在印度北方邦优化土豆产量预测:对机器学习模型的比较分析
Ahmad Alsaber1, Anurag Satpathi2, Mariam Alsabah3
1Department of Management, College of Business and Economics, American University of Kuwait, 15 Salem Al Mubarak St., Salmiya, Kuwait. aalsaber@auk.edu.kw.
Scientific reports
|July 24, 2025
概括
准确的土豆产量预测对于粮食安全至关重要. 人工神经网络 (ANN) 模型在预测印度北方邦的土豆产量时显示出超过98%的准确性,优于其他机器学习方法.
科学领域:
- 农业科学 农业科学
- 机器学习应用 机器学习应用
- 数据科学在农业中的应用
背景情况:
- 马是全球粮食安全和减少贫困的关键主食.
- 印度是全球领先的土豆生产国,因此准确的产量预测至关重要.
- 可持续的农业和食品供应链管理需要精确的产量预测.
研究的目的:
- 为了比较五个机器学习模型的性能,用于土豆产量预测.
- 确定最有效的机器学习方法来预测印度北方邦的土豆产量.
- 提供准确的预测,用于在粮食供应和资源优化方面进行主动规划.
主要方法:
- 收集了16年的 (2005-2021) 时间序列数据关于土豆产量和天气变量在北方邦的七个地区.
- 应用数据减值和天气指数处理.
- 训练并验证了五种机器学习模型:弹性网络 (ELNET),随机森林,人工神经网络 (ANN),极端梯度增强 (XGBoost) 和支持向量回归 (SVR),使用70%的训练和30%的测试数据分割.
主要成果:
- 人工神经网络 (ANN) 模型表现出卓越的性能,实现了最高的R2值和最低的错误指标.
- 模型的性能排名是:ANN > XGBoost > 随机森林 > ELNET > SVR.
- 该ANN模型在预测研究区未来的土豆产量时达到98%以上的准确性.
结论:
- 人工神经网络 (ANN) 是研究区域土豆产量预测最可靠的模型.
- 根据当地农业条件定制机器学习模型可以显著提高预测的准确性.
- 准确的产量预测使粮食安全,市场稳定和农业资源管理的积极规划成为可能.
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