Real-Time Implementation of Fruit Ripeness Detection Using Deep Learning

Fruits play a crucial role in nutrition, and the global economy yet face challenges like post-harvest losses and artificial ripening issues. Advances in Artificial Intelligence (AI), machine learning (ML), and deep learning (DL) are transforming fruit ripeness assessment through non-destructive, real-time evaluation using lightweight models and edge computing platforms like Raspberry Pi. The integration of AI, embedded systems, and IoT aims to address challenges such as data standardization and environmental unpredictability within the agricultural supply chain. In this view, this paper discusses a computer vision system utilizing Raspberry Pi and OpenCV to automate the classification of ripe and unripe fruits. By analysing attributes such as colour, texture, and form across different species, including bananas and apples, the CNN-based approach performs well in classifying ripeness effectively, offering a scalable solution for cost-efficient fruit quality control and enhanced operational efficiency in agriculture.