Artificial neural networks as a tool for seasonal forecast of attack intensity of Spodoptera spp. in Bt soybean.

Autor: de França LC; Limagrain Sementes, Goianésia, Goiás, 76380-000, Brazil., Pereira PS; Department of Entomology, Federal University of Viçosa, Viçosa, Minas Gerais, 36570-900, Brazil., Sarmento RA; Graduate Programme in Biotechnology and Biodiversity, Rede Bionorte, Federal University of Tocantins, Palmas, Tocantins, 77650-000, Brazil., Barreto AB; Department of Entomology, Federal University of Viçosa, Viçosa, Minas Gerais, 36570-900, Brazil. alice.barbutti@ufv.br., da Silva Paes J; Department of Plant Science, Federal University of Viçosa, Viçosa, Minas Gerais, 36570-900, Brazil., do Carmo DDG; Department of Plant Science, Federal University of Viçosa, Viçosa, Minas Gerais, 36570-900, Brazil., de Souza HDD; Graduate Programme in Biotechnology and Biodiversity, Rede Bionorte, Federal University of Tocantins, Palmas, Tocantins, 77650-000, Brazil., Picanço MC; Department of Entomology, Federal University of Viçosa, Viçosa, Minas Gerais, 36570-900, Brazil.; Department of Plant Science, Federal University of Viçosa, Viçosa, Minas Gerais, 36570-900, Brazil.
Jazyk: angličtina
Zdroj: International journal of biometeorology [Int J Biometeorol] 2024 Nov; Vol. 68 (11), pp. 2387-2398. Date of Electronic Publication: 2024 Aug 13.
DOI: 10.1007/s00484-024-02747-w
Abstrakt: Soybean (Glycine max) is the world's most cultivated legume; currently, most of its varieties are Bt. Spodoptera spp. (Lepidoptera: Noctuidae) are important pests of soybean. An artificial neural network (ANN) is an artificial intelligence tool that can be used in the study of spatiotemporal dynamics of pest populations. Thus, this work aims to determine ANN to identify population regulation factors of Spodoptera spp. and predict its density in Bt soybean. For two years, the density of Spodoptera spp. caterpillars, predators, and parasitoids, climate data, and plant age was evaluated in commercial soybean fields. The selected ANN was the one with the weather data from 25 days before the pest's density evaluation. ANN forecasting and pest densities in soybean fields presented a correlation of 0.863. It was found that higher densities of the pest occurred in dry seasons, with less wind, higher atmospheric pressure and with increasing plant age. Pest density increased with the increase in temperature until this curve reached its maximum value. ANN forecasting and pest densities in soybean fields in different years, seasons, and stages of plant development were similar. Therefore, this ANN is promising to be implemented into integrated pest management programs in soybean fields.
(© 2024. The Author(s) under exclusive licence to International Society of Biometeorology.)
Databáze: MEDLINE
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