Crop variety identification is an essential link in seed detection, phenotype collection and scientific breeding. This paper takes peanut as an example to explore a new method for crop variety identification. Peanut is a crucial oil crop and cash crop. The yield and quality of different peanut varieties are different, so it is necessary to identify and classify different peanut varieties. The traditional image processing method of peanut variety identification needs to extract many features, which has defects such as intense subjectivity and insufficient generalization ability. Based on the deep learning technology, this paper improved the deep convolutional neural network VGG16 and applied the improved VGG16 to the identification and classification task of 12 varieties of peanuts. Firstly, the peanut pod images of 12 varieties obtained by the scanner were preprocessed with gray-scale, binarization, and ROI extraction to form a peanut pod data set with a total of 3365 images of 12 varieties. A series of improvements have been made to VGG16. Remove the F6 and F7 fully connected layers of VGG16. Add Conv6 and Global Average Pooling Layer. The three convolutional layers of conv5 have changed into Depth Concatenation and add the Batch Normalization(BN) layers to the model. Besides, fine-tuning is carried out based on the improved VGG16. We adjusted the location of the BN layers. Adjust the number of filters for Conv6. Finally, the improved VGG16 model's training test results were compared with the other classic models, AlexNet, VGG16, GoogLeNet, ResNet18, ResNet50, SqueezeNet, DenseNet201 and MobileNetv2 verify its superiority. The average accuracy of the improved VGG16 model on the peanut pods test set was 96.7%, which was 8.9% higher than that of VGG16, and 1.6–12.3% higher than that of other classical models. Besides, supplementary experiments were carried out to prove the robustness and generality of the improved VGG16. The improved VGG16 was applied to the identification and classification of seven corn grain varieties with the same method and an average accuracy of 90.1% was achieved. The experimental results show that the improved VGG16 proposed in this paper can identify and classify peanut pods of different varieties, proving the feasibility of a convolutional neural network in variety identification and classification. The model proposed in this experiment has a positive significance for exploring other Crop variety identification and classification.

In order to realize highly intelligent and automatic species identification and recognition, we obtained the images of 11 varieties and each variety includes 50 seeds. For each image, we acquired 33 characteristics including shape, color and texture characteristics. And then we constructed the Artificial Neural Network and Support Vector Machine model to train and identify different varieties. We built the recognition system based on Visual C++ 6.0 and OpenCV library.Results shows that the SVM method has higher recognition effect than neural network overall and the recognition effect is more stability, the overall self-_recognition performance can reach 100% and test accuracy can reach 85%. The recoginition System base on Visual C++ runs faster than that of Matlab, which is more suitable for real-time varieties identification.

With the rapid development of digital technology, the problem of media tampering has become increasingly serious, and higher education teaching resources are facing a crisis from image tampering and sound tampering to video tampering and even deep forgery, which brings great challenges to education work. Through deep learning, tampering can be automatically detected and self-learning and optimized in the process of detection, so that it can remain efficient and accurate in the face of new tampering techniques. The purpose of this paper is to discuss in detail the application of deep learning in media tampering detection and to explore its application in the protection of higher education teaching resources.

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