Cotton Leaf Disease Recognition System for Agricultural Crop Health Monitoring Using Deep Learning

Abstract: Agricultural production greatly depends on crop yield, production and quality of the crops. For increasing the overall Agricultural Production we have to detect and identify the crop diseases early and manage the diseases using specific types of techniques which are effective and efficient for the development of crop health. If we can't identify the crop diseases at the early stage it effect the income of the crop production. By using the traditional methods we can detect the plant and leaf diseases but it is a time consuming process and not that much efficient. And the traditional methods are suitable for only the small areas of crops, but not suitable for larger area of crops. Traditional methods provide very low accuracy. In this project, we can easily detect the disease at earlier by using Artificial Intelligence especially Deep Learning. The system uses artificial intelligence model to grasp the leaf patterns and checks whether the plant is healthy or not. It classifies the image and detects the disease at the earlier and provides the solution for the disease. The developed model distinguishes the plant leaves that are healthy or not. Comparing to the traditional methods this modern methods like deep learning will pro ide faster and accurate results. And the modern methods are cost effective and reduce the dependent on experts, integration with mobile or web applications. Overall by utilizing these deep learning techniques it helps the farmers to take action immediately, and increases the crop yield and quality and reduces the crop loss and provides sustainable agriculture.