capture of Unmanned aerial vehicle (UAV) footage; using Deep Learning (DL) and Convolutional Neural Network (CNN). A search engine was applied to 5 databases. Cutting criteria were applied in 3 stages, and there were 71 papers at the end. The 71 went through 3 quality assessment questions, leaving 47 works for final analysis. This study demonstrated that the DL and the CNN tool using real images have the potential for insect control and combat solutions. Another tool in recent studies associated with CNN is the attention mechanism, improving pest identification results. Identification of insects through leaf images using CNN requires.
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ABID, H.; NIDA, N.; IRTAZA, A. PestinaNet- A real-time crop pest detection system. In: 2nd International Conference on Computing and Machine Intelligence (ICMI), p. 1–4, 2022. doi:10.1109/ICMI55296.2022.9873654 DOI: https://doi.org/10.1109/ICMI55296.2022.9873654
AGNIHOTRI, V. Machine learning based pest identification in paddy plants. International conference on Electronics, Communication and Aerospace Technology – ICECA, v. 3, p. 246-250, 2019. doi:10.1109/ICECA.2019.8822047 DOI: https://doi.org/10.1109/ICECA.2019.8822047
ALBAHAR, M. A survey on deep learning and its impact on agriculture: Challenges and opportunities. Agriculture, v. 13, p. 540, 2023. doi:10.3390/agriculture13030540 DOI: https://doi.org/10.3390/agriculture13030540
ALBATTAH, W.; MASOOD, M.; JAVED, A.; NAWAZ, M.; ALBAHLI, s. Custom cornernet: a drone based improved deep learning technique for large-scale multiclass pest localization and classification. Complex Intelligent Systems, v. 2198-6053. 2022. doi.org/10.1007/s40747-022-00847-x DOI: https://doi.org/10.1007/s40747-022-00847-x
ALBORE, A. V. Z.; PEYRARD, N.; SABBADIN, R.; TEICHTEIL-KONIGSBUCH, F. An online replanning approach for crop fields mapping with autonomous UAVs. Proceedings of the Twenty-Fifth International Conference on International Conference on Automated Planning and Scheduling – ICAPS 25, p. 259-2677, 2015. doi.org/10.1609/icaps.v25i1.13692 DOI: https://doi.org/10.1609/icaps.v25i1.13692
ALVES, A. N.; WITENBERG. S. R. S.; BORGES, D. L. Cotton pests classification in field-based image using deep residual networks. Computers and Electronics in Agriculture, v. 174 p. 105488, 2020. doi.org/10.1016/j.compag.2020.105488 DOI: https://doi.org/10.1016/j.compag.2020.105488
ARVIND, G.; ATHIRA, V.; HARIPRIYA, H.; RANY, R. A.; ARAVIND, S. Automated irrigation with advanced seed germination and pest control. Technological Innovations in ICT for Agriculture and Rural Development – TIAR, 2017. doi:10.1109/TIAR.2017.8273687 DOI: https://doi.org/10.1109/TIAR.2017.8273687
AWUOR, F.; OTANGA, S.; KIMELI, V.; RAMBIM, D.; ABUYA, T. E-pest: Surveillance large scale crop pest surveillance and control. Africa Week Conference – IST, 2019. doi:10.23919/ISTAFRICA.2019.8764824 DOI: https://doi.org/10.23919/ISTAFRICA.2019.8764824
BANERJEE, G.; SARKAR, U.; GHOSH, I. A radial basis function network-based classifier for detection of selected tea pests. International Journal of Advanced Research in Computer Science and Software Engineering – IJARCSSE, v. 7, p. 665–669, 2017. doi:10.23956/IJARCSSE/V7I5/0152 DOI: https://doi.org/10.23956/ijarcsse/V7I5/0152
BRUNELLI, D.; ALBANESE, A.; ACUNTO, D.; NORDELLO, M. Energy neutral machine learning based IoT device for pest detection in precision agriculture. Internet of Things, v. 2, 2019. doi:10.1109/IOTM.0001.1900037 DOI: https://doi.org/10.1109/IOTM.0001.1900037
CASTILLO, E.; GUTIERREZ, J. M.; HADI, A. S. Expert Systems and Probabilistic Network Models. [S. l.]: Springer Link, 2012.
