根据天气驱动的花生价格预测和利用提升算法评估泰米尔纳德州的作物模式的利能力
Kalpana Muthuswamy1, Shrishail Dolli2, Kedar Khandeparkar3
1Office of the Dean (Agriculture), Tamil Nadu Agricultural University, Coimbatore, 641003, Tamil Nadu, India. kalpanam@tnau.ac.in.
Scientific reports
|September 30, 2025
概括
在泰米尔纳德州优化地瓜种植涉及将天气预报与先进的机器学习模型集成在一起. HistGradientBoosting擅长预测价格,指导等有利可图的作物选择,以获得更好的农民回报.
科学领域:
- 农业经济学 农业经济学
- 数据科学数据科学数据科学
- 气候科学 气候科学
背景情况:
- 地瓜 (花生) 种植对于油种子的生产至关重要,但非常容易受到影响价格和利能力的天气变化的影响.
- 准确的价格预测对于优化作物模式和提高农民收入至关重要.
研究的目的:
- 整合天气预报和利能力分析,以优化泰米尔纳德邦的土豆作物种植模式.
- 评估先进的提升算法在使用天气参数预测农作物价格方面的性能.
- 根据预测价格和经济效率,确定最有利可图的作物种植策略.
主要方法:
- 使用了多变量预测模型,包括天气参数 (温度,湿度,降雨) 和高级提升算法 (LightGBM,XGBoost,HistGBM,CatBoost).
- 使用k-平均集群的分离天气数据和使用季节趋势分解 (LOESS) 的分解作物价格.
- 使用预测价格分析了作物模式的利能力,并计算了成本效益比率 (BCR).
主要成果:
- 在价格预测中,HistGradientBoosting表现出卓越的性能,平均绝对误差 (MAE) 最低.
- 与单变量方法相比,多变量模型显著提高了预测准确度.
- 包括洋等高价值作物在内的作物模式实现了最高的BCR (2.18),黑色在经济上优于绿色.
结论:
- 根据天气信息,针对特定地区的作物策略对于最大限度地提高农民的利能力和减轻土豆种植风险至关重要.
- 集成天气数据的先进机器学习模型为农业决策提供了强大的工具.
- 在优化模式下实现高价值作物的多样化,提高了农民的经济可行性.
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