Optimizing Vegetable Classification with Convolutional Neural Networks Model

Improvement of effectiveness and quality control in the supply chain for agricultural products depends on correct vegetable categorization. This paper uses a large collection of different vegetable photos to investigate the applicability of Convolutional Neural Networks (CNNs) for veggie categorization. Source from public sources and customized photographs, the collection catches many variances in lighting, backdrop, orientation, and ripeness levels. Trained over five epochs, the CNN model consisted of many convolutional, pooling, and dense layers. Starting from 1.8434 in epoch 1 and working down to 0.0448 by epoch 5, the training procedure showed a notable drop in training loss. Training accuracy also shown amazing development, going from 46.92% in the first epoch to 98.78% in the final period concurrently. Validation loss dropped from 0.5191 to 0.2528 while validation accuracy rose from 84.60% to 93.70
^%
. These findings show the model's efficiency and strong ability to correctly categorize veggies, therefore suggesting its prospective pragmatic use in agricultural environments. CNN-based models give a practical replacement for automated vegetable categorization, therefore improving the labor cost, productivity, and quality control in agriculture.