AI-Enabled Fruit Decay Detection

In recent years, the significance of fruit classification has witnessed a remarkable surge across various industries, owing to its pivotal role in delineating fruit species, shaping pricing strategies, and ensuring overall quality, especially concerning the exportation of fresh produce. This study capitalizes on a substantial dataset comprising 5658 meticulously curated fruit images categorized into 10 distinct classes, harnessing the power of convolutional neural network (CNN) models for precise classification endeavors. The deployment of automated classification systems emerges as indispensable in discerning both fresh and rotten fruits accurately, thereby streamlining operational processes, curbing human intervention, and curtailed processing durations and expenses within the agricultural domain. Amongst the array of CNN models scrutinized, InceptionV3 emerged as the epitome of performance, boasting an unparalleled accuracy rate of 97.34%. Moreover, the abstract underscores the cardinal importance of maintaining high standards of fruit and vegetable quality, underscoring their irrefutable link to human health. It accentuates the transformative impact of advancements in computer vision and image processing technologies, elucidating their pivotal role in identifying subtle variations and intricate patterns requisite for meticulous classification. These groundbreaking technological strides herald a new era of heightened efficacy in quality control mechanisms across the agricultural landscape, promising manifold benefits for stakeholders involved.