使用机器学习方法与植被指数和生长指标相结合,预测Bromus inermis的种子产量
Chengming Ou1, Zhicheng Jia1, Shoujiang Sun1
1Forage Seed Laboratory, College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China.
Plants (Basel, Switzerland)
|April 9, 2024
概括
使用植被指数和叶子含量,可以预测光滑的草种子产量. 随机森林模型显示高准确度,叶子中的含量是关键预测因素.
科学领域:
- 农业科学 农业科学
- 农业学是一种农业学.
- 遥感 遥感 遥感 遥感
背景情况:
- 平滑的草 (Bromus inermis) 是一种有价值的料草,但其种子产量是可变的.
- 准确的种子产量预测对于优化生产和减轻风险至关重要.
研究的目的:
- 开发和评估平滑草种子产量的预测模型.
- 通过遥感和植物生理学数据,确定用于种子产量预测的关键指标.
主要方法:
- 在两年 (2022-2023) 期间,使用不同含量的 (0, 100, 200 kg·N·ha-1) 实地试验.
- 在关键生长阶段收集遥感数据 (RVI,NDVI) 和植物生理数据 (LNC,LAI).
- 应用多重线性回归 (MLR),支向量机 (SVM) 和随机森林 (RF) 模型用于种子产量预测.
主要成果:
- 在2022年的植被指数,LNC,LAI和种子产量之间发现了显著的正相关性,特别是在头部阶段.
- 随机森林模型使用2022年的数据实现了最高的预测准确度 (R2 = 0.75),叶子中含量被确定为关键预测因素.
- 在预测2023年收益率时,模型准确性下降,表明需要进一步改进.
结论:
- 遥感和植物生理学数据,特别是头部阶段的叶子含量,可以有效地预测平滑的草种子产量.
- 机器学习模型,特别是随机森林,显示出对产量预测的希望.
- 未来的研究应该包含更广泛的数据集和天气数据,以提高模型的准确性和适应性.
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