Fruit Grade Classification and Disease Detection using Deep Learning Techniques

Abstract

Ensuring optimal food quality and agricultural productivity hinges on effective fruit quality assessment and disease detection. Introducing a comprehensive strategy employing deep learning techniques to address critical aspects of fruit quality assessment and disease detection in agriculture. The methodology is structured into two distinct phases, each designed to optimize the accuracy and efficiency of the overall system. In the initial phase, image acquisition, preprocessing, and precise Region of Interest (ROI) detection using the Expectation-Maximization (EM) method lay the foundation for fruit classification with the AlexNet architecture. Rigorous training and testing procedures ensure the model's efficacy. The subsequent phase extends the initial process, with a heightened focus on feature extraction facilitated by DenseNet201. Thorough performance analysis, incorporating multiple metrics, assesses the accuracy and effectiveness of the system. This framework aspires to establish a robust solution for automated fruit grading and disease detection. By harnessing the capabilities of deep learning models, the goal is to accurately classify fruits and identify potential diseases, contributing significantly to agricultural practices and food quality management. The anticipated outcomes aim to set the groundwork for future advancements in the agricultural sector, providing a technological solution that enhances efficiency in fruit quality assessment and disease detection, ultimately benefiting food quality and crop yield.