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Comparative Analysis for Predicting Shelf Life of Fruits Using Advanced Deep Learning Approaches

Author(s): Radhika Mishra, Sanath Shenoy


The food industry aims to reduce food waste and ensure the delivery of fresh produce to consumers, making it crucial to predict fruit shelf life accurately. Traditional approaches rely on expensive and time-consuming laboratory testing, which often involves destructive methods. However, recent studies suggested that advanced deep learning techniques can predict fruit shelf life accurately and efficiently. This paper presents a novel approach to predicting fruit shelf life using deep learning models. The study focuses on the application of these advanced techniques to forecast the shelf life of bananas, which can contribute significantly to achieving the food industry’s objective. The study tries to develop accurate and efficient models that could predict the maturity of bananas, based on their average shelf-life and appearance. In order to achieve this objective, two object detection algorithms—Faster R-CNN and You Only Look Once (YOLO) are used and their performance is compared in the present research. The dataset has been created by collecting images of the life cycle of bananas and segregating them based on their maturity. Various preprocessing and augmentation techniques have been applied to enhance the features of the training dataset which is useful to get better accuracy. The algorithms were trained on the family of Cavendish Bananas dataset and were able to predict the shelf life of bananas with better training accuracy. The YOLO algorithm which is known for efficiency is compared with Faster R-CNN well known for identifying very fine features. This study demonstrates the potential of deep learning algorithms in predicting the shelf life of bananas and can be extended to different fruits.

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