Fine-grained tomato disease recognition based on attention residual mechanism

HU Zhiwei; YANG Hua; HUANG Jiming; XIE Qianqian

doi:10.7671/j.issn.1001-411X.201812048

Journal of South China Agricultural University > 2019 > 40(6): 124-132. > DOI: 10.7671/j.issn.1001-411X.201812048

HU Zhiwei, YANG Hua, HUANG Jiming, et al. Fine-grained tomato disease recognition based on attention residual mechanism[J]. Journal of South China Agricultural University, 2019, 40(6): 124-132. DOI: 10.7671/j.issn.1001-411X.201812048

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Fine-grained tomato disease recognition based on attention residual mechanism

1.
College of Information Science and Engineering, Shanxi Agricultural University, Taigu 030801, China
2.
School of Computer Science, Wuhan University, Wuhan 430072, China

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Received Date: December 24, 2018
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
To solve the insufficient identification of fine-grained tomato diseases in greenhouse.

Method
Taking tomato leaves with five early or late diseases as research objects, we proposed a new convolutional neural network model ARNet based on the combination of attention and residual thought. A multi-layered attention module was introduced to solve the problem of early disease location dispersion and the difficulty of feature extraction by extracting hierarchically disease classification information. In order to avoid the degradation of network training, we constructed a residual module to effectively integrate high- and low-order features. Meantime, we introduced the data expansion technology to prevent model over-fitting.

Result
Model training and testing results of early and late disease leaf datasets with 44 295 pictures showed that ARNet has better classification performance with an average recognition accuracy of 88.2%, which was significantly higher than those of other existing models. In addition, the identification accuracy of ARNet for early disease was significantly better than that for late disease, which verified the effectiveness of attention mechanism in extracting fine region features, and there was no excessive jitter during training process.

Conclusion
This model proposed in this paper has strong robustness and high stability, and can provide a reference for intelligent diagnosis of fine-grained tomato diseases in practical application.
- fine-grained,
- attention mechanism,
- residual network,
- convolutional network,
- tomato leaf,
- disease recognition

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References (24)

References

[1]	BAI X, LI X, FU Z, et al. A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images[J]. Comput Electron Agric, 2017, 136: 157-165. doi: 10.1016/j.compag.2017.03.004
[2]	王翔宇, 温皓杰, 李鑫星, 等. 农业主要病害检测与预警技术研究进展分析[J]. 农业机械学报, 2016, 47(9): 266-277. doi: 10.6041/j.issn.1000-1298.2016.09.037
[3]	龙满生, 欧阳春娟, 刘欢, 等. 基于卷积神经网络与迁移学习的油茶病害图像识别[J]. 农业工程学报, 2018, 34(18): 194-201. doi: 10.11975/j.issn.1002-6819.2018.18.024
[4]	MOKHTAR U, ALI M A S, HASSENIAN A E, et al. Tomato leaves diseases detection approach based on support vector machines[C]//IEEE. Computer Engineering Conference (ICENCO). Egypt: IEEE, 2015: 246-250.
[5]	XIE C, SHAO Y, LI X, et al. Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging[J]. Sci Rep, 2015, 5: 16564. doi: 10.1038/srep16564
[6]	柴阿丽, 李宝聚, 石延霞, 等. 基于计算机视觉技术的番茄叶部病害识别[J]. 园艺学报, 2010, 37(9): 1423-1430.
[7]	AMARA J, BOUAZIZ B, ALGERGAWY A. A deep learning based approach for banana leaf diseases classification[M]// MITSCHANG B. Lecture Notes in Informatics (LNI). Bonn : Gesellschaft Für Informatik, 2017: 79-88.
[8]	马浚诚, 杜克明, 郑飞翔, 等. 基于卷积神经网络的温室黄瓜病害识别系统[J]. 农业工程学报, 2018, 34(12): 186-192. doi: 10.11975/j.issn.1002-6819.2018.12.022
[9]	DURMUS H, GUNES E O, KIRCI M. Disease detection on the leaves of the tomato plants by using deep learning[C]//IEEE. 2017 6th International Conference on Agro-Geoinformatics. Fairfax VA: IEEE, 2017: 1-5.
[10]	FUENTES A, YOON S, KIM S C, et al. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition[J]. Sensors, 2017, 17(9): 2022. doi: 10.3390/s17092022
[11]	BRAHIMI M, BOUKHALFA K, MOUSSAOUI A. Deep learning for tomato diseases: Classification and symptoms visualization[J]. Appl Artif Intell, 2017, 31(4): 299-315.
[12]	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Curran Associates. Advances in Neural Information Processing Systems(NIPS). New York: Curran Associates, 2017: 5998-6008.
[13]	LETARTE G, PARADIS F, GIGUERE P, et al. Importance of self-attention for sentiment analysis[C]// Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. Brussels: Association for Computational Linguistics. 2018: 267-275.
[14]	YU C, WANG J, PENG C, et al. BiSeNet: Bilateral segmentation network for real-time semantic segmentation [C]//Springer. Proceedings of the European Conference on Computer Vision (ECCV). Munich: Springer, 2018: 325-341.
[15]	WANG F, JIANG M, QIAN C, et al. Residual attention network for image classification[C]//IEEE. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 3156-3164.
[16]	FU J, ZHENG H, MEI T. Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition[C]//IEEE. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 4476-4484.
[17]	HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//IEEE. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.
[18]	HE K, ZHANG X, REN S, et al. Identity mappings in deep residual networks[C]//Springer. Proceedings of the European Conference on Computer Vision (ECCV). Amsterdam : Springer, 2016: 630-645.
[19]	ZHANG K, SUN M, HAN T X, et al. Residual networks of residual networks: Multilevel residual networks[J]. IEEE T Circ Syst Vid, 2018, 28(6): 1303-1314. doi: 10.1109/TCSVT.2017.2654543
[20]	孙俊, 谭文军, 毛罕平, 等. 基于改进卷积神经网络的多种植物叶片病害识别[J]. 农业工程学报, 2017, 33(19): 209-215. doi: 10.11975/j.issn.1002-6819.2017.19.027
[21]	SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]//IEEE. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 2818-2826.
[22]	CHOLLET F. Xception: Deep Learning with Depthwise Separable Convolutions[C]//IEEE. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 1800-1807.
[23]	SANDLER M, HOWARD A, ZHU M, et al. MobileNetV2: Inverted residuals and linear bottlenecks[C]//IEEE. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City: IEEE, 2018: 4510-4520.
[24]	RUSSAKOVSKY O, DENG J, SU H, et al. Imagenet large scale visual recognition challenge[J]. Int J Comput Vision, 2015, 115(3): 211-252. doi: 10.1007/s11263-015-0816-y

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Fine-grained tomato disease recognition based on attention residual mechanism

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