• C.K.SAMPOORNA,
• Dr. K. RASADURAI

Agriculture is the major sector in India. About 58% of the rural livelihood influenced by in agriculture. Out of which tomato is one of the common food crops in India. Due to which detection of disease on tomato plant becomes important because less susceptibility. The plants productivity gets affected if proper care is not taken. Image processing is one of upbringing technology which is helping to resolve such issues with various algorithms and techniques. Most of the diseases of tomato plant detected at initial stages as they affects leaves first. By detecting the diseases at initial stage on leaves will surely avoid impending loss. In this project four key diseases are identified using image segmentation,convolutional neural networks and Multi-class Support Vector Machine (SVM) algorithm. For parting of damaged area on leaves image segmentation is used and for classification of accurate disease Multi-class SVM algorithm is used. In last stage symptoms and treatment to detected diseases recommended user. Automatic leaf disease detection is the very important research topic in agriculture field as it may prove benefits in monitoring and controlling large fields of crops and thus automatically detect diseases as soon as they appear on plant leaves. For excellent use of pesticide and to minimize the economical loss, the identification of disease severity is main factor. The disease is usually used for destruction of live plants. This project provide description of leaf disease detection using image processing that can recognize problems in crops from images, based on colour, texture and shape to automatically detect diseases and give the fast and accurate solutions to the farmer. The methods studies are for improve throughput and it will reduce subjectiveness which is arising from human experts in detecting the leaf disease. Digital image processing is a main technique which is used for Enhancement of the image. To upgrade agricultural products automatic detection of disease symptoms is beneficial. The design and implementation of Otsu segmentation technologies are totally automatic and it will greatly aid in selective chemical application, reducing costs and thus leading to increased productivity, as well as improved produce. In the context of Smart farming, I address the challenge of integrating Internet of Things with Raspberry pi and sensors with image processing to improve the efficiency of the agriculture.

IOT, Green House, Raspberry Pi, Soil moisture, UV Sensor, Humidity Sensor, K-means, SVM and CNN classifier.

"TOMATO PLANT LEAF DISEASE DETECTION USING K-MEANS AND SVM CLASSIFIER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.250-256, June 2019, Available :http://www.jetir.org/papers/JETIR1907D39.pdf

"TOMATO PLANT LEAF DISEASE DETECTION USING K-MEANS AND SVM CLASSIFIER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp250-256, June 2019, Available at : http://www.jetir.org/papers/JETIR1907D39.pdf