CHEN, J.; CHEN, W.; ZEB, A.; ZHANG, D.; NANEHKARAN, Y. A. Crop pest recognition using attention-embedded lightweight network under feld conditions. Applied Entomology and Zoology, v. 56, p. 427–442, 2021. doi.org/10.1007/s13355-021-00732-y DOI: https://doi.org/10.1007/s13355-021-00732-y
CHOUGULE, A.; JHA, V.; MUKHOPADHYAY, D. Ontology based system for pests and disease management of grapes in india. International Conference on Advanced Computing – IACC, v. 6, 2016. doi:10.1109/IACC.2016.34 DOI: https://doi.org/10.1109/IACC.2016.34
CHOUGULE, A.; JHA, V. K.; Mukhopadhyay, D. Decision support for grape crop protection using ontology. International Journal of Reasoning-based Intelligent Systems – IJRIS, v. 11, n. 1, 2019. doi.org/10.1504/IJRIS.2019.098051 DOI: https://doi.org/10.1504/IJRIS.2019.098051
CHUDZIK, P.; MITCHELLl, A.; ALKASEEM, M.; WU, Y.; FANG, S.; HUDAIB, T.; PEARSON, S.; ALDIRI, B. Mobile real-time grasshopper detection and data aggregation framework. Scientific Reports, v. 10, 2020. doi:10.1038/s41598-020-57674-8 DOI: https://doi.org/10.1038/s41598-020-57674-8
COULIBALYA, S.; KAMSU-FOGUEM, B.; KAMISSOKO, D.; TRAORE, D. Deep neural networks with transfer learning in millet crop images. Computers in Industry, v. 108, 2019. doi.org/10.1016/j.compind.2019.02.003 DOI: https://doi.org/10.1016/j.compind.2019.02.003
DAS, S.; GHOSH, I.; BANERJEE, G.; SANKAR, U. Artificial Intelligence in Agriculture: A Literature Survey. International Journal of Scientific Research in Computer Science Applications and Management Studies - Artificial-IJSRCSAMS, v. 7, 2018.
DHARINI, P. U.; NARRMADHA, K.; SARANYA, k.; MORRISHA, S. Iot based decision support system for agriculture yield enhancements. International Journal of Recent Technology and Engineering – IJRTE, v. 7, p. 362–367, 2018. ISSN: 2277-3878
DOMINGUES, T.; BRANDÃO, T.; FERREIRA, J. C. Machine learning for detection and prediction of crop diseases and pests: A comprehensive survey. Agriculture, v. 12, n. 9, p. 1350, 2022. doi:10.3390/agriculture12091350 DOI: https://doi.org/10.3390/agriculture12091350
DONG, C.; ZHANG, Z.; YUE, J.; ZHOU, L. Automatic recognition of strawberry diseases and pests using convolutional neural network. Smart Agricultural Technology, v. 1, p. 100009, 2021. doi:10.1016/j.atech.2021.100009 DOI: https://doi.org/10.1016/j.atech.2021.100009
DU, Y.; LIU, Y.; LI, N. Insect detection research in natural environment based on Faster-R-CNN model. Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence, p. 182–186, 2020. doi:10.1145/3395260.3395265 DOI: https://doi.org/10.1145/3395260.3395265
ESPINOZA, K.; VALERA, D. L.; TORRES, J. A.; LÓPEZ, A.; MOLINA-AIZ, F. D. Combination of image processing and artificial neural networks as a novel approach for the identification of BemisiaTabaci and Frankliniella occidentalis on sticky traps in greenhouse agriculture. Computers and Electronics in Agriculture, v. 127, 2016. doi.org/10.1016/j.compag.2016.07.008 DOI: https://doi.org/10.1016/j.compag.2016.07.008
FIEHN, H. B.; SCHIEBEL, L.; AVILA, A. F.; MILLER, B.; MICKELSON, A, Smart agriculture system based on deep learning. International Conference on Smart Digital Environment – ICSDE, v. 2, 2018. doi.org/10.1145/3289100.3289126 DOI: https://doi.org/10.1145/3289100.3289126
