"智能农业:采用气候驱动的方法来建模和预测使用机器学习算法在玉米中的秋季军种群"
Vani Sree Kalisetti1, Upendhar Sudharshanam2, Nagesh Kumar Mallela Venkata1
1Maize Research Centre, Agricultural Research Institute, Professor Jayashankar Telangana Agricultural University, Hyderabad, India.
Frontiers in plant science
|November 17, 2025
概括
一个新的人工神经网络 (ANNX) 模型准确地预测了秋季军疫情,提前一周. 这种早期预警系统有助于保护玉米作物,并支持可持续农业.
科学领域:
- 农业昆虫学 农业昆虫学
- 计算生物学 计算生物学
- 气候科学 气候科学
背景情况:
- 秋季军虫 (Spodoptera frugiperda) 是一种主要的害虫,由于其适应性和传播,影响了全球玉米生产.
- 准确预测害虫爆发对于有效的作物保护和产量保存至关重要.
研究的目的:
- 开发和评估一周前的预测系统来预测秋季军虫疫情.
- 为了比较人工神经网络与气候输入 (ANNX),支持向量回归与气候输入 (SVRX) 和整数值GARCH与外部变量 (INGARCHX) 模型的性能.
主要方法:
- 从2019-2023年每周的费洛蒙陷计数与天气数据 (温度,湿度,降雨量) 结合起来.
- 实施了三种不同的建模方法 (ANNX,SVRX,INGARCHX),并使用性能指标进行评估,例如平均平方误差和根平均平方误差.
- 模型性能差异的统计学意义使用Diebold-Mariano测试进行了验证.
主要成果:
- 在训练和测试阶段,ANNX模型表现出卓越的性能,与SVRX和INGARCHX相比,实现了较低的平均平方误差和根平均平方误差值.
- ANNX在预测秋季军虫数量激增方面取得了大约80%的准确性,提前一周.
- 在统计学上,ANNX对SVRX和INGARCHX的性能优势是显著的.
结论:
- 开发的ANNX模型作为秋季军虫疫情的可靠预警系统.
- 整合气候变量可以提高神经网络对害虫管理的预测能力.
- 这种预测系统可以及时,有针对性的害虫防治,从而最大限度地减少玉米产量损失,并促进可持续的农业实践.
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