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使用混合分类器与物联网传感器结合使用葡萄病攻击的早期预测

Apeksha Gawande¹, Swati Sherekar¹, Ranjit Gawande²

¹Department of Computer Science & Engineering, Sant Gadge Baba Amravati University, Amravati, Maharashtra, India.

|October 10, 2024

概括

机器学习和物联网 (IoT) 可以使用环境数据预测葡萄植物疾病. 这项研究在识别诸如粉状菌和菌等疾病方面取得了很高的准确性.

科学领域:

农业技术农业技术
机器学习机器学习
物联网 (IoT) 的物联网 (IoT) 的物联网.

背景情况:

环境因素显著影响葡萄庄稼的质量和寿命.
精准农业和智能环境越来越多地利用物联网进行数据收集.
准确监测温度,湿度和叶子湿度对于葡萄种植至关重要.

研究的目的:

探索机器学习在农业中的应用,用于葡萄植物疾病预测.
调查物联网在监测影响葡萄种植的环境条件方面的有效性.
开发和评估一种用于分类葡萄植物疾病的系统.

主要方法:

利用机器学习算法进行葡萄疾病分类.
使用传感器开发了一个自己创建的天气参数数据库 (5个类别的10,000条记录).
通过物联网设备监测关键的环境因素,包括温度,湿度和叶子湿度.

主要成果:

对于特定的葡萄疾病实现了高预测准确度:98.25%的粉状菌,98.85%的状菌,93.95%的细菌叶斑.
证明了开发的数据集和机器学习模型在疾病识别中的有效性.
验证了环境数据在疾病预测中的重要性.

结论:

与物联网实践集成的机器学习为农业葡萄病预测提供了强大的解决方案.
开发的系统提供了准确可靠的疾病分类,帮助农民在作物管理.
持续监测环境参数对于保持葡萄的质量和产量至关重要.

关键词:

农业农业农业农业疾病分类疾病分类.机器学习是机器学习.天气数据库天气数据库

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