GAN, G.; XIAO, X.; JIANG, C.; YE, Y.; HE, Y.; XU, Y.; LUO, C. Strawberry disease and pest identification and control based on se-resnext50 model. 3rd International Conference on Computer Vision, Image and Deep Learning International Conference on Computer Engineering and Applications (CVIDL & ICCEA), p. 237–243, 2022. doi:10.1109/CVIDLICCEA56201.2022.9825283 DOI: https://doi.org/10.1109/CVIDLICCEA56201.2022.9825283
GASAYE, K.; MOLLO, R. K. A mobile application for fruit fly identification using deep transfer learning: A case study for Mauritius. International Conference for Advancement in Technology (ICONAT), p. 1–5, 2022. doi:10.1109/ICONAT53423.2022.9725945 DOI: https://doi.org/10.1109/ICONAT53423.2022.9725945
HE, Y.; ZHOU, Z.; TIAN, L.; LIU, Y.; LUO, X. Brown rice planthopper (nilaparvata lugens stal) detection based on deep learning. Precision Agriculture, v. 21, pp. 1385-1402, 2020. doi.org/10.1007/s11119-020-09726-2 DOI: https://doi.org/10.1007/s11119-020-09726-2
HOSSAIN, M. D. A.; TAREQ, A. H. M.; UDDIN, M. Pest-inspector: An insect detection system by learning data representations. International Conference on Advances in Science, Engineering and Robotics Technology – ICASERT, p. 1-7, 2019. doi:10.1109/ICASERT.2019.8934578 DOI: https://doi.org/10.1109/ICASERT.2019.8934578
ISSAD, H. A.; AOUDJIT, R.; RODRIGUES, J. A comprehensive review of data mining techniques in smart agriculture. Engineering in Agriculture Envivonment and Food, v. 12, p. 511–525, 2019. doi.org/10.1016/j.eaef.2019.11.003 DOI: https://doi.org/10.1016/j.eaef.2019.11.003
JIA, S.; GAO, H.; Hang, X. Tomato pests and diseases classification model based on optimized convolutional neural network. Journal of Physics: Conference Series, v. 1437 2019. doi.10.1088/1742-6596/1437/1/012052 DOI: https://doi.org/10.1088/1742-6596/1437/1/012052
JIAO, L.; DONG, S.; ZHANG, S.; XIE, C.; WANG, H. AF-RCNN: An anchor-free convolutional neural network for multi-categories agricultural pest detection. Computers and Electronics in Agriculture, v. 174, p. 105522, 2020. doi.org/10.1016/j.compag.2020.105522 DOI: https://doi.org/10.1016/j.compag.2020.105522
JUNIOR, C. R. G.; GOMES, P. H.; MANO, L. Y.; OLIVEIRA, R. B. de; CARVALHO, A. C. P. de L. F. de; FAIÇAL B. S. A machine learning-based approach for prediction of plant protection product deposition. Brazilian Conference on Intelligent Systems, 2017. doi:10.1109/BRACIS.2017.26 DOI: https://doi.org/10.1109/BRACIS.2017.26
KARAR, M. E.; ALSUNAYDI, F.; ALBUSAYMI, S.; ALOTAIBI, S. A new mobile application of agricultural pests recognition using deep learning in cloud computing system. Alexandria Engineering Journal, v. 60 p. 4423–4432, 2021. doi.org/10.1016/j.aej.2021.03.009 DOI: https://doi.org/10.1016/j.aej.2021.03.009
KHALIFA, N. E. M.; LOEY, M.; TAHA, M. N. Insect pests recognition based on deep transfer learning models. Theoretical and Applied Information Technology, v. 98, p. 60–68, 2020. ISSN: 1992-8645
KHATTABA, A.; HABIB, S. E. D.; ISMAIL, H.; ZAYAN, Y. F. S.; KHAIRY, M. M. An iot-based cognitive monitoring system for early plant disease forecast. Computers and Electronics in Agriculture, v. 166, 2019. doi.org/10.1016/j.compag.2019.105028 DOI: https://doi.org/10.1016/j.compag.2019.105028
KITCHENHAM, B. Procedures for undertaking systematic reviews. Information and Software Technology, v. 33, p. 1–26. 2004
KITCHENHAM, B.; BRERETON, P.; BUDGEN, D.; TURNER, M.; BAILEY, J.; LINKMAN, S. Systematic literature reviews in software engineering – a systematic literature review. Information and Software Technology, v. 51, 2009. doi.org/10.1016/j.infsof.2008.09.009 DOI: https://doi.org/10.1016/j.infsof.2008.09.009
