利用机器学习算法识别叶片病害以改进农业系统

Pub Date : 2023-01-01 DOI:10.12720/jait.14.1.122-129
Keerthi Kethineni, G. Pradeepini
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引用次数: 3

摘要

植物病害诊断是在复杂环境下有效、准确防治植物病害的基础。智能农业是农业系统中快速发展的过程之一,植物病害识别是帮助农民的主要方法之一。处理后的数据保存在数据库中,用于提前支持、分析植物并帮助制定作物计划。植物是避免全球变暖的重要资源之一。然而,诸如稻瘟病、溃疡病、黑斑病、褐斑病和细菌性叶片等疾病会损害植物。本文开发了图像处理集成技术,通过存储处理后的数据,识别病害类型,实现对所有叶片批次的自动检测。在一些地方,农民不认识专家,也没有适当的设施。在这种条件下,有一种技术可以帮助跟踪和监测更多的作物。这项技术使检测疾病变得更加容易和便宜。机器学习可以提供一种检测疾病的方法和算法。应该对所有类型的叶子图像进行训练,包括健康和疾病叶子图像。本文采用五阶段检测方法。这些阶段包括预处理、使用k-Mean进行分割、特征提取、使用Firefly优化算法(FA)进行特征优化以及使用支持向量机(SVM)进行分类。该方法的准确率为91.3%,灵敏度为90.72%,特异度为91.88,精密度为92%。利用matlab软件工具对结果进行了评价。
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Identification of Leaf Disease Using Machine Learning Algorithm for Improving the Agricultural System
Diagnosing plant disease is the foundation for effective and accurate plant disease prevention in a complicated environment. Smart farming is one of the fast-growing processes in the agricultural system, with the identification of disease in plants being a major one to help farmers. The processed data is saved in a database and used in making decisions in advance support, analysis of plants, and helps in crop planning. Plants are one of the essential resources for avoiding global warming. However, diseases such as blast, canker, black spot, brown spot, and bacterial leaf damage the plants. In this paper, image processing integration is developed to identify the type of disease and help automatically inspect all the leaf batches by storing the processed data. In some places, farmers are unaware of the experts and do not have proper facilities. In such conditions, one technique can be beneficial in keeping track and monitoring more crops. This technique makes it much easier and cheaper to detect disease. Machine learning can provide a method and algorithm to detect the disease. There should be training in images of all types of leaves, including healthy and disease leaf images. Five-stage detection processes are done in this paper. The stages are preprocessing, segmentation using k-Mean, feature extraction, features optimization using Firefly optimization Algorithm (FA), and classification using Support Vector Machine (SVM). The accuracy rate achieved using the proposed technique, i.e., GA-SVM is 91.3%, sensitivity is 90.72%, specificity 91.88, and precision is 92%. The results are evaluated using the matlab software tool.
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