Defect Exposure in Vegetables and Fruits using Machine Learning

Defects to agricultural food items like fruits and vegetables, results in decrease of quality and quantity of agricultural consuming items. This directly affects the financial source of agriculturists and the human health. So, exposing to this kind of defects in fruits and vegetables at early stages of developing products leads to minimization in loss of production and quality. The regular or most used day to day approach for defect detection is continuously watching and observation of crop either by farmers or further by some professionals. But, it is very time-consuming method. We have used image processing and classification techniques in machine learning to identify and detect the diseases on agricultural products like vegetables and fruits. The main goal of the system being proposed here is to monitor and detect diseases in fruits and vegetables in their early stages to farmers for a healthy yield. Our System can deliver the most accurate results in a very short time while consuming minimal computational resources.