KUMAR, V.; LAXMI, V. Pests detection using artificial neural network and image processing: A review. International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), p. 462–467, 2022. doi:10.1109/ICSCDS53736.2022.9760976 DOI: https://doi.org/10.1109/ICSCDS53736.2022.9760976
LI, Y.; YAN, H.; HU, F.; YUAN, K.; QIAN, M.; LIU, P.; CAI, Q.; LI, X.; GUO, J.; YU, J.; QIN, L.; LIU, H.; WU, W.; XIAO, P.; ZHOU, Z. The recognition of rice images by uav based on capsule network. Cluster Computin, v. 22, p. 9515–9524, 2018. doi.org/10.1007/s10586-018-2482-7 DOI: https://doi.org/10.1007/s10586-018-2482-7
LI, R.; WANG, R.; ZHANG, J.; XIE, C.; LIU, L.; WANG, F.; CHEN, H.; Chen, T.; HU, H.; JIA, X.; HU, M.; ZHOU, M.; LI, D.; LIU, W. An effective data augmentation strategy for cnn-based pest localization and recognition in the field. IEEE ACCESS, v. 7, p. 160274–160283, 2019. doi:10.1109/ACCESS.2019.2949852 DOI: https://doi.org/10.1109/ACCESS.2019.2949852
LI, Y.; WANG, H.; DANG, L. M.; SADEGHI-NIARAKI, A.; MOON, H. Crop pest recognition in natural scenes using convolutional neural networks. Computers and Electronics in Agriculture, v. 169, p. 105174, 2020. doi.org/10.1016/j.compag.2019.105174 DOI: https://doi.org/10.1016/j.compag.2019.105174
LINS, E. A.; RODRIGUEZ, J. P. M.; SCOLOSKI, S. I.; PIVATO, J.; LIMA, M. B.; FERNANDES, J. M. C.; PEREIRA, P. R. V. da.; LAU, D.; RIEDER, R. A method for counting and classifying aphids using computer vision. Computers and Electronics in Agriculture, v. 169, p. 105200, 2020. doi.org/10.1016/j.compag.2019.105200 DOI: https://doi.org/10.1016/j.compag.2019.105200
LIU, L.; WANG, R.; XIE, C.; YANG, P.; SUDIRMAN, S.; WANG, F.; LIU, W. Deep learning based automatic approach using hybrid global and local activated features towards large-scale multi-class pest monitoring. International Conference on Industrial Informatics, v. 17, 2019a. doi:10.1109/INDIN41052.2019.8972026 DOI: https://doi.org/10.1109/INDIN41052.2019.8972026
LIU, L.; WANG, R.; XIE, C.; YANG, P.; WANG, F.; SUDIRMAN, S.; LIU, W. Pestnet: An end-to-end deep learning approach for large-scale multi-class pest detection and classification. IEEE Access, v. 7, 2019b. doi.org/10.1109/access.2019.2909522 DOI: https://doi.org/10.1109/ACCESS.2019.2909522
LIU, L.; WANG, R.; XIE, C.; LI, R.; WANG, F.; QI, L. A global activated feature pyramid network for tiny pest detection in the wild. Machine Vision and Applications, v. 33, n. 76, 2022. doi.org/10.1007/s00138-022-01310-0 DOI: https://doi.org/10.1007/s00138-022-01310-0
MA, C.; LIANG, Y.; LYU, X. Weather analysis to predict rice pest using neural network and d-s evidential theory. International Conference on CyberEnabled Distributed Computing and Knowledge Discovery – CYBERC, 2019. doi:10.1109/CyberC.2019.00054 DOI: https://doi.org/10.1109/CyberC.2019.00054
MA, K.; NIE, Ming-Jun; LIN, S.; KONG, J.; YANG, C.; LIU, J. Fine-Grained Pests Recognition Based on Truncated Probability Fusion Network via Internet of Things in Forestry and Agricultural Scenes. Algoritmos, v. 14, n. 10, p. 290, 2021. doi.org/10.3390/a14100290 DOI: https://doi.org/10.3390/a14100290
MARTINI, D. R. D.; TETILA, E. C.; MARCATO, J. J.; MATSUBARA, E. T.; SIQUEIRA, H.; CASTRO JUNIOR, A. A. de; ARAUJO, M. S.; MONTEIRO, C. H.; PISTORE, H.; LIESENBERG, V. Machine learning applied to UAV imagery in precision agriculture and forest monitoring in brazililian savanah. International Geoscience and Remote Sensing Symposium – IGARSS, 2019. doi:10.1109/IGARSS.2019.8900246 DOI: https://doi.org/10.1109/IGARSS.2019.8900246
