在智能农业中基于机器学习的最佳作物选择系统
Sita Rani1, Amit Kumar Mishra2, Aman Kataria3
1Department of Computer Science and Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab, 141006, India.
Scientific reports
|September 25, 2023
概括
本研究介绍了一种机器学习模型,用于使用天气和土壤数据进行最佳作物选择. 它利用长期短期记忆循环神经网络 (LSTM RNN) 进行天气预测和随机森林分类器进行作物选择,改善农业产量预测.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 环境科学 环境科学
背景情况:
- 农作物种植高度依赖于区域气候模式,因此农业气候分析对于最大限度地提高产量至关重要.
- 为特定的土地和季节选择合适的作物对于农业生产率至关重要.
- 机器学习为分析复杂的环境数据提供先进的解决方案,以帮助农业决策.
研究的目的:
- 开发和评估一种基于机器学习的模型,以实现最佳的作物选择.
- 整合天气状况分析和土壤参数,以改善农业规划.
- 通过准确的天气预报和及时播种建议,提高作物产量预测.
主要方法:
- 利用长短期记忆循环神经网络 (LSTM RNN) 准确预测天气条件 (温度和降雨).
- 采用随机森林分类器用于准确的作物选择,资源依赖性评估和最佳播种时间的确定.
- 综合天气数据分析与土壤参数,以创建一个全面的决策模型.
主要成果:
- 在天气预报中,LSTM RNN实现了较低的根平均平方误差 (RMSE):为Min.的5.023%. 暂时,7.28%的马克斯. 临时性和8.24%的降雨量.
- 随机森林分类器的准确性很高:97.235%用于作物选择,96.437%用于资源依赖,97.647%用于播种时间.
- 对于随机森林分类器来说,模型构建时间是有效的,为5.34秒.
结论:
- 拟议的ML模型有效地整合了天气和土壤数据,用于明智的作物选择和农业规划.
- LSTM RNN 和随机森林分类器为农业气候分析和产量优化提供了准确和高效的工具.
- 该模型显示了通过数据驱动的洞察力改善农业实践和提高作物产量的巨大潜力.
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