整合基于机器学习的推系统,有效预测合适的农作物种植
Mahmudul Hasan1, Md Abu Marjan1, Md Palash Uddin1,2
1Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh.
Frontiers in plant science
|August 28, 2023
概括
这项研究引入了一种新的机器学习模型,即K-Nearest Neighbor Random Forest Ridge Regression (KRR),用于准确预测孟加拉国的作物产量. 该KRR模型显著提高了主要作物的预测准确性,有助于农业规划.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 全球粮食安全受到农业生产增长不足的威胁.
- 由于资源有限,发展中国家在增加粮食生产方面面临挑战.
- 准确的作物生产预测对于有效的农业规划至关重要.
研究的目的:
- 开发一种有效的机器学习模型,用于孟加拉国作物产量预测.
- 为了解决缺乏公开可用的农业数据的问题.
- 为最佳作物选择提出一个推系统.
主要方法:
- 从孟加拉研究机构收集和预处理数据.
- 整体机器学习模型的开发:K-最近邻居随机森林回归 (KRR).
- 评估KRR与传统和集体学习模型相比,使用MSE和R-squared等指标.
主要成果:
- 对于主要作物:澳大利亚大米 (0.009 MSE,99% R2),阿曼大米 (0.92 MSE,90% R2),博罗大米 (0.246 MSE,99% R2),小麦 (0.062 MSE,99% R2) 和土豆 (0.016 MSE,99% R2) 的预测准确度很高.
- 迪博德-马里亚诺测试证实了KRR模型的稳定性和统计学意义.
- 设计了一个推系统,建议适合未来种植的作物.
结论:
- 拟议的KRR模型为作物产量预测提供了强大而准确的解决方案.
- 开发的系统可以帮助农民和农业利益相关者做出明智的决策.
- 这种方法可以有助于提高农业生产率和粮食安全.
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