Improved Identification of Leaf Diseases and Pest Infestations on Rice by Means of Coordinate Attention Mechanism-based Residual Network

LIAO Yuanjun; YANG Le; SHAO Peng; YU Xiaoyun

doi:10.19303/j.issn.1008-0384.2023.10.011

Volume 38 Issue 10

Oct. 2023

Turn off MathJax

Article Contents

Article Navigation > Fujian Journal of Agricultural Sciences > 2023 > 38(10): 1220-1229

LIAO Y J, YANG L, SHAO P, et al. Improved Identification of Leaf Diseases and Pest Infestations on Rice by Means of Coordinate Attention Mechanism-based Residual Network [J]. Fujian Journal of Agricultural Sciences，2023，38（10）：1220−1229 doi: 10.19303/j.issn.1008-0384.2023.10.011

Citation:

LIAO Y J, YANG L, SHAO P, et al. Improved Identification of Leaf Diseases and Pest Infestations on Rice by Means of Coordinate Attention Mechanism-based Residual Network [J]. Fujian Journal of Agricultural Sciences，2023，38（10）：1220−1229 doi: 10.19303/j.issn.1008-0384.2023.10.011

Citation:

PDF( 3277 KB)

Improved Identification of Leaf Diseases and Pest Infestations on Rice by Means of Coordinate Attention Mechanism-based Residual Network

doi: 10.19303/j.issn.1008-0384.2023.10.011

LIAO Yuanjun^1
,,
YANG Le^{1, 2
,
,},
SHAO Peng¹,
YU Xiaoyun¹

1.
College of Computer Information and Engineering, Jiangxi Agricultural University, Nanchang, Jiangxi　330045, China
2.
Key Laboratory of Information Technology in Agriculture for College and Universities of Jiangxi Province, Jiangxi Agriculture University, Nanchang, Jiangxi　330045, China

Received Date: 2023-02-08
Rev Recd Date: 2023-03-10

Available Online: 2023-09-19

Publish Date: 2023-10-28

Abstract

Abstract

Objective A new deep learning network was designed to improve the often-inaccurate identification of diseases and pest infestations on rice. Method The coordinate attention mechanism (CA) was introduced under the residual convolution block of RestNet-50 using the LeakyRelu activation function to replace the Relu activation function as well as the three 3×3 convolution kernels to replace the original 7×7 convolution kernel under the first convolution layer. Result The newly designed ResNet-50-CA effectively balanced the detection accuracy and model simplicity the original method lacked. The improved model was further fine-tuned with experiments to achieve a much-improved detection accuracy of 99.21% in identifying the diseases and infestations on a batch of 16 specimens with a learning rate of 0.0001. Conclusion The superior deep learning algorithm of the current ResNet50-CA system extracted more detailed and accurate information on the diseases and infestations than did the previous model. It could be applied for field and/or clinic diagnosis on rice plants.
- Deep learning network,
- ResNet50,
- rice leaf diseases and pest infestations,
- coordinate attention mechanism

FullText(HTML)

References(20)

