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Daiane das Graças do Carmo¹, Jhersyka da Silva Paes², Abraão Almeida Santos³

¹Department of Plant Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil. daiane.carmo@ufv.br.

Neotropical entomology

|December 5, 2024

概括

现在可以通过人工神经网络 (ANN) 预测玉米 (Dalbulus maidis) 的密度. 该ANN准确预测害虫种群,帮助综合性害虫管理策略.

科学领域:

农业昆虫学农业昆虫学
计算生物学计算生物学
生态生态学生态生态学

背景情况:

玉米叶 (Dalbulus maidis) 对玉米生产构成重大威胁.
准确预测害虫种群对于有效的综合害虫管理至关重要.
预测D. maidis密度的现有方法存在局限性.

研究的目的:

开发和验证一个人工神经网络 (ANN),用于预测玉米田中的Dalbulus maidis密度.
确定影响D. maidis季节动态的关键环境和植物因素.
评估ANN在不同生态场景中的表现.

主要方法:

在巴西收集了两年的气象变量,植物年龄和D. maidis密度的数据.
开发和测试1056个人工神经网络 (ANN).
基于根平均二次误差和与观察到的密度的相关性,选择一个最佳的ANN模型.

主要成果:

一个具有30天时间延迟,六个神经元,物流激活和弹性传播的ANN实现了最佳性能 (RMSE=0.057,R=0.919).
该模型表明,在低密度 (大西洋森林) 和高密度 (巴西热带草原) 条件下,适合D. maidis密度.
玉米植物的年龄,降雨量,平均气温和相对湿度被确定为关键因素.

结论:

人工神经网络 (ANN) 为准确预测D. maidis季节动态提供了一个强大的工具.
开发的ANN可以成为针对D. maidis.的综合虫害管理 (IPM) 计划的宝贵资产.
这种方法提高了农业害虫管理的预测能力.

关键词:

气候气候气候气候气候玉米叶跳 (英语:Corn leafhopper) 是一种在玉米叶跳 (英语:Corn leafhopper) 中的鸟类.机器学习是机器学习.玉米玉米玉米是一种害虫管理害虫管理害虫管理人口生态学人口生态学

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