INTELIGÊNCIA ARTIFICIAL E SUAS FERRAMENTAS NO CONTROLE DE PRAGAS PARA PRODUÇÃO AGRÍCOLA: UMA REVISÃO
DOI:
https://doi.org/10.47820/recima21.v5i5.5277Palavras-chave:
Agronegócio. CNN. Processamento de imagens. Agricultura inteligente. Pestes. Imagem.Resumo
A inteligência artificial e suas ferramentas estão sendo amplamente utilizadas em todo o mundo. O seu uso na agricultura está sendo amplamente estudado e expandido, abrangendo desde a pré-safra até o pós-safra. O aumento da população mundial tem desencadeado a necessidade de aumentar a produção de alimentos. Essa demanda desencadeou uma busca por soluções que promovam o aumento da produção e qualidade dos alimentos. Uma forma de alcançar esse objetivo é o controle das pragas. A inteligência artificial e suas ferramentas têm demostrado ser uma solução em crescimento e ascensão no controle e combate às pragas. Esta pesquisa concentra-se em revisar e demostrar os avanços no combate e controle de pragas, utilizando ferramentas de inteligência artificial e imagens. Destacam-se atividades como classificação de pragas, identificação de insetos, uso e captura de imagens por Unmanned Aerial Vehicle, além da utilização deep learning e convolutional neural network. O estudo apresenta a atual utilização da inteligência artificial, machine learning e deep learning, identificando as ferramentas em uso e as soluções propostas ou desenvolvidas para o combate e controle de pragas. Esta pesquisa serve como base para abordar futuros desafios referentes ao uso de inteligência artificial e suas ferramentas na identificação de pragas em imagens reais, fornecendo insights para pesquisadores interessados em desenvolver estudos sobre o uso de deep learning na agricultura.
Downloads
Referências
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
Downloads
Publicado
Como Citar
Licença
Direitos de Autor (c) 2024 RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218
Este trabalho encontra-se publicado com a Licença Internacional Creative Commons Atribuição 4.0.
Os direitos autorais dos artigos/resenhas/TCCs publicados pertecem à revista RECIMA21, e seguem o padrão Creative Commons (CC BY 4.0), permitindo a cópia ou reprodução, desde que cite a fonte e respeite os direitos dos autores e contenham menção aos mesmos nos créditos. Toda e qualquer obra publicada na revista, seu conteúdo é de responsabilidade dos autores, cabendo a RECIMA21 apenas ser o veículo de divulgação, seguindo os padrões nacionais e internacionais de publicação.