MEKHA, V.; PARTHASARATHY, V. An automated pest identification and classification in crops using artificial intelligence—a state-of-art-review. Automatic Control and Computer Sciences, v. 56, 3, p. 283–290, 2022. doi:10.3103/S0146411622030038 DOI: https://doi.org/10.3103/S0146411622030038
MIQUE, E. L.; PALAOAG, T. D. Rice pest and disease detection using convolutional neural network. Proceedings of the 2018 International Conference on Information Science and System – ICISS, p. 147–151, 2018. doi.org/10.1145/3209914.3209945 DOI: https://doi.org/10.1145/3209914.3209945
NAM, N. T.; P. D. HUNG. Pest detection on traps using deep convolutional neural networks, Proceedings of the 2018 International Conference on Control and Computer Vision – ICCCV, p. 33–38, 2018. doi.org/10.1145/3232651.3232661 DOI: https://doi.org/10.1145/3232651.3232661
OZDEMIR, D.; KUNDURACI, M. S. Comparison of deep learning techniques for classification of the insects in order level with mobile software application. IEEE Access, v. 10, p. 35675–35684, 2022. doi:10.1109/ACCESS.2022.3163380 DOI: https://doi.org/10.1109/ACCESS.2022.3163380
PATEL, P. P.; VAGHELA, D. B. Crop diseases and pests detection using convolutional neural network. IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), p. 1–4, 2019, 10.1109/ICECCT.2019.8869510 DOI: https://doi.org/10.1109/ICECCT.2019.8869510
PENG, Y.; WANG, Y. Cnn and transformer framework for insect pest classification. Ecological Informatics, v. 72, p. 101846, 2022. doi.org/10.1016/j.ecoinf.2022.101846 DOI: https://doi.org/10.1016/j.ecoinf.2022.101846
PEREIRA, R. de C.; HIROSE, E.; CARVALHO, O. L. F. de; COSTA, R. M. da.; BORGES, D. L. Detection and classification of whiteflies and development stages on soybean leaves images using an improved deep learning strategy. Computers and Electronics in Agriculture, v. 199, p. 107132, 2022. doi.org/10.1016/j.compag.2022.107132 DOI: https://doi.org/10.1016/j.compag.2022.107132
PRADEEP, N.; KAUTISH, S.; NIRMALA, C.R.; GOYAL, V.; ABDELLATIF S. Modern Techniques for Agricultural Disease Management and Crop Yield Prediction. IGI Global Books, 2019. 10.4018/978-1-5225-9632-5 DOI: https://doi.org/10.4018/978-1-5225-9632-5
QINSI, W.; XUEYI, J.; XIAOLONG, X.; XUESHUN, W. Research on invasive insect image recognition based on artificial intelligence. 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), p. 744–748, 2021. doi:10.1109/ICBAIE52039.2021.9389971 DOI: https://doi.org/10.1109/ICBAIE52039.2021.9389971
QIAN, S.; DU, J.; ZHOU, J.; XIE, C.; JIAO, L.; LI, R. An effective pest detection method with automatic data augmentation strategy in the agricultural field. Signal, Image and Video Processing, 2022. doi.org/10.1007/s11760-022-02261-9 DOI: https://doi.org/10.1007/s11760-022-02261-9
RAJAN, P.; RADHAKRISHNAN, B.; SURESH, L. P. Detection and classification of pests from crop images using support vector machine. International Conference on Emerging Technological Trends – IETT, 2016. doi:10.1109/ICETT.2016.7873750 DOI: https://doi.org/10.1109/ICETT.2016.7873750
RAVISANKA, H.; RAO, S.; SREEDHAR, U. A web based expert system for identification and management of insect pests of tobacco. Journal of Entomology and Zoology Studies, v. 7, 2019.
