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使用机器学习的模型,根据土壤和叶子的化学特性预测咖啡产量

Rafael de Oliveira Faria¹, Aldir Carpes Marques Filho¹, Lucas Santos Santana²

¹Agricultural Engineering Department, Federal University of Lavras, Lavras, Brazil.

Journal of the science of food and agriculture

|February 7, 2024

概括

机器学习使用植物和土壤数据准确预测咖啡产量. 这种精准农业方法优化了种植,并增加了农民的作物可持续性.

科学领域:

农业科学农业科学
机器学习应用机器学习应用
农业学是一种农业学.

背景情况:

咖啡种植是一个重要的全球经济驱动力,需要有效的管理实践.
精密咖啡种植通过数据驱动的作物管理来提高产量和可持续性.
准确的产量预测对于优化资源分配和农场利能力至关重要.

研究的目的:

开发和评估用于预测咖啡产量的机器学习模型.
确定影响咖啡产量预测的关键土壤和植物属性.
评估土壤化学分析在精密咖啡种植模型中的有用性.

主要方法:

在两个季节内从54.6公的咖啡田中收集了关于土壤和植物属性的数据.
应用机器学习方法来根据收集的属性预测咖啡产量.
监控的叶子分析图和由土壤属性图指导的各种受精率.

主要成果:

机器学习模型在预测咖啡产量方面表现出高效率.
最好的预测模型获得了0.86.6的皮尔森相关系数.
发现土壤的化学属性是无干扰的,这表明它们可以被遗漏在预测模型中.

结论:

该研究成功开发了准确的机器学习模型来预测咖啡产量.
这些发现支持整合精准农业技术,以优化咖啡种植.
结果为生产者和研究人员提供了有价值的见解,旨在最大限度地提高咖啡产量和可持续性.

关键词:

双年收益率是两年一次的收益率.咖啡种植的咖啡种植.数字农业的数字农业精准农业精准农业精准农业

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