Automated assessment of fruit freshness is indispensable in the agricultural sector. Traditionally, fruit grading relies on human inspection, a method prone to inconsistency and susceptibility to external influences. Hence, there is a pressing need for a swift, precise, and automated system tailored for industrial applications. This study employs a deep learning approach for fruit freshness classification. The proposed Convolutional Neural Network (CNN) model utilizes the "Fruit Fresh and Rotten for Classification" dataset sourced from Kaggle. This dataset comprises images depicting three varieties of fresh fruits (Apple, Banana, and Oranges) along with their corresponding rotten counterparts. Leveraging deep learning, the CNN model extracts key characteristics or attributes from these fruit images. Subsequently, a softmax function categorize the images into fresh and rotten classes. The performance of the proposed CNN model is evaluated using the dataset, yielding an impressive accuracy of 95%. These results demonstrate the efficacy of the CNN model in fruit classification. Additionally, this study explores four Transfer Learning methods for fruit classification. A comparative analysis of classification performance underscores the superior efficiency of the Convolutional Neural Network model over transfer learning approaches. In this paper, they introduce a novel dataset comprising high-quality images of fruits. Furthermore, they present the outcomes of numerical experiments conducted to train a neural network for fruit detection. Additionally, they elucidate the rationale behind selecting fruits for this project by proposing several potential applications that could benefit from such a classifier.

CNN Agriculture Transfer Learning Models