Corn is one of the main food crops in China, and its area ranks in the top three in the world. However, the corn leaf disease has seriously affected the yield and quality of corn. To quickly and accurately identify corn leaf diseases, taking timely and effective treatment to reduce the loss of corn yield. We proposed identifying corn leaf diseases using the Mobilenetv3 (CD-Mobilenetv3) model. Based on the Mobilenetv3 model, we replaced the model’s cross-entropy loss function with a bias loss function to improve accuracy. Replaced the model’s squeeze and excitation (SE) module with the efficient channel attention (ECA) module to reduce parameters. Introduced the cross-layer connections between Mobile modules to utilize features synthetically. Then we Introduced the dilated convolutions in the model to increase the receptive field. We integrated a hybrid open-source corn leaf disease dataset (CLDD). The test results on CLDD showed the accuracy reached 98.23%, the precision reached 98.26%, the recall reached 98.26%, and the F1 score reached 98.26%. The test results are improved compared to the classic deep learning (DL) models ResNet50, ResNet101, ShuffleNet_x2, VGG16, SqueezeNet, InceptionNetv3, etc. The loss value was 0.0285, and the parameters were lower than most contrasting models. The experimental results verified the validity of the CD-Mobilenetv3 model in the identification of corn leaf diseases. It provides adequate technical support for the timely control of corn leaf diseases.

In order to solve the problems of high subjectivity, frequent error occurrence and easy damage of traditional corn seed identification methods, this paper combines deep learning with machine vision and the utilization of the basis of the Swin Transformer to improve maize seed recognition. The study was focused on feature attention and multi-scale feature fusion learning. Firstly, input the seed image into the network to obtain shallow features and deep features; secondly, a feature attention layer was introduced to give weights to different stages of features to strengthen and suppress; and finally, the shallow features and deep features were fused to construct multi-scale fusion features of corn seed images, and the seed images are divided into 19 varieties through a classifier. The experimental results showed that the average precision, recall and F1 values of the MFSwin Transformer model on the test set were 96.53%, 96.46%, and 96.47%, respectively, and the parameter memory is 12.83 M. Compared to other models, the MFSwin Transformer model achieved the highest classification accuracy results. Therefore, the neural network proposed in this paper can classify corn seeds accurately and efficiently, could meet the high-precision classification requirements of corn seed images, and provide a reference tool for seed identification.