使用机器学习来预测作物中的含量:对长寿地区的土壤健康和农业用地利用的影响
Liyue Zhang1, Tao Yu2, Guodong Zheng3
1School of Science, China University of Geosciences, Beijing 100083, PR China.
The Science of the total environment
|January 22, 2025
概括
这项研究使用机器学习来预测作物中的 (Se) 含量,建立健康的土壤值. 结果显示,人工神经网络模型准确估计了Se水平,指导了Se丰富的作物种植.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 生物地质化学生物地质化学
背景情况:
- (Se) 对人类健康至关重要,但具有狭窄的生物耐受范围.
- 使用机器学习预测作物Se含量和土壤健康值是一个尚未探索的领域.
- 从长寿地区的作物中了解Se吸收因子对于健康和农业至关重要.
研究的目的:
- 分析影响长寿地区作物Se吸收的因素.
- 应用机器学习模型来预测作物中的Se生物度因子 (BAF).
- 估计健康土壤的Se值,并优化作物种植.
主要方法:
- 在作物中Se吸收的因子分析.
- 机器学习模型的开发和应用,包括人工神经网络 (ANN).
- 预测Se BAF和估计米和玉米中的Se含量.
主要成果:
- 该ANN模型显示Se BAF的优异预测性能.
- 已确定的Se度范围:大米 (0.030.17毫克/公斤),玉米 (0.040.25毫克/公斤).
- 确定68%的耕地适合种植富含Se的作物.
结论:
- 机器学习可以有效地预测作物Se含量,并为土壤健康评估提供信息.
- 对于大米和玉米等主要作物,定义了允许的Se度范围.
- 结果为优化Se丰富作物种植和土地利用规划提供了见解.
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