Open AccessSmart Farming: IoT Based Plant Leaf Disease Detection and Prediction using Deep Neural Network with Image Processing
Author(s) -
K. V. Prema,
Carmel Mary Belinda
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.i7707.078919
Subject(s) - agriculture , image processing , field (mathematics) , computer science , artificial neural network , artificial intelligence , feature extraction , automation , precision agriculture , population , deep learning , machine learning , internet of things , data science , image (mathematics) , engineering , geography , computer security , mathematics , environmental health , mechanical engineering , medicine , archaeology , pure mathematics
Agriculture plays a major role in human life. Almost 60% of the population is involved directly or indirectly in some agriculture activity. But Nowadays, farmers have quit agriculture and shifted to other sectors due to less adoption of automation and other reasons like increase in the requirement of agricultural laborers. So, Farmers now largely depend on adoption of cognitive solutions with technological advancements to acquire the benefits. Image processing and Internet of Things jointly produces new dimensions in the field of smart precision farming. This proposed methodology aims to create an approach for plant leaf disease detection based on deep neural network. This approach combines IoT and image processing which runs preprocessing and feature extraction techniques by considering different features such as color, texture, size and performs classification using deep learning model that expands to help identification of plant leaf disease