REN, L.; HU, M.; FANG, Y.; DU, X.; FENG, H. Recognition of common pests in agriculture and forestry based on convolutional neural networks. Chinese Automation Congress – CAC, 2018. doi:10.1109/CAC.2018.8623223 DOI: https://doi.org/10.1109/CAC.2018.8623223
RIMAL, K.; SHANH, K.B.; JHA, A.K. Advanced multi-class deep learning convolution neural network approach for insect pest classifcation using tensorflow. International Journal of Environmental Science and Technology, 2022. doi.org/10.1007/s13762-022-04277-7 DOI: https://doi.org/10.1007/s13762-022-04277-7
SEGALLA, A.; FIACCO, G.; TRAMARIN, L.; NARDELLO, M.; BRUNELLI, D. Neural networks for pest detection in precision agriculture. 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), p. 7-12, 2020. doi:10.1109/MetroAgriFor50201.2020.9277657 DOI: https://doi.org/10.1109/MetroAgriFor50201.2020.9277657
SHAHZADI, R.; FERZUND, J.; TAUSIF, M.; ASIF, M. Internet of things based expert system for smart agriculture. International Journal of Advanced Computer Science and Applications, v. 7, 2016. doi:10.14569/IJACSA.2016.070947 DOI: https://doi.org/10.14569/IJACSA.2016.070947
SHANKAR, R. H.; VEERARAGHAVAN, A. K.; UVAIS; SIVARAMAN, K.; RAMACHANDRAN, S. S. Application of UAV for pest, weeds and disease detection using open computer vision. International Conference on Smart Systems and Inventive Technology, 2018. doi:10.1109/ICSSIT.2018.8748404 DOI: https://doi.org/10.1109/ICSSIT.2018.8748404
SILVA, L. A.; BRESSAN, P. O.; NUNES, D. G.; FREITAS, D. M.; MACHADO, B. B.; GONÇALVES, W. Estimating soybean leaf defoliation using convolutional neural networks and synthetic images. Computers and Electronics in Agriculture, v. 156, p. 360-368, 2019. doi.org/10.1016/j.compag.2018.11.040 DOI: https://doi.org/10.1016/j.compag.2018.11.040
SOBREIRO, L.; BRANCO, S.; CABRAL, J.; MOURA, L. Intelligent insect monitoring system -i²ms- using internet of things technologies and cloud-based services for early detection of pests of field crops. Industrial Electronics Society – IECON, 2019. p. 3080–3084. doi:10.1109/IECON.2019.8927085 DOI: https://doi.org/10.1109/IECON.2019.8927085
SONG, Y.; DUAN, X.; REN, Y.; XU, J.; LUO, L.; LI, D. Identification of the agricultural pests based on deep learning models. International Conference on Machine Learning, Big Data and Business Intelligence – MLBDBI, 2019. doi:10.1109/MLBDBI48998.2019.00044 DOI: https://doi.org/10.1109/MLBDBI48998.2019.00044
SOURAV, M.S.U.; WANG, H. Intelligent identification of jute pests based on transfer learning and deep convolutional neural networks. Neural Processing Letters, 2022. doi.org/10.1007/s11063-022-10978-4 DOI: https://doi.org/10.1007/s11063-022-10978-4
TASSIS, L. M.; SOUZA, J. E. T. de; KROHLING, R. A. A deep learning approach combining instance and semantic segmentation to identify diseases and pests of coffee leaves from in-field images. Computers and Electronics in Agriculture, v. 186, p. 106191, 2021. doi.org/10.1016/j.compag.2021.106191 DOI: https://doi.org/10.1016/j.compag.2021.106191
TETILA, E.C.; MACHADO, B.B.; MENEZES, G.V.; BELETE, N.A.S.; ASTOLFI, G.; PISTORE, H. A deep-learning approach for automatic counting of soybean insect pests. IEEE Geoscience and Remote Sensing Letters, v. 17, n. 10, p. 1837–1841, 2020a. doi:10.1109/LGRS.2019.2954735 DOI: https://doi.org/10.1109/LGRS.2019.2954735
TETILA, E. C.; MACHADO, B. B.; ASTOLFI, G.; BELETE, N.A.S; AMORIM, W. P.; ROEL, A. R.; PISTORI, H. Detection and classification of soybean pests using deep learning with uav images. Computers and Electronics in Agriculture, v. 179, p. 105836, 2020b. doi.org/10.1016/j.compag.2020.105836 DOI: https://doi.org/10.1016/j.compag.2020.105836
TRUONG, Q. B.; THANH, T.K.N.; NGUYEN, M. T.; TRUONG, Q. D.; HUYNH, H. X. Shallow and deep learning architecture for pests identification on pomelo leaf. International Conference on Knowledge and Systems Engineering KSE, v. 10, 2018. doi:10.1109/KSE.2018.8573422 DOI: https://doi.org/10.1109/KSE.2018.8573422
