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Green Leaf Disease Detection System for Agriculture using Raspberry Pi
E Vijaya Babu, Y Syamala, K V Balaramakrishna, T. Ramakrishnaiah and Srinivas Talasila
The main aim of this project is to identify the Green and effected leaf with the help of raspberry pi and agricultural sensors. This study addressed a framework for detecting and preventing the transmission of plant disease using Raspberry PI. For image processing, the k means clustering algorithm was used. It has a variety of focus points for use in large harvest ranches, and it naturally distinguishes signs of illness anywhere they appear on plant leaves. The recognition of leaf disease is a key theme in pharmaceutical science since it has the benefits of tracking crops in the field in the form and thereby automatically identifies signs of disease by image processing using a clustering k-means algorithm. The word “disease” describes the form of plant damage that occurs. This paper demonstrates the most effective technique for detecting plant infections using the ideas of image processing and alerting the disease name through e-mail, SMS text, and presenting the disease name on the framework proprietor’s screen monitor. Disease signs may be detected automatically, which is helpful for improving agricultural goods. The chemical application would benefit greatly from fully automated design and deployment of these technologies. Pesticides and other ingredients would be less expensive. Farm production will rise as a result of this. This system has usb camera, temperature, humidity sensor, fertilizer, GSM and Raspberry pi. All these sensors are interfaced to the raspberry pi. This system continuously monitors the all the environment conditions and display on monitor. If effected leaf detected at any place, then this system automatically sprays the fertilizer on crop. So that this system makes better and user friendly system for formers.
Keywords: Green Leaf, agriculture, plant disease monitoring system, Raspberry pi