Open AccessDeep Learning based Model for Plant Disease Detection
Author(s) -
S.V. Kogilavani*,
S. Malliga
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l2585.1081219
Subject(s) - plant disease , convolutional neural network , computer science , agriculture , plant growth , process (computing) , disease , artificial intelligence , deep learning , machine learning , agricultural engineering , microbiology and biotechnology , agronomy , biology , engineering , medicine , ecology , pathology , operating system
Plant disease prediction is vital in Agriculture sector. Farmer's economic growth depends on the quality of the products that they produce, which relies on the plant's growth and the yield they get. Identifying the disease can lead to quicker interventions that can be implemented to reduce the effects of major economic loss. There may be high degree of complexity in diagnosing the various types of diseases in plants through leaves of the plants. Manual mode of plant disease detection is a tedious process. In this proposed work, Convolutional Neural Network methodologies like Sequential model and SmallerVGG model were utilized for detecting diseases in plants and diagnosis using plant leaf image. Among these two models, SmallerVGG model achieved more accuracy rate of 87% than 65% of sequential model.