Authors - R.V. Sai Sriram, A. Srujan, K. Rahul, K. Sathvik, Para Upendar Abstract - Freshness plays a crucial role in determining the quality of fruits and vegetables, directly impacting consumer health and influencing nutritional value. Fresh produce used in food processing industries must go through multiple stages—harvesting, sorting, classification, grading, and more—before reaching the customer. This paper introduces an organized and precise approach for classifying and detecting the freshness of fruits and vegetables. Leveraging advanced deep learning models, particularly convolutional neural networks (CNNs), this method analyzes images of produce. The training and evaluation dataset is large and varied, including diverse fruits and vegetables in various conditions. Freshness is determined by analyzing key features like color, texture, shape, and size. For example, fresh produce typically shows vibrant color and is free from mold or brown spots. Traditional methods for assessing quality through manual inspection and sorting are often slow and error prone. Automated detection techniques can significantly mitigate these challenges. Therefore, this paper proposes an automated approach to freshness detection, which first identifies whether an image shows a fruit or vegetable and then classifies it as either fresh or rotten. The ResNet18 deep learning model is employed for this identification and classification task. It also estimates the size of the fruit/vegetable using OpenCV. The qualitative analysis of this approach demonstrates outstanding performance on the fruits and vegetables dataset.
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