Scholarlink

<p>The cotton boll weevil <i>Anthonomus grandis grandis</i> Boh. (Coleoptera: Curculionidae) is a destructive pest of cotton and can cause losses of up to 100%. Understanding and predicting the seasonal dynamics of pests makes it possible to plan sampling and apply control methods more efficiently. Artificial neural networks (ANNs) are machine learning tools with high predictive power. Therefore, this work aimed to determine a prediction model for the seasonal dynamics of <i>A. grandis grandis</i> in cotton crops using ANN. We used pest density data obtained in fields located in Brazil that were collected for five years. 1716 ANN were determined. The selected ANN used meteorological data from 20 days before evaluating the pest density, six neurons in the hidden layer, logistic activation function and Rprop learning algorithm. ANN predictions showed a high correlation (Pearson correlation = 0.865) with crop pest densities. Among the ANN predictors, the duration of the reproductive stage, air temperature and rainfall had positive effects on pest density. The opposite occurred with relative air humidity. Thus, the ANN determined in this work is promising to be used to predict the seasonal dynamics of <i>A. grandis grandis</i>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Artificial neural network model for predicting population dynamics of Anthonomus grandis grandis (Coleoptera: Curculionidae) in cotton fields, as a function of climatic elements

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月16日

查看全文
  • Andréa Aparecida Santos Oliveira,
  • Cristina Schetino Bastos,
  • Jhersyka da Silva Paes,
  • Leticia Caroline da Silva Sant’Ana,
  • Tamíris Alves de Araújo,
  • Ana Caroline Alves de Araújo,
  • Apurba Barman,
  • Marcelo Coutinho Picanço

摘要

The cotton boll weevil Anthonomus grandis grandis Boh. (Coleoptera: Curculionidae) is a destructive pest of cotton and can cause losses of up to 100%. Understanding and predicting the seasonal dynamics of pests makes it possible to plan sampling and apply control methods more efficiently. Artificial neural networks (ANNs) are machine learning tools with high predictive power. Therefore, this work aimed to determine a prediction model for the seasonal dynamics of A. grandis grandis in cotton crops using ANN. We used pest density data obtained in fields located in Brazil that were collected for five years. 1716 ANN were determined. The selected ANN used meteorological data from 20 days before evaluating the pest density, six neurons in the hidden layer, logistic activation function and Rprop learning algorithm. ANN predictions showed a high correlation (Pearson correlation = 0.865) with crop pest densities. Among the ANN predictors, the duration of the reproductive stage, air temperature and rainfall had positive effects on pest density. The opposite occurred with relative air humidity. Thus, the ANN determined in this work is promising to be used to predict the seasonal dynamics of A. grandis grandis.