Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (4): 104-114.doi: 10.16088/j.issn.1001-6600.2021120101

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Tomato Leaf Disease Recognition Model Based on SK-EfficientNet

TIE Jun1,2*, LONG Juanjuan1,2, ZHENG Lu1,2, NIU Yue1,2, SONG Yanlin1,2   

  1. 1. School of Computer Science, South-Central Minzu University, Wuhan Hubei 430074, China;
    2. Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises, Wuhan Hubei 430074, China
  • Published:2022-08-05

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Abstract: To solve the problems of the large amount of network parameters, low accuracy, and difficulty in deploying the mobile terminal model of the current deep learning model for tomato leaf diseased identification, a tomato leaf disease identification method based on SK-EfficientNet is proposed. The lightweight model EfficientNet is used as the benchmark model, and the selective convolution kernel mechanism SKNet is used to replace the SENet in the EfficientNet core module MBConv, so that the convolution kernel can adaptively chooses the size of the receptive field according to the multi-scale information of the input features. This method improves the ability to extract image features while making more effective use of parameters.Through multiple sets of comparative experiments, the results show that the training accuracy of the improved model has been further improved, and the model parameter is 3.83 MiB.The average accuracy rate on the PlantVillage data set is 99.64%, and the recognition accuracy of SK-EfficientNet-B2 is the highest; in natural scenes, the average accuracy rate is 3.81 percentage point higher than the original model.The results show that the improved model can effectively increase the accuracy of tomato leaf disease recognition in natural scenes, which provide a reference for the deployment of tomato leaf disease recognition models on mobile terminals.

Key words: tomato leaves, disease identification, EfficientNet network, SKNet, MBConv

CLC Number: 

  • TP391.41
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