Corn is an important means of production.In order to recognize and protect corn seeds,5 corn varie-ties were collected in this experiment.After processing,a total of 1 778 corn grain images were obtained,and a mixed dataset of embryo surface and endosperm surface was established.The training set,verification set,and test set were divided in a ratio of 7:2:1.Firstly,based on transfer learning,DenseNet121,MobileNetV2,VGG16 and GoogLeNet were selected to recognize the corn kernel image,and the accuracy rates in the test set were 94.32％,93.18％,95.45％and 92.61％,respectively.Since VGG16 had the highest accuracy,Therefore,VGG16 was im-proved,and channel attention SE module was introduced to construct a new network model L-SE-VGG while sim-plifying the model structure.Compared with VGG16 without pre-training,VGG16 with transfer learning and L-VGG without SE module,the recognition accuracy of L-SE-VGG was up to 98.86％.The study provides a new and effective strategy and experimental method for the application of deep learning technology in the identification of corn varieties and provides reference for the identification and detection of corn varieties.
维普
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