山地农业智慧茶园土壤墒情监测数据集

通过传感器设备持续采集土壤环境参数，并结合预处理技术（如去噪、标准化）后利用机器学习算法构建模型。采用回归分析预测未来土壤墒情变化趋势，同时结合深度学习算法对多变量数据进行分类和行为识别，实现精准的土壤墒情监测与预警功能。

Soil environmental parameters are continuously collected via sensor devices. Following preprocessing operations including denoising and standardization, machine learning algorithms are utilized to develop predictive models. Regression analysis is adopted to forecast future change trends of soil moisture, while deep learning algorithms are integrated to conduct classification and behavior recognition on multi-variate data, thus realizing accurate soil moisture monitoring and early warning functions.

搜集汇总

背景与挑战

背景概述

该数据集由贵州省灵峰科技产业园有限公司自行产生，包含50条山地茶园土壤环境数据，更新周期为年，主要用于监测土壤湿度、温度和pH值等多维度参数，通过机器学习算法实现精准灌溉和施肥决策支持。