UN. World Population prospectos. UN: United nations report, 2017. http://population.un.org/wpp/Publications/Files/WPP2017_KeyFindings.pdf. Acesso em: 30 abr. 2020
VOUTOS, P. M.; KATHENIOTIS, J.; SOFOU, A. A survey on intelligent agricultural information handling methodologies. Sustentabilidade, v. 11, n. 12, 2019. doi.org/10.3390/su11123278 DOI: https://doi.org/10.3390/su11123278
WANG, W.; YANG, Y.; WANG, X.; WANG, W.; LI, J. Development of convolutional neural network and its application in image classification: a survey. Optical Engineering, v. 58, n. 4, p. 040901, 2019. doi.org/10.1117/1.OE.58.4.040901 DOI: https://doi.org/10.1117/1.OE.58.4.040901
WANG, F.; WANG, R; XIE, C.; ZHANG, J.; LI, R.; LIU, L. Convolutional neural network based automatic pest monitoring system using hand-held mobile image analysis towards non-site-specific wild environment. Computers and Electronics in Agriculture, v. 187, p. 106268, 2021a. doi.org/10.1016/j.compag.2021.106268 DOI: https://doi.org/10.1016/j.compag.2021.106268
WANG, R.; JIAO, L.; XIE, C.; CHEN, P.; DU, J.; LI, R. S-RPN: Sampling-balanced region proposal network for small crop pest detection. Computers and Electronics in Agriculture, v. 187, p. 106290, 2021b. doi.org/10.1016/j.compag.2021.106290 DOI: https://doi.org/10.1016/j.compag.2021.106290
WANG, H.; LI, Y.; DANG, L. M.; MOON, H. An efficient attention module for instance segmentation network in pest monitoring. Computers and Electronics in Agriculture, v. 195, p. 106853, 2022. 106853. doi.org/10.1016/j.compag.2022.106853 DOI: https://doi.org/10.1016/j.compag.2022.106853
WANI, H.; ASHTANKAR, N. An appropriate model predicting pest/diseases of crops using machine learning algorithms. International Conference on Advanced Computing and Communication Systems – ICACCS, v. 4, 2017. doi:10.1109/ICACCS.2017.8014714 DOI: https://doi.org/10.1109/ICACCS.2017.8014714
WU, J; LI, B.; WU, Z. Detection of crop pests and diseases based on deep convolutional neural network and improved algorithm. International Conference Proceeding Series, p. 20-27, 2019. doi.org/10.1145/3340997.3341010 DOI: https://doi.org/10.1145/3340997.3341010
XIE, C.; ZHANG, J.; LI, R.; LI, J.; HONG, P.; XIA, J.; CHEN, P. C. Automatic classification for field crop insects via multiple-task sparse representation and multiple-kernel learning. Computers and Electronics in Agriculture, v. 119 2015. doi.org/10.1016/j.compag.2015.10.015 DOI: https://doi.org/10.1016/j.compag.2015.10.015
XU, J.; WEI, H.; YE, M.; WANG, W. Research on recognition method of zanthoxylum armatum rust based on deep learning. International Conference Proceeding Series, p. 84-88, 2019. doi.org/10.1145/3365966.3365975 DOI: https://doi.org/10.1145/3365966.3365975
XU, C.; YU, C.; ZHANG, S.; WANG, X. Multi-scale convolution-capsule network for crop insect pest recognition. Electronics, v. 11, n. 10, p. 1630, 2022. doi:10.3390/electronics11101630 DOI: https://doi.org/10.3390/electronics11101630
YANG, F.; LI, F.; XU, J.; SU, G.; LI, J.; JI, M.; XIONG, W.; ZHAO, B. Effective insect recognition based on deep neural network models in complex background. Proceedings of the 5th International Conference on High Performance Compilation, Computing and Communications, p. 62–67, 2021. doi:10.1145/3471274.3471285 DOI: https://doi.org/10.1145/3471274.3471285
YU, H.; LIU, J.; CHEN, C.; HEIDARI, A. A.; ZHANG, Q.; CHEN, H. Optimized deep residual network system for diagnosing tomato pests. Computers and Electronics in Agriculture, v. 195, p. 106805, 2022. doi.org/10.1016/j.compag.2022.106805 DOI: https://doi.org/10.1016/j.compag.2022.106805
ZHAI, Z.; ORTEGA, J. F. M.; BELTRAN, V.; MARTINEZ, N. L. An Associated Representation Method for Defining Agricultural Cases in a Case-Based Reasoning System for Fast Case Retrieval. Sensors, (Basel), v. 19, n. 23, 2019. doi.org/10.3390/s19235118 DOI: https://doi.org/10.3390/s19235118
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