改进了基于SVM的土壤水分含量预测模型,用于茶叶种植园.
Ying Huang1,2
1Electronic Information School, Wuhan University, Wuhan 430072, China.
Plants (Basel, Switzerland)
|June 28, 2023
概括
一个改进的支向量机器 (SVM) 模型准确地预测了茶叶种植园中的土壤水分含量. 这种方法提高了灌效率和作物产量,即使数据有限.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 准确地预测土壤水分含量 (SMC) 对茶叶种植园灌和生产率至关重要.
- 传统的SMC预测方法是昂贵且劳动密集的.
- 现有的用于SMC预测的机器学习模型往往缺乏足够的数据.
研究的目的:
- 开发一个改进的基于支向量机 (SVM) 的模型,用于精确地预测茶叶种植园中的土壤水分含量 (SMC).
- 通过结合新的功能和优化SVM性能来解决现有方法的局限性.
- 为了提高预测性能,特别是当现实数据有限时.
主要方法:
- 开发了一个增强的SVM模型,其中包含了SMC预测的新功能.
- 优化了SVM超参数,使用 Bald Eagle 搜索 (BES) 算法.
- 使用了包括土壤湿度测量和环境变量 (降雨量,温度,湿度,土壤类型) 在内的综合数据集.
- 应用特征选择技术来识别最有信息性的变量.
主要成果:
- 与传统的SVM和其他机器学习算法相比,改进的SVM模型在预测土壤水分含量方面表现出卓越的性能.
- 实现了高精度,R2为0.9435,MSE为0.0194和RMSE为0.1392.
- 该模型显示了在不同时间段和地点的稳定性和概括能力.
结论:
- 拟议的基于SVM的模型为茶叶种植园提供及时和准确的土壤湿度预测.
- 能够根据信息进行灌安排和水资源管理,提高茶叶作物的产量.
- 通过优化灌实践,最大限度地减少用水量,减少对环境的影响.
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