Agricultural Pest Detection Methods and Control Measures Combining Deep Learning Algorithms

[1] Deng, L., Wang, Z., Wang, C., He, Y., & Zhang, X. (2019). Application of agricultural insect pest detection and control map based on image processing analysis. Journal of Intelligent and Fuzzy Systems, 38(8), 1-11. Search in Google Scholar

[2] Jaber, N., Daher, N., & Asmar, D. (2022). Deep-pest-detector: automated detection and localization of processionary moth nests on pine trees via aerial drones and deep neural networks. Applied Engineering in Agriculture. Search in Google Scholar

[3] Feng Yuan, Yehua Dennis Wei, Jinlong Gao, & Wen Chen. (2019). Budding trends in integrated pest management using advanced micro- and nano-materials: challenges and perspectives. Journal of Cleaner Production. Search in Google Scholar

[4] Kostromytska, O. S., Shaohui, W., & Koppenhöfer Albrecht M. (2018). Diagnostic dose assays for the detection and monitoring of resistance in adults from listronotus maculicollis (coleoptera: curculionidae) populations. Journal of Economic Entomology(5), 5. Search in Google Scholar

[5] Royer, JE, Khan, M, Mayer, & DG. (2018). Methyl-isoeugenol, a highly attractive male lure for the cucurbit flower pest zeugodacus diversus (coquillett) (syn. bactrocera diversa) (diptera:tephritidae: dacinae). J Econ Entomol. Search in Google Scholar

[6] Deng, L., Wang, Y., Han, Z., & Yu, R. (2018). Research on insect pest image detection and recognition based on bio-inspired methods. Biosystems Engineering, 169, 139-148. Search in Google Scholar

[7] Huijuan, L., Lu, W., & Junbao, W. (2023). Observations on neurophysiological pattern and behavioural traits as death-feigning mechanism in eucryptorrhynchus scrobiculatus (coleoptera: curculionidae). Journal of Experimental Biology(20), 20. Search in Google Scholar

[8] Allison, J. D., & Redak, R. A. (2017). The impact of trap type and design features on survey and detection of bark and woodboring beetles and their associates: a review and meta-analysis*. Annual Review of Entomology, 62(1), 127-146. Search in Google Scholar

[9] Guillem-Amat, AnaSanchez, LucasLopez-Errasquin, ElenaUrena, EnricHernandez-Crespo, PedroOrtego, Felix. (2020). Field detection and predicted evolution of spinosad resistance in ceratitis capitata. Pest Management Science, 76(11). Search in Google Scholar

[10] Antonio López, Ruiz, P., Vicent Yusà, & Clara Coscollà. (2021). Methodological aspects for the implementation of the air pesticide control and surveillance network (pestnet) of the valencian region (spain). Atmosphere, 12(5). Search in Google Scholar

[11] Shrestha, S., Topbjerg, H. B., Ytting, N. K., Skovg?Rd, H., & Boelt, B. (2018). Detection of live larvae in cocoons of bathyplectes curculionis (hymenoptera: ichneumonidae) using visible/near‐infrared multispectral imaging. Pest Management Science. Search in Google Scholar

[12] Lemke, E., Herk, W. G., Singleton, K., Saguez, J., Fowler, G., & Pepper, D., et al. (2023). Mixed sex pheromone lures for combined captures of agriotes and limonius pest click beetles in north america. Journal of Applied Entomology. Search in Google Scholar

[13] Rustia, Dan Jeric ArcegaChao, Jun-JeeChiu, Lin-YaWu, Ya-FangChung, Jui-YungHsu, Ju-ChunLin, Ta-Te. (2021). Automatic greenhouse insect pest detection and recognition based on a cascaded deep learning classification method. Journal of Applied Entomology, 145(3). Search in Google Scholar

[14] Koralewski, T. E., Hsiao-Hsuan, W., Grant, W. E., Brewer, M. J., Elliott, N. C., & Westbrook, J. K., et al. (2020). Integrating models of atmospheric dispersion and crop-pest dynamics: linking detection of local aphid infestations to forecasts of region-wide invasion of cereal crops. Annals of the Entomological Society of America(2), 2. Search in Google Scholar

[15] Crawley, S. E., & Borden, J. H. (2021). Detection and monitoring of bed bugs (hemiptera: cimicidae): review of the underlying science, existing products and future prospects. Pest Management Science. Search in Google Scholar

[16] Liu, L., Xie, C., Wang, R., Yang, P., & Wang, F. (2020). Deep learning based automatic multi-class wild pest monitoring approach using hybrid global and local activated features. IEEE Transactions on Industrial Informatics, PP(99), 1-1. Search in Google Scholar

[17] Yadav, S., & Kumar, V. (2022). A prey-predator model and control of a nematodes pest using control in banana: mathematical modeling and qualitative analysis. International journal of biomathematics(1), 15. Search in Google Scholar

[18] Xu, Q., Liu, H., Yuan, P., Zhang, X., & Qingqing Chen…. (2017). Development of a simplified rt-pcr without rna isolation for rapid detection of rna viruses in a single small brown planthopper ( laodelphax striatellus fallén). Virology Journal, 14(1). Search in Google Scholar

[19] Guo, Q., Wang, C., Xiao, D., & Huang, Q. (2023). Automatic monitoring of flying vegetable insect pests using an rgb camera and yolo-sip detector. Precision Agriculture. Search in Google Scholar

[20] Dimililer, K., & Zarrouk, S. (2017). Icspi: intelligent classification system of pest insects based on image processing and neural arbitration. Applied Engineering in Agriculture, 33(4), 453-460. Search in Google Scholar