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通过使用机器学习预测天气不变的土壤参数来提高农业自动化
Monisha Mushtary Uttsha1, A K M Nadimul Haque1, Tahsin Tariq Banna1
1Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh.
Heliyon
|April 11, 2024
概括
这项研究开发了一种廉价的方法,利用天气数据预测土壤水分和温度. 机器学习模型,特别是XGBoost,可以准确预测农业自动化这些关键的土壤参数.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 环境监测 环境监测
背景情况:
- 土壤参数对于作物产量至关重要,但孟加拉国农业自动化研究有限.
- 昂贵的传感器和受控环境阻碍了大规模的土壤参数监测.
- 需要具有成本效益的解决方案来预测土壤水分和温度.
研究的目的:
- 开发一种廉价的方法来预测土壤的湿度和温度.
- 为了建立一个强大的天气参数 (湿度,温度) 和土壤参数之间的关系.
- 探索在农业自动化中使用机器学习的可行性.
主要方法:
- 使用一个自定义的数据集~9000数据点采集使用廉价的传感器在不受控制的农业环境中.
- 应用机器学习模型,包括多层感知器 (MLP) 和随机森林,从环境天气数据中预测土壤参数.
- 使用R平方,根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 评估模型性能.
主要成果:
- XGBoost回归器表现出卓越的性能,实现了土壤湿度的0.93和土壤温度的0.99的R平方得分.
- 该模型显示出非常低的误差:RMSE为0.037和MAE为0.015的土壤水分;RMSE为0.001和MAE为0.0008的土壤温度.
- 在天气和土壤参数之间发现了强烈的相关性,验证了预测方法.
结论:
- 通过随时可用的天气数据,可以准确预测土壤参数 (湿度,温度).
- 这种方法为大规模农业自动化提供了具有成本效益的解决方案,特别是在孟加拉国等地区.
- 这些发现对改善农业实践和资源管理有重大影响.
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