摘要：Monitoring the wheat growth process by machine vision with artificial feature extraction suffers from poor objectivity and low efficiency. To solve this problem, this paper introduces deep learning into monitoring the wheat growth process. The convolutional neural network (CNN) has been widely used in image classification as a common algorithm in deep learning. The application of deep feature extraction networks could automatically recognize and extract image features. However, the large number of parameters and computational overhead brought by regular deep CNNs make it difficult to apply these models to embedded devices with limited storage space and computing power. Therefore, the knowledge distillation method has been proposed for the feature extraction network of the target detection network to improve the performance of shallow feature extraction networks and ensure recognition accuracy while reducing the computational burden and model size. This paper took ResNet50 and MobileNet as two different teacher networks to guide the student model MobileNet for training. The experimental results showed that when ResNet50 was used as teacher model, MobileNet had the best recognition effect. The average recognition accuracy of the student model MobileNet was 97.3%, and the model size was compressed to only 19.71 MB, which is 88.9% smaller than that of ResNet50. Knowledge distillation improves the accuracy of the obtained model and reduces the number of network model parameters. Furthermore, it reduces the model running time and the cost of model deployment greatly. The application of the knowledge distillation method could provide further technical support for intelligent wheat production in the field.
