Agricultural development driven by the digital economy: improved EfficientNet vegetable quality grading

Introduction: The conventional manual grading of vegetables poses challenges that necessitate innovative solutions. In this context, our paper proposes a deep learning methodology for vegetable quality grading. Methods: To address the scarcity of vegetable datasets, we constructed a unique dataset comprising 3,600 images of diverse vegetables, including lettuce, broccoli, tomatoes, garlic, bitter melon, and Chinese cabbage. We present an improved CA-EfficientNet-CBAM model for vegetable quality grading. The CA module replaces the squeeze-and-excitation (SE) module in the MobileNet convolution (MBConv) structure of the EfficientNet model. Additionally, a channel and spatial attention module (CBAM) is integrated before the final layer, accelerating model training and emphasizing nuanced features. Results: The enhanced model, along with comparisons to VGGNet16, ResNet50, and DenseNet169, was subjected to ablation experiments. Our method achieved the highest classification accuracy of 95.12% on the cabbage vegetable image test set, outperforming VGGNet16, ResNet50, and DenseNet169 by 8.34%, 7%, and 4.29%, respectively. Notably, the proposed method effectively reduced the model’s parameter count. Discussion: Our experimental results highlight the effectiveness of the deep learning approach in improving vegetable quality grading accuracy. The superior performance of the enhanced EfficientNet model underscores its potential for advancing the field, achieving both high classification accuracy and parameter efficiency. We hope this aligns with your expectations. If there are further adjustments or clarifications needed, please let us know.