Green Leaf Disease detection Using Raspberry pi

M. Sankar, D. Mudgal, Todkar varsharani jagdish, Nandi wale Geetanjali Laxman, Mane Mahesh Jalinder
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引用次数: 7

Abstract

This paper talked about a framework utilizing raspberry PI to detect and prevent plant disease from spreading. The k means clustering algorithm was used for image analysis. It has numerous focal points for use in vast harvest ranches and in this way distinguishes indications of sickness naturally at whatever point they show up on plant leaves. In pharmaceutical research, the recognition of leaf ailment is essential and a critical theme for research, because it has the advantages of monitoring crops in the field in the form and thus automatically detects symptoms of disease by image processing using an algorithm clustering k - means. The term disease refers to the type of plant damage. This paper gives the best strategy to recognizing plant infections utilizing picture preparing and alarming the ailment brought about by email, SMS and showing the malady name on the framework proprietor's screen display. Automatic detection of symptoms of disease is useful for upgrading agricultural products. Completely automatic design and implementation of these technologies will make a significant contribution to the chemical application. The cost of pesticides and other products will be reduced. This will lead to an increase in farm productivity.
用树莓派检测绿叶病
本文讨论了一个利用树莓派检测和预防植物病害传播的框架。图像分析采用k均值聚类算法。它有许多焦点,用于巨大的收获牧场,这样就可以自然地区分疾病的迹象,无论它们出现在植物叶子上的什么地方。在药物研究中,叶片疾病的识别是必不可少的,也是一个关键的研究主题,因为它具有在田间监测作物的形态,从而通过图像处理使用聚类k - means算法自动检测疾病症状的优点。病害一词指的是植物受损的类型。本文提出了利用图片制作、电子邮件、短信报警以及在框架业主屏幕上显示病害名称等方法识别植物病害的最佳策略。疾病症状的自动检测有助于农产品的升级换代。这些技术的全自动设计和实现将为化工应用做出重大贡献。农药和其他产品的成本将会降低。这将导致农业生产力的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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