E-AlexNet: quality evaluation of strawberry based on machine learning

Strawberry is a kind fruit with high nutritional value and economic value, but powdery mildew, black mildew, and other diseases decreased the quality of strawberry. In this paper, an enhanced AlexNet (E-AlexNet) was proposed for strawberry quality evaluation. Firstly, strawberry images shot from laboratory and field were collected, then, a series of pre-processing, such as data augmentation and balance were performed on the data set. At last, the images were imported into the E-AlexNet for training. Our improvement is as follows: (1) The convolution kernel’s size has been modified; (2) A single convolutional layer is divided into three convolutional layers with different convolution kernels; (3) Using the BN layer and L2 regularization. The network’s accuracy and order of magnitude can be improved by the above operation. Finally, the accuracy influence of the neuron volume in the final fully connected layer was discussed. Result shows that the average recognition accuracy of the original AlexNet network with original data set is 84.50%, and the accuracy of E-Alexnet network is 90.70%, after augmentation, the average recognition accuracy of AlexNet is 89.34% and E-Alexnet is 95.75%. The E-AlexNet is superior to the original AlexNet before and after data augmentation. The proposed model is also compared with other classical models, and the experimental results show that the proposed improved method is feasible. The network improvement method proposed in this paper is significant, and the E-AlexNet network has a broad application prospect in strawberry quality evaluation.