气候和疾病:用先进的预测模型解决咖啡棕眼斑
Lucas Eduardo de Oliveira Aparecido1, Rafael Fausto de Lima2, Guilherme Botega Torsoni2
1Federal Institute of Sul de Minas Gerais, Muzambinho, Brazil.
Journal of the science of food and agriculture
|February 13, 2024
概括
使用农气数据和机器学习模型,可以提前一周预测咖啡棕眼斑. 这些工具有助于在咖啡种植园中控制这种重要的真菌病.
科学领域:
- 农业科学 农业科学
- 植物病理学 植物病理学
- 数据科学数据科学数据科学
背景情况:
- 气候显著影响宿主-病原体相互作用,这在农业中至关重要.
- 像Cercospora coffeicola (棕眼斑) 这样的真菌病严重影响咖啡产量.
- 准确的疾病预测对于有效的作物管理至关重要.
研究的目的:
- 开发和评估用于预测咖啡棕眼点的机器学习模型.
- 预测疾病发病率至少在发病前一周.
- 在巴西主要的咖啡生产地区空间预测疾病的严重程度.
主要方法:
- 使用了农气数据 (温度,湿度,降雨量等). 来自多个地点.
- 使用了六种机器学习模型:KNN,MLP,SVM,RF,XGBoost和GradBOOSTING.
- 纳入了Penman-Monteith和Thornthwaite和Mather的水平衡计算方法.
主要成果:
- XGBoost模型在高收益条件下表现出色 (R2=0.77),而SVM在低收益场景中表现更好 (精度=0.76).
- 疾病发病率在高产和低产条件之间以及不同地区之间有很大差异.
- 预测准确度受到两年一度的咖啡生产周期的影响.
结论:
- 农业气象学变量和机器学习模型有效地预测了棕色眼斑发病率,带有7天的预测时间.
- 这些预测模型为管理咖啡棕眼斑提供了有价值的工具.
- 该研究强调了数据驱动方法在农业疾病管理中的潜力.
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