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通过整合作物现象模型和机器学习来预测中国各地的米现象学

Jinhan Zhang¹, Xiaomao Lin², Chongya Jiang¹

¹National Engineering and Technology Center for Information Agriculture, Engineering Research Center of Smart Agriculture, Ministry of Education, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu 210095, PR China.

The Science of the total environment

|August 18, 2024

概括

结合作物现象学和机器学习的混合模型准确地预测了中国各地的米头和成熟日期. 可解释机器学习 (IML) 确定温度是影响这些预测的关键因素.

科学领域:

农业科学农业科学
数据科学数据科学数据科学
气候科学气候科学

背景情况:

准确的米现象学预测对于粮食安全和气候变化适应至关重要.
传统的作物现象学模型在捕捉复杂的环境相互作用方面存在局限性.

研究的目的:

整合作物现象学模型和机器学习,以提高在中国的米现象学预测.
了解气候和品种因素对使用可解释机器学习 (IML) 的预测准确性的影响.

主要方法:

开发和比较作物现象学,机器学习和混合模型,用于预测大米分类和成熟日期.
利用夏普利添加式解释 (SHAP) 来进行模型预测的可解释性分析.
从1981年至2020年期间,从中国337个地点收集的数据.

主要成果:

混合型号实现了最高的准确性,表现最好的模型 (基于XGBoost) 的RMSE为4.65和5.72天的标题和到期期.
SHAP分析发现温度是影响现象学预测的最重要的气候变量,特别是在极端条件下.
变量的重要性在现象学阶段,种植模式和地理区域之间有所不同,突出显示了区域性.

结论:

混合模型与IML相结合,提供了一个强大的框架,可以提高米现象学预测的准确性.
这种方法为现象变化的驱动因素提供了宝贵的见解,有助于完善作物建模.
这些发现支持农业建模的数据驱动进步,以更好地管理作物和应对气候变化.

关键词:

农作物现象学模型混合动力模型混合动力模型可以解释的机器学习机器学习机器学习大米大米大米大米大米

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