The proposed method of using a convolutional neural network (CNN) for fruit freshness detection is an efficient and nondestructive approach. Traditional methods for food quality detection can be timeconsuming, time-intensive, and require specialized equipment and investigators to operate. By utilizing machine learning, specifically CNNs, the detection process can be streamlined and automated. CNNs are a type of deep learning algorithm commonly used for image identification tasks. They have shown excellent performance in various computer vision applications, including object detection and classification. In the context of fruit freshness detection, CNNs can analyze the visual characteristics of fruits and determine their freshness based on visual cues such as color, texture, and surface defects. The advantage of using CNNs for fruit freshness detection is that they can learn complex patterns and features directly from the input data without relying on explicit rules or predefined features. This makes CNNs well-suited for detecting subtle differences in fruit appearance that may indicate freshness or spoilage. By training the CNN on a large dataset of labeled fruit images, the model can learn to differentiate between fresh and spoiled fruits. The paper you mentioned likely presents experimental results that demonstrate the effectiveness of CNNs in identifying fruit freshness. These results would validate the proposed approach and provide evidence that machine learning can be a valuable tool in the field of food quality detection. Additionally, the paper may discuss the challenge of over fitting, which is a common issue in machine learning where the model becomes too specialized to the training dataset and performs poorly on new, unseen data. Overall, using CNNs for fruit freshness detection offers several benefits, including improved efficiency in food circulation, reduced storage and labor costs, and enhanced food safety. By leveraging the power of machine learning, this approach has the potential to revolutionize the way fruit quality is assessed and monitored throughout the supply chain.