Transforming Agricultural Technology with CNNs: High-Precision Vegetable Classification for Enhanced Food Supply Chains

Accurate classification of vegetable photos becomes more important as world food consumption increases to maximize farming methods, quality control, and food supply networks. This work presents a Convolutional Neural Network (CNN) model intended for the classification of photos of 15 different vegetable classes, including Bean, Bitter Gourd, Bottle Gourd, Brinjal, Broccoli, Cabbage, Capsicum, Carrot, cauliflower, Cucumber, Papaya, Potato, Pumpkin, Radish, and Tomato. With a dataset of 14,000 training photos, 3,000 validation images, and 3,000 test images taken from Kaggle, the CNN model shows an amazing general accuracy of 98%. The model uses numerous layers of convolution and max-pooling, then fully connected layers with dropout regularization to guard against overfitting. Classes including Bean, Carrot, and Potato showed almost 100% accuracy; Tomato showed a somewhat worse recall. Examining training and validation losses as well as accuracy helps to validate the model's resilience and capacity for broad generalization to unprocessed data. Reflecting its practical relevance in agricultural environments, the confusion matrix supports the great classification performance of the model. Using better vegetable sorting, lower post-harvest losses, and support of effective food processing, this study emphasizes CNN's potential to propel agricultural technologies forward. Larger datasets and more CNN architectures could be investigated in future study to improve classification capacity.