一个新的深度学习框架用于田间小麦产量预测
M Lokeshwari1,2, Girish Kumar Jha3, A Praveenkumar1,2
1The Graduate School, ICAR-Indian Agricultural Research Institute, Pusa, New Delhi, India.
概括
一个新的深度神经网络,通过遗传算法进行优化,使用近距离传感数据准确预测小麦产量. 这种先进的模型超越了传统的作物监测和产量估计方法.
科学领域:
- 农业科学 农业科学
- 机器学习 机器学习
- 遥感 遥感 遥感 遥感
背景情况:
- 近距离传感技术通过光谱植被指数提供快速,非破坏性的作物监测.
- 准确的收割前小麦产量预测对于农业管理和育种计划至关重要.
研究的目的:
- 开发和评估一个基因算法优化的深度神经网络,用于田间小麦产量预测.
- 将拟议的深度学习模型与传统机器学习算法的性能进行比较.
主要方法:
- 利用近距离传感数据,包括规范差异植被指数 (NDVI),树冠温度 (CT) 和植物高度 (PH).
- 开发了一个通过遗传算法 (GA) 优化的深度神经网络 (DNN).
- 在灌和雨水条件下,对3350种不同种类的小麦生殖质进行了培训和验证.
主要成果:
- 在小麦产量预测中,GA优化的DNN显著优于随机森林回归 (RFR),LASSO和SVR.
- 在五个生长阶段进行的NDVI测量显示出强大的预测能力 (R2 ≥60%灌,R2 ≥50%雨水).
- 随机森林回归确定了影响谷物产量预测的关键特征.
结论:
- 使用近距离传感数据的GA优化的DNN为准确的小麦产量预测提供了强大而可扩展的解决方案.
- 这种方法代表了GA优化的DNN与近距离传感用于印度农业作物产量预测的首次应用.
- 该模型支持高效的基因型选择,并通过改进产量估计,为可持续发展目标做出贡献.
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