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Crop plant sickness or climatic changes can bring starvation. Indias more than three-fourth population depends on agriculture.

Rice is grown in nearly every state of India, with West Bengal having its highest production, followed by Uttar Pradesh and Tamil Nadu. India is known for its agriculture as different types of crops are cultivated here. KeywordsRice leaf disease feature extraction SVM KNN CNN multimedia sensors It further identifies the type of rice disease into rice blast, rice blight, brown spots, leaf smut, tungro and sheath blight. In addition, a hardware prototype and model using Convolutional Neural Network (CNN) is proposed that detects rice disease. The survey gives insights of the different techniques used for the identification of rice plant disease. The survey focuses to distinguish different methods based on the classifier used. Papers of the last decade are studied thoroughly, including the work carried on various rice plant diseases, and a survey is presented based on essential aspects. This research paper provides a survey on various techniques and briefly discusses significant aspects of different classifiers and techniques used to detect rice diseases. In the field of agriculture and modern computer era, recognizing infection or diseases using plants' image is one of the keen research areas. Rice cultivation brings employment and also helps to stabilize the Gross Domestic Product (GDP) by its vast contribution. India has the largest area under rice cultivation that includes the cultivation of brown and white rice. Pratikhsa Shetgaonkar Computer Engineering SRIEIT, Goa University Shiroda, IndiaĪbstractRice/Paddy is the staple crop of India. Kunal Deshmukh Computer Engineering SRIEIT, Goa University Shiroda, IndiaĪjay Reddy Computer Engineering SRIEIT, Goa University Shailendra Aswale Computer Engineering SRIEIT, Goa University Shiroda, India Lobhas Verekar Computer Engineering SRIEIT, Goa University Shiroda, India

Tejas Tawde Computer Engineering SRIEIT, Goa University Shiroda, India Rice Plant Disease Detection and Classification Techniques : A Survey