Food safety, waste reduction, and overall efficiency all depend on the early detection of rotten fruits in supply chains. In this research, a deep learning and computer vision-based AI-powered method for identifying rotting apples is presented. Our model uses convolutional neural networks (CNNs) that have been trained on a variety of fresh and rotting fruit datasets to obtain high classification accuracy. For quick and accurate identification at different supply chain checkpoints, the system combines edge computing and real-time image analysis. In order to improve detection skills, we also investigate the usage of Internet of Things (IoT) sensors and hyperspectral imaging. Results from experiments show that our AI-powered solution performs faster and more accurately than conventional manual inspection techniques. This study demonstrates how AI has the ability to revolution the handling of perishable items, reduce losses, and guarantee improved quality standards in the food sector.
AI, Python, IoT, CNN, Rotten Fruit Detection, Fruit Quality Assessment, Image Processing
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