References

[1]	ZHOU J, LI J X, WANG C S, et al. Crop disease identification and interpretation method based on multimodal deep learning [J]. Computers and Electronics in Agriculture, 2021, 189: 106408. doi: 10.1016/j.compag.2021.106408
[2]	王超, 王春圻, 刘金明. 基于深度学习的玉米叶片病害识别方法研究 [J]. 现代农业研究, 2022, 28(6):102−106. WANG C, WANG C Q, LIU J M. Identification of maize leaf diseases based on deep learning [J]. Modern Agriculture Research, 2022, 28(6): 102−106.(in Chinese)
[3]	JIANG F, LU Y, CHEN Y, et al. Image recognition of four rice leaf diseases based on deep learning and support vector machine [J]. Computers and Electronics in Agriculture, 2020, 179: 105824. doi: 10.1016/j.compag.2020.105824
[4]	BRAHIMI M, BOUKHALFA K, MOUSSAOUI A. Deep learning for tomato diseases: Classification and symptoms visualization [J]. Applied Artificial Intelligence, 2017, 31(4): 299−315. doi: 10.1080/08839514.2017.1315516
[5]	赵恒谦, 杨屹峰, 刘泽龙, 等. 农作物叶片病害迁移学习分步识别方法 [J]. 测绘通报, 2021(7):34−38. ZHAO H Q, YANG Y F, LIU Z L, et al. Step-by-step identification method of crop leaf diseases based on transfer learning [J]. Bulletin of Surveying and Mapping, 2021(7): 34−38.(in Chinese)
[6]	KAWASAKI Y, UGA H, KAGIWADA S, et al. Basic study of automated diagnosis of viral plant diseases using convolutional neural networks[M]//Advances in Visual Computing. Cham: Springer International Publishing, 2015: 638－645.
[7]	LU Y Z, YOUNG S. A survey of public datasets for computer vision tasks in precision agriculture [J]. Computers and Electronics in Agriculture, 2020, 178: 105760. doi: 10.1016/j.compag.2020.105760
[8]	HOU Q B, ZHOU D Q, FENG J S. Coordinate attention for efficient mobile network design[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville, TN, USA. IEEE, 2021: 13708－13717.
[9]	王志强, 于雪莹, 杨晓婧, 等.基于WGAN和MCA-MobileNet的番茄叶片病害识别[J]. 农业机械学报, 2023, 54（5）: 244－252. WANG Z Q, YU X Y, YANG X J, et al. Tomato Leaf Diseases Recognition Based on WGAN and MCA - MobileNet[J]. 2023, 54(5): 244－251.
[10]	HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770−778.
[11]	XU B, WANG N Y, CHEN T Q, et al. Empirical evaluation of rectified activations in convolutional network[EB/OL]. 2015: arXiv: 1505.00853.https://arxiv.org/abs/1505.00853
[12]	李键红, 吴亚榕. 基于多任务联合稀疏表示的水稻叶片病害自动识别算法 [J]. 仲恺农业工程学院学报, 2017, 30(2):40−44. doi: 10.3969/j.issn.1674-5663.2017.02.007 LI J H, WU Y R. Automatic recognition algorithm for rice leaf diseases based on multi-task joint sparse representation [J]. Journal of Zhongkai University of Agriculture and Engineering, 2017, 30(2): 40−44.(in Chinese) doi: 10.3969/j.issn.1674-5663.2017.02.007
[13]	JIANG Z C, DONG Z X, JIANG W P, et al. Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning [J]. Computers and Electronics in Agriculture, 2021, 186: 106184. doi: 10.1016/j.compag.2021.106184
[14]	刘立波, 周国民. 基于多层感知神经网络的水稻叶瘟病识别方法 [J]. 农业工程学报, 2009, 25(S2):213−217. LIU L B, ZHOU G M. Identification method of rice leaf blast using multilayer perception neural network [J]. Transactions of the Chinese Society of Agricultural Engineering, 2009, 25(S2): 213−217.(in Chinese)
[15]	MNIH V, HEESS N, GRAVES A, et al. Recurrent models of visual attention[C]//In Advances in neural information processing systems. 2014: 2204－2212.
[16]	XU K, BA J L, KIROS R, et al. Show, attend and tell: Neural image caption generation with visual attention[C]//Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37. July 6 - 11, 2015, Lille, France. New York: ACM, 2015: 2048－2057.
[17]	HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA. IEEE, 2018: 7132－7141.
[18]	WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[M]//Computer Vision – ECCV 2018. Cham: Springer International Publishing, 2018: 3－19.
[19]	WANG H Y, ZHU Y K, GREEN B, et al. Axial-DeepLab: Stand-alone axial-attention for panoptic segmentation[EB/OL]. 2020: arXiv: 2003.07853. https://arxiv.org/abs/2003.07853
[20]	ZHAO H S, SHI J P, QI X J, et al. Pyramid scene parsing network[J]. IEEE Computer Society, 2016: 6230－6239.