The classification and identification of fruits and vegetables through image recognition is a significant challenge in modern agriculture, food processing, and retail automation. Manual sorting methods are often error-prone, time-consuming, and inefficient for large-scale operations, necessitating the development of automated, scalable solutions. This study introduces a deep learning–based framework for fruit and vegetable recognition using Convolutional Neural Networks (CNNs). A sequential CNN model was designed and trained on a publicly available dataset containing thousands of labeled fruit and vegetable images. Preprocessing techniques such as resizing, normalization, and data augmentation (rotation, flipping, zooming, and shearing) were applied to enhance generalization and mitigate overfitting. The model was implemented using TensorFlow and Keras, trained with categorical cross-entropy loss, and evaluated using accuracy, precision, recall, and confusion matrix analysis. Results indicate that the CNN achieved high classification accuracy, demonstrating its effectiveness in distinguishing between visually similar categories of produce. The framework shows strong potential for integration into commercial retail systems, automated inventory management, and agricultural inspection workflows. Furthermore, this work lays the foundation for future enhancements, including expansion to additional produce categories and real-time mobile or web-based deployment, thereby contributing to intelligent, AI-driven solutions for food quality control and supply chain optimization.
Mushraf et al. (Mon,) studied this question.