Deep Learning-Based Fruit Detection and Ripeness Assessment

Abstract

Ensuring the quality and freshness of fruits is a critical challenge in modern agriculture and the post-harvest supply chain. Traditional manual methods of fruit detection and ripeness assessment are often labor-intensive, subjective, and prone to inconsistencies, leading to significant post-harvest losses and inefficiencies. In recent years, advancements in artificial intelligence, and intense learning, have shown promising capabilities in automating these processes with high precision and consistency. This paper presents a robust deep learning-based system for automated fruit detection and ripeness classification using image data. The proposed approach employs convolutional neural networks (CNNs) for ripeness classification and a YOLOv5 model for real-time fruit detection. A comprehensive dataset comprising various fruits at different ripeness stages and will be augmented to improve model generalization. The system train and validate using a stratified dataset split, and evaluates the performance using standard metrics such as accuracy, precision, recall, F1-score, and Mean Average Precision (mAP). The results demonstrate the effectiveness of deep learning in accurately identifying fruit types and determining their ripeness stages, offering a scalable solution for smart farming and quality control in the agricultural supply chain.