1. Automated orange quality classification using convolutional neural networks: A deep learning approach for smart agriculture

Quality control is the core activity of an agribusiness and food processing industry just to make sure that the customers have access to quality oranges in a reduced wastage system. This study molds a deep learning idea to classify oranges as either good or bad. These images capture critical features such as consistency of color, surface texture, and apparent defects. Brightness adjustments, enhanced contrasts, and even the addition of some noise are among the possible scenes to improve model generalization error performance. The proposed system would give an automated and scalable real-time orange grading system that would gradually reduce the influence of time-based human inspection practices and improve quality. The finding that even a simple CNN without any pre-train models can be used to achieve high accuracy in this classification task indeed, the results provide for deep learning to be effective in fruit sorting, with scope for much else based on larger data sets, as well as real-world deployment potential