ArticleInternational journal of biometeorology2022
Forecasting the seasonal dynamics of Trichoplusia ni (Lep.: Noctuidae) on three Brassica crops through neural networks.
Article in International journal of biometeorology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Colonization and Age Structure of Dalbulus maidis Population in Maize Crops During the Vegetative Stage in the Brazilian Cerrado.Neotropical entomology · 2026Article
- Predicting the Seasonal Dynamics of Fruit Fly Anastrepha fraterculus Populations in Apple Orchards Using Artificial Neural Networks.Neotropical entomology · 2025Article
- Moth caterpillar embryos and parasitoid egg infection as revealed in vivo and visualized by micro-CT scanning.Journal of insect science (Online) · 2025Article
- Utilising artificial intelligence for cultivating decorative plants.Botanical studies · 2024Article
- Predicting the seasonal dynamics of Dalbulus maidis (Hemiptera: Cicadellidae) in corn using artificial neural networks.Neotropical entomology · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
The cabbage looper, Trichoplusia ni Hübner (Lep.: Noctuidae), is a destructive pest of Brassica crops. Their larvae defoliate plants, leading to reduced crop yield. Understanding and modeling pest seasonal dynamics is central to management programs because it allows one to set up sampling and control efforts. This study aimed to train, with field-collected data, artificial neural networks (ANN) for T. ni forecasting on Brassica crops. ANNs were used due to their suitability to fit complex models with multiple predictors. Three weather variables (air temperature, rainfall, and relative humidity lagged at different intervals from the day of pest assessment) and three host plants (broccoli, cabbage, and cauliflower) along with another plant-related variable (days after transplanting) were used as input variables to build ANNs with different topologies. Two outputs (T. ni eggs or larvae) were tested to verify which one would yield more precise models. ANNs forecasting T. ni eggs performed better, based on Pearson's correlation (r
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