Main Article Content
Abstract
Computerization in the field of agriculture is witnessing a great success in many agricultural aspects including detection of various plant diseases. Focus of almost every country has shifted towards the automation of agriculture to attain preciseness and accuracy and to serve the continuously increasing demand of food. Among the major challenges in agriculture, plant disease detection is a significant factor affecting the outcome of farming. Quality ofvegetables, fruits, legumes and grains is affected by plant disease, and heavy loss in production and subsequently economic loses are observed, so there is requirement of fast and effective plant disease detection and assessment methods. This paper explores the ways in which machine learning models can be applied to improve the process of plant disease detection in early stages to improve grain security and sustainability of agro-ecosystem.