Disease Identification and Gradation of Fruit and Image Processing Approach

Abstract

Farmers require an automated system to grade Pomegranate fruits rather than a manual system to increase productivity and quality of Pomegranate fruits. Manual grading of fruits does not produce adequate results and requires additional time for disease identification and gradation, as well as the expertise of an expert, making it ineffective. Pomegranate cultivation, as well as reliable fruit quality assessment and disease detection, are critical tasks for farmers and researchers. So, in this research, we established a new technique for detecting diseases and grading them based on color, size, and disease. This work uses Python to offer an image processing-based technique for pomegranate fruit gradation and disease identification. Based on retrieved features, machine learning techniques such as SVM or CNN are used to classify the pomegranate fruit into distinct gradation categories