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Self-Supervised Collaborative Multi-Network for Fine-Grained Visual Categorization of Tomato Diseases

文献类型： 外文期刊

作者： Yang, Guofeng 1 ; Chen, Guipeng 1 ; He, Yong 2 ; Yan, Zhiyan 1 ; Guo, Yang 1 ; Ding, Jian 1 ;

作者机构： 1.Jiangxi Acad Agr Sci, Inst Agr Econ & Informat, Nanchang 330200, Jiangxi, Peoples R China

2.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Peoples R China

关键词： Diseases; Agriculture; Training; Feature extraction; Annotations; Object detection; Image classification; Fine-grained visual categorization; multi-network; self-supervised; tomato diseases

期刊名称：IEEE ACCESS （ 影响因子：3.367； 五年影响因子：3.671 ）

ISSN： 2169-3536

年卷期： 2020 年 8 卷

页码：

收录情况： SCI

摘要： Artificial recognition of tomato diseases is often time-consuming, laborious and subjective. For tomato disease images, it is difficult to find small discriminative features between different tomato diseases, which can bring challenges to fine-grained visual categorization of tomato leaf-based images. Therefore, we propose a novel model, which consists of 3 networks, including a Location network, a Feedback network, and a Classification network, named LFC-Net. At the same time, a self-supervision mechanism is proposed in the model, which can effectively detect informative regions of tomato image without the need for manual annotation such as bounding boxes/parts. Based on the consideration of the consistency between category of the image and informativeness of the image, we design a novel training paradigm. The Location network of the model first detects informative regions in the tomato image, and optimizes iterations under the guidance of the Feedback network. Then, the Classification network uses informative regions proposed by the Location network and the full image of the tomato for classification. Our model can be regarded as a multi-network collaboration, and networks can progress together. Compared with the pre-trained model on ImageNet, our model achieves the most advanced performance in the tomato dataset, with accuracy up to 99.7%. This work demonstrates that our model has a high accuracy and has the potential to be applied to other vegetable and fruit datasets, which can provide a reference for the prevention and control of tomato diseases.

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