Detection and Classification of Paddy Crop Disease using Deep Learning Techniques
Usha Kiruthika1, Kanagasuba Raja S2, Jaichandran R3, Priyadharshini C4

1Usha Kiruthika, SRM Institute of Science and Technology, Kattankulathur Campus, Chennai. Tamil Nadu, India.
2Kanagasuba Raja S, Department of IT, SRM Eswari Engineering College, Chenaai, Tamil Nadu, India.
3Jaichandran R*, Department of CSE, Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation (Deemed to be University), Rajiv Gandhi Salai, Old Mamallapuram Road, Paiyanoor, Kanchipuram (DT), (Tamil Nadu), India.
4Priyadharshini C, Department of Information Technology, Easwari Engineering College, Ramapuram Campus, Chennai.

Manuscript received on 16 August 2019. | Revised Manuscript received on 23 August 2019. | Manuscript published on 30 September 2019. | PP: 4353-4359 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5506098319/2019©BEIESP | DOI: 10.35940/ijrte.C5506.098319
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Abstract: Agricultural production plays a vital role in Indian economy. The biggest menace for a farmer is the various diseases that infect the crop. Quality and high production of crops is involved with factors like efficient detection of diseases in the crop. The disease detection though Naked-eye observation of expert can be prohibitively expensive and requires meticulous and scrupulous analysis to detect the disease. The existing systems on disease detection is not efficient enough in terms on real time basis. This paper presents an effective method for identification of paddy leaf disease. The proposed approaches involves pre-processing of input image and the paddy plant disease type is recognized using Gray-Level Co-occurrence Matrix (GLCM) technique and classifiers namely Artificial Neural Networks is used for better accuracy of detection. This method will be very useful to farmers to detect paddy diseases beforehand and thus prevent over usage of pesticides which in turn affects the crop production.
Keywords: Paddy Disease Detection, Artificial Neural Networks, Grey Level Co-Occurrence Matrix.

Scope of the Article: Classification