Enhancing the Accuracy of Fruit Freshness Detection by Utilizing Transfer Learning and Customizing Convolutional Neural Network(CNN)

Food quality and safety are paramount concerns in our modern world and perishable goods, especially fruits and vegetables, stand at the intersection of these concerns. The ability to accurately determine the freshness of these products not only impacts food safety but also holds the key to reducing waste in our food supply chain. To enhance the mean lifespan of humans, it is imperative to eradicate the potential for infectious illnesses. The majority of a high-risk community's diet consists of fruits and vegetables. Consequently, differentiating spoiled fruits from viable ones is critical for their preservation. Automation technology is an indispensable component of daily existence. The principal source of wealth is agriculture in the modern world. Daily growth is observed in the sales volume of fresh produce. People who prioritize their health select only high-quality, nutritious fresh fruits. In this paper, we present a novel approach that leverages state-of-the-art artificial intelligence and computer vision techniques, including Convolutional Neural Networks (CNN), ResNet50, VGG16 and InceptionV3 to tackle the challenge of assessing the quality of fruits and vegetables. By automating the evaluation process, our method goes beyond the traditional, subjective, and time-consuming ones. Using the power of deep learning, we present a complete framework that can tell with unprecedented accuracy whether a wide range of produce items are fresh and safe to eat.