Acta Agriculturae Zhejiangensis ›› 2021, Vol. 33 ›› Issue (7): 1329-1338.DOI: 10.3969/j.issn.1004-1524.2021.07.19

• Biosystems Engineering • Previous Articles     Next Articles

Tomato disease recognition scheme based on multi-scale and attention mechanism

ZHANG Ning1,2(), WU Huarui2,3,4,*(), HAN Xiao2,3,4, MIAO Yisheng2,3,4   

  1. 1. College of Computer and Information Engineering, Beijing University of Agriculture, Beijing 102206, China
    2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    3. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
    4. Key Laboratory of Agri-Informatics, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
  • Received:2020-09-25 Online:2021-07-25 Published:2021-08-06
  • Contact: WU Huarui

Abstract:

The timely detection and management of tomato diseases can help increase the yield and quality of tomatoes and increase the economic benefits of farmers. The internet of things and artificial intelligence can effectively detect tomato diseases without damage. This paper proposed an improved AT-InceptionV3 (Attention-InceptionV3) neural network tomato leaf disease detection model. The network used InceptionV3 as the backbone network, combined with multi-scale convolution and attention mechanism CBAM (convolutional block attention module, CBAM) module, which enhanced disease information expression and suppressed irrelevant information interference. At the same time, transfer learning was introduced to prevent over-fitting when the sample data volume was small. In order to evaluate the effectiveness of the optimization model, an experimental simulation test was conducted on the public tomato disease data set of Plant Village. The improved model had an accuracy of 98.4% in classification of five leaf images of healthy tomato leaves, bacterial spot disease, late blight, leaf mold and yellow leaf curl vircus in the test stage, and the optimization effect was significant. In order to further verify the universality of this method in different Internet of Things, experiments had compared the model’s classification effect on disease images with different resolutions. The results showed that partial loss of image resolution would not reduce the accuracy of disease classification. This model could be used for tomato greenhouse intelligence and provide an important basis for network decision-making and judgment.

Key words: tomato, multi-scale convolution, attention mechanism, transfer learning, disease recognition

CLC Number: