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使用人工智能来预测主要农作物中主要害虫的季节性动态

Lucas de Paulo Arcanjo¹, Jhersyka da Silva Paes¹, Poliana Silvestre Pereira²

¹Department of Entomology, Federal University of Viçosa, Viçosa, 36570-900, Minas Gerais, Brazil.

International journal of biometeorology

|June 30, 2025

概括

人工智能模拟了大豆作物中白害虫Bemisia tabaci的季节性动态. 这种方法可以准确预测害虫种群,有助于及时保护作物,并最大限度地减少产量损失.

科学领域:

农业昆虫学农业昆虫学
计算生物学计算生物学
机器学习应用机器学习应用

背景情况:

白 (Bemisia tabaci) 是一种严重的害虫,会影响大豆等关键作物,造成巨大的经济损失.
有效的害虫防治依赖于了解和预测B. tabaci种群的季节性动态.
大豆是全球动物蛋白质的重要来源,使其对害虫的保护对粮食安全至关重要.

研究的目的:

开发和验证人工神经网络 (ANN) 模型,用于预测大豆作物中Bemisia tabaci的季节性动态.
确定影响白种群波动的关键环境和作物相关因素.
在农业中利用人工智能进行主动的害虫管理策略.

主要方法:

在两年内从100个田里收集了关于白密度,气候变量 (温度,降雨量,风速,大气压) 和大豆年龄的数据.
用于对特征选择的相关性分析和生物相关性.
调查了各种ANN架构,根据皮尔森相关性和根平均平方误差选择了表现最佳的模型.
分析的特征的重要性,以了解不同因素对害虫种群的影响.

主要成果:

选择了一个具有五个输入 (大豆年龄,平均温度,降雨量,风速,大气压) 和四个隐藏神经元的ANN模型.
平均温度和风速被确定为预测成年白种群的最关键特征.
该模型在适应和预测观察到的白密度方面表现出高度准确性.
开发的模型可以提前七天预测白种群.

结论:

人工智能,特别是ANN,为模拟农业系统中复杂的害虫动态提供了强大的工具.
准确的季节性预测Bemisia tabaci能够及时和有针对性的干预,减少作物损害和经济损失.
这种人工智能驱动的方法通过优化对大豆生产的害虫控制策略来增强可持续农业.

关键词:

甘氨酸最大值 (Glycine max) 是一种一个年龄,一个年龄.人工智能的人工智能是人工智能.预测预测预测预测

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