使用机器学习算法预测豆产量
Azanu Mirolgn Mequanenit1, Aleka Melese Ayalew1, Ayodeji Olalekan Salau2,3
1Department of Information Technology, University of Gondar, Gondar, Ethiopia.
Heliyon
|December 25, 2024
概括
这项研究使用机器学习预测了埃塞俄比亚的豆生产情况. 该Xgboosting算法实现了98.65%的准确性,识别了季节和肥料等关键因素,以改善农业决策.
科学领域:
- 农业经济学 农业经济学
- 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:
- 农业对埃塞俄比亚经济至关重要,豆种植为小农提供了大量收入.
- 准确预测豆产量对于经济规划和支持农业社区至关重要.
研究的目的:
- 开发和评估用于预测埃塞俄比亚豆生产的机器学习模型.
- 通过使用数据驱动的洞察力来确定影响豆产量的关键因素.
主要方法:
- 利用了来自埃塞俄比亚中央统计局的10,273个实例的数据集.
- 使用Python实现并比较Random Forest,渐变增强和Xgboosting算法.
- 评估模型性能与准确性,精度,回忆,F1得分和混矩阵.
主要成果:
- Xgboosting分类器表现出卓越的性能,达到98.65%的测试准确率和99.8%的训练准确率.
- 确定豆生产的关键决定因素是梅赫尔季节,扩展计划使用,肥料应用和肥料类型.
结论:
- 在Xgboosting模型是有效的预测豆生产在埃塞俄比亚.
- 调查结果为农业专家提供了可操作的见解,以加强决策和应对生产挑战.
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