Machine Learning Based Recognition of Crops Diseases By CNN
K. Naresh1, G. Naga Satish2, K. BhargavRam3, Ch. Srinivasulu4

1Mr. K. Naresh, Assistant Professor, Department of CSE BVRIT HYDERABAD
2Dr. G. Naga Satish, Associate Professor, Department of CSE.  BVRIT HYDERABAD
3Mr. K. Bhargav Ram, Assistant Professor, Department of CSE. BVRIT HYDERABAD
4Dr. Ch. Srinivasulu, Professor, Department of CSE BVRIT HYDERABAD

Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 1264 -1268| Volume-8 Issue-9, July 2019 | Retrieval Number: I8133078919/19©BEIESP | DOI: 10.35940/ijitee.I8133.078919
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Abstract: Machine learning plays a major role from past years in image detection, spam reorganization, normal speech command, product recommendation and medical diagnosis. Present machine learning algorithms used for the identification of crops, their diseases and also provide remedies to them. In the present paper, we discuss the Crops Diseases and remedies. Agro consultant application deals with crops and its diseases, nutritive deficiency indication and providing remedy using machine learning techniques for automated vision system used at agricultural field. The task of identifying plants is time-consuming even for botanists. Real- time identification of crop diseases, nutritive deficiency symptoms, and providing a remedy is an application that will aid users in spotting plant species through their leaves. The system will allow the user to search the database, browse the list of collected leaf samples, and take pictures of leaves and analyze them. Crop diseases are a major hazard to food security, but their swift naming remains not easy in many parts of the world due to the lack of the necessary communications. Using a public dataset of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify crop species, their diseases and also provide remedies for diseased crops, weeds, and damaged pest.
Keywords: Machine learning algorithm, Convolution Neural Network

Scope of the Article: Machine Design