通过使用机器学习模型的生物物理和图像参数的协同集成,优化病下豆产量预测
R N Singh1,2, P Krishnan3, C Bharadwaj4
1Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi, India.
Scientific reports
|February 5, 2025
概括
病显著降低了小的产量,并影响了作物生物物理参数. 机器学习模型准确地预测了早期的豆产量,标准化差异植被指数 (NDVI) 是一个关键预测指标.
科学领域:
- 农业科学 农业科学
- 植物病理学 植物病理学
- 机器学习 机器学习
背景情况:
- 生物压力,像枯病一样,严重影响作物健康和产量,需要有效的评估和预测工具.
- 对农民和政策制定者来说,早期产量预测对于有效的作物管理和市场规划至关重要.
研究的目的:
- 评估不同病水平对小生物物理参数的影响.
- 开发和评估机器学习模型,用于在枯压力下预测早期豆产量.
主要方法:
- 在三年内进行的实地实验中,使用了85种豆基因型,它们的抗性各不相同.
- 收集热和可见图像,并测量生物物理参数 (LAI,光合作用,透气,口腔导电,RWC,MSI,NDVI).
- 开发机器学习模型,集成图像指数和生物物理数据,用于产量预测.
主要成果:
- 天花板温度与枯严重程度正相关;光合作用,透气,LAI,RWC,MSI和NDVI随着枯的增加显著下降.
- 敏感的小基因型经历了44-69%的产量减少.
- 机器学习模型提供了准确的早期产量预测,准确性在收获接近时得到改善;XGB模型和NDVI是表现最好的.
结论:
- 病严重损害了豆作物的健康和产量潜力.
- 机器学习模型,特别是利用NDVI的XGBoost,对于在枯压力下豆的早期产量预测是有效的.
- 该研究量化了疾病的影响,并证明了遥感和ML在作物管理中的实用性.
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