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Tomato Disease Classification and Identification Method Based on Multimodal Fusion Deep Learning

文献类型： 外文期刊

作者： Zhang, Ning 1 ; Wu, Huarui 1 ; Zhu, Huaji 1 ; Deng, Ying 1 ; Han, Xiao 1 ;

作者机构： 1.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Beijing Univ Agr, Coll Comp & Informat Engn, Beijing 102206, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100097, Peoples R China

4.Minist Agr & Rural Affairs, Key Lab Qual Testing Software & Hardware Prod Agr, Beijing 100097, Peoples R China

关键词： multimodal fusion; transfer learning; ResNet34; residual network; disease diagnosis

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.408； 五年影响因子：3.459 ）

ISSN：

年卷期： 2022 年 12 卷 12 期

页码：

收录情况： SCI

摘要： Considering that the occurrence and spread of diseases are closely related to the planting environment, a tomato disease diagnosis method based on Multi-ResNet34 multi-modal fusion learning based on residual learning is proposed for the problem of limited recognition rate of a single RGB image of a tomato disease. Based on the ResNet34 backbone network, this paper introduces transfer learning to speed up training, reduce data dependencies, and prevent overfitting due to a small amount of sample data; it also integrates multi-source data (tomato disease image data and environmental parameters). The feature-level multi-modal data fusion method is used to retain the key information of the data to identify the feature, so that the different modal data can complement, support and correct each other, and obtain a more accurate identification effect. Firstly, Mask R-CNN was used to extract partial images of leaves from complex background tomato disease images to reduce the influence of background regions on disease identification. Then, the formed image environment data set was input into the multi-modal fusion model to obtain the identification results of disease types. The proposed multi-modal fusion model Multi-ResNet34 has a classification accuracy of 98.9% for six tomato diseases: bacterial spot, late blight, leaf mold, yellow aspergillosis, gray mold, and early blight, which is higher than that of the single-modal model. With the increase by 1.1%, the effect is obvious. The method in this paper can provide an important basis for the analysis and diagnosis of tomato intelligent greenhouse diseases in the context of agricultural informatization.

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