{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:16:44Z","timestamp":1771373804115,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,5,26]],"date-time":"2024-05-26T00:00:00Z","timestamp":1716681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["AP 19675312"],"award-info":[{"award-number":["AP 19675312"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>This study investigates the application of different ML methods for predicting pest outbreaks in Kazakhstan for grain crops. Comprehensive data spanning from 2005 to 2022, including pest population metrics, meteorological data, and geographical parameters, were employed to train the neural network for forecasting the population dynamics of Phyllotreta vittula pests in Kazakhstan. By evaluating various network configurations and hyperparameters, this research considers the application of MLP, MT-ANN, LSTM, transformer, and SVR. The transformer consistently demonstrates superior predictive accuracy in terms of MSE. Additionally, this work highlights the impact of several training hyperparameters such as epochs and batch size on predictive accuracy. Interestingly, the second season exhibits unique responses, stressing the effect of some features on model performance. By advancing our understanding of fine-tuning ANNs for accurate pest prediction in grain crops, this research contributes to the development of more precise and efficient pest control strategies. In addition, the consistent dominance of the transformer model makes it suitable for its implementation in practical applications. Finally, this work contributes to sustainable agricultural practices by promoting targeted interventions and potentially reducing reliance on chemical pesticides.<\/jats:p>","DOI":"10.3390\/make6020054","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T10:27:59Z","timestamp":1716805679000},"page":"1154-1169","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Fine-Tuning Artificial Neural Networks to Predict Pest Numbers in Grain Crops: A Case Study in Kazakhstan"],"prefix":"10.3390","volume":"6","author":[{"given":"Galiya","family":"Anarbekova","sequence":"first","affiliation":[{"name":"Department of Computer Science, S. Seifullin Kazakh Agrotechnical Research University, Astana 010011, Kazakhstan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6716-5115","authenticated-orcid":false,"given":"Luis Gonzaga Baca","family":"Ruiz","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Granada, 18014 Granada, Spain"}]},{"given":"Akerke","family":"Akanova","sequence":"additional","affiliation":[{"name":"Department of Computer Science, S. Seifullin Kazakh Agrotechnical Research University, Astana 010011, Kazakhstan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7267-3261","authenticated-orcid":false,"given":"Saltanat","family":"Sharipova","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Astana Information Technology University, Astana 010000, Kazakhstan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0100-1008","authenticated-orcid":false,"given":"Nazira","family":"Ospanova","sequence":"additional","affiliation":[{"name":"Department of Computer Science, S. Toraighyrov Pavlodar State University, Pavlodar 140008, Kazakhstan"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Anderson, J.A., Ellsworth, P.C., Faria, J.C., Head, G.P., Owen, M.D., Pilcher, C.D., Shelton, A.M., and Meissle, M. (2019). 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