Open AccessDetection and Classification of Paddy Crop Disease using Deep Learning Techniques
Author(s) -
Usha Kiruthika*,
Kanagasuba Raja S,
Jaichandran R*,
C Priyadharshini
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c5506.098319
Subject(s) - computer science , plant disease , agriculture , crop , identification (biology) , agricultural engineering , artificial intelligence , crop production , gray level , production (economics) , microbiology and biotechnology , agronomy , pixel , engineering , biology , ecology , botany , macroeconomics , economics
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