Leveraging ML Techniques for Image-based Freshness Index Prediction of Fruits and Vegetables

Abstract

Freshness is a prime factor of consideration when purchasing consumables like fruits and vegetables. Studies have proven that Computer Vision can be successfully involved in classifying fresh and stale fruits and vegetables and measuring their freshness to some extent. This work attempts to determine and analyze the freshness of fruits and vegetables from their images by proposing a Machine Learning methodology. The entire study was divided into two steps. The first step focused on obtaining classification between images of fresh and stale fruits and vegetables. For this, we trained the ConvNeXt model on an open-source imagery dataset consisting of 12 classes, and it proved efficient by achieving an accuracy of 99.77%. The second step focused on analyzing how fresh a particular fruit or vegetable is from its image. We achieved this by using an open-source dataset of tomato images and extracting features specific to the texture, shape, and colour from these images. Further, we trained classification models on these extracted features and presented the results as quantitative measures with scores of 10 for each of these three factors. Thus, we attempted to achieve an in-depth freshness analysis by grading the images based on these three critical factors while defining freshness.