ABSTRACT
Diseases in pomegranate fruit cause devastating problem in economic losses and production in agricultural industry worldwide. In this project, a solution for the detection and classification of pomegranate fruit diseases is proposed and experimentally validated. Our experimental results express that the proposed solution can significantly support accurate detection and automatic classification of pomegranate fruit diseases using Convolutional Neural Network. An early detection of fruit diseases can aid in decreasing such losses and can stop further spread of diseases. A lot of work has been done to automate the visual inspection of the fruits by machine vision with respect to size and color. However, detection of defects in the fruits using images is still problematic due to the natural variability of skin color in different types of disease in fruits, high variance of defect types. To know what control factors to consider next year to overcome similar losses, it is of great significance to analyze what is being observed. This project is developed in python.
PROJECT OUTPUT
PROJECT DEMO VIDEO
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