Leveraging Deep Learning for the Identification and Categorization of Fruit Diseases

The precise assessment of fruit quality and the detection of illnesses are critical tasks in the agriculture sector. One important component that has a direct impact on consumers' physical well-being and inclination to buy is the freshness of fruits and vegetables. Analyzing the degree of freshness in fruits and vegetables is important since it has a big impact on how market prices are set. Fruit and vegetable classification and identification by machine vision is difficult because of their comparable colors, textures, and external environmental elements including illumination, reflections, and complicated backdrops. This study presents a comprehensive overview of methods for detecting and classifying the quality of fruits and plants using several strategies for feature extraction and classification. Several academics have suggested various ways including binarization, histogram-based approaches, Gray Level Co-occurrence matrix, machine learning techniques, and deep learning. The outcome of our study showcases the results obtained from different methodologies, together with their respective benefits and challenges. In conclusion, we will discuss potential areas for improvement and future directions for additional research.