开发一种简单的预测模型,用于在环境增长受限制条件下对牛豆产量进行预测
Yoshihiro Hirooka1, Shuhei Eda2, Kenta Ikazaki3
1Graduate School of Agriculture, Kindai University, Nara, Nara, 631-8505, Japan. hirooka@nara.kindai.ac.jp.
Scientific reports
|November 20, 2024
概括
机器学习模型可以预测牛产量,叶覆盖率是关键因素. 优化特征变量和算法可以提高气候压力农业的预测准确性.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 计算机科学 计算机科学
背景情况:
- 气候变化加剧了环境压力,需要先进的作物产量预测方法.
- 机器学习 (ML) 为产量预测提供了强大的工具,但最佳特征选择和算法准确性需要进一步调查.
- 牛 (Vigna unguiculata) 是苏丹草原的一个重要作物,面临环境限制.
研究的目的:
- 为了确定最佳的特征变量组合来预测牛产量.
- 为了比较牛的主要机器学习算法的预测准确性.
- 探索影响预测错误的环境和植物因素.
主要方法:
- 在苏丹中部萨凡纳进行了牛田间实验.
- 使用四个ML算法和七个特征变量组合开发了28个预测模型.
- 评估支持向量回归 (SVR) 和神经网络 (NN) 算法.
主要成果:
- SVR和NN算法使用连续叶覆盖率证明了有效的牛产量预测.
- 根据土壤类型和牛生长习惯,预测准确度有所不同.
- 与芽生长和植物生理状态相关的特征变量被发现可以最大限度地减少预测错误.
结论:
- 连续的叶覆盖是使用ML的牛产量的强有力的预测指标.
- 算法选择和特定的环境因素 (土壤,生长习惯) 影响预测的准确性.
- 整合芽生长和生理数据可以提高预测模型的稳定性.
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