  This study proposes a method based on lightweight convolutional neural
networks (CNN) and generative adversarial networks (GAN) for apple ripeness and
damage level detection tasks. Initially, a lightweight CNN model is designed by
optimizing the model's depth and width, as well as employing advanced model
compression techniques, successfully reducing the model's parameter and
computational requirements, thus enhancing real-time performance in practical
applications. Simultaneously, attention mechanisms are introduced, dynamically
adjusting the importance of different feature layers to improve the performance
in object detection tasks. To address the issues of sample imbalance and
insufficient sample size, GANs are used to generate realistic apple images,
expanding the training dataset and enhancing the model's recognition capability
when faced with apples of varying ripeness and damage levels. Furthermore, by
applying the object detection network for damage location annotation on damaged
apples, the accuracy of damage level detection is improved, providing a more
precise basis for decision-making. Experimental results show that in apple
ripeness grading detection, the proposed model achieves 95.6\%, 93.8\%, 95.0\%,
and 56.5 in precision, recall, accuracy, and FPS, respectively. In apple damage
level detection, the proposed model reaches 95.3\%, 93.7\%, and 94.5\% in
precision, recall, and mAP, respectively. In both tasks, the proposed method
outperforms other mainstream models, demonstrating the excellent performance
and high practical value of the proposed method in apple ripeness and damage
level detection tasks.
