Rice disease and pest identification based on MSDB-ResNet

LIU Yongmin; HU Kui; NIE Jiawei; XIE Tieqiang

doi:10.7671/j.issn.1001-411X.202208020

Journal of South China Agricultural University > 2023 > 44(6): 978-985. > DOI: 10.7671/j.issn.1001-411X.202208020

LIU Yongmin, HU Kui, NIE Jiawei, et al. Rice disease and pest identification based on MSDB-ResNet[J]. Journal of South China Agricultural University, 2023, 44(6): 978-985. DOI: 10.7671/j.issn.1001-411X.202208020

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Rice disease and pest identification based on MSDB-ResNet

1.
School of Computer and Information Engineering/Research Center of Smart Forestry Cloud, Central South University of Forestry and Technology, Changsha 410004, China
2.
School of Animal Science, South China Agricultural University, Guangzhou 510642, China

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Received Date: July 31, 2022
Available Online: November 12, 2023
Published Date: July 06, 2023

Graphical Abstract

Abstract

Abstract

Objective
The yield of rice is related to food security of all mankind. How to effectively prevent and detect rice diseases and pests is an important topic in the field of smart agriculture. Deep learning has become the preferred method for identifying rice diseases and pests due to its excellent performance in self-learning image features. However, in natural environments, the dataset is relatively small and susceptible to complex backgrounds, resulting in overfitting and difficulty in extracting subtle features during training. This study aims to address the aforementioned issues.
Method
We proposed a rice disease and pest identification model with multi-scale dual branch structure based on improved ResNet (MSDB ResNet). On the basis of the ResNet model, ConvNeXt residual blocks were introduced to optimize the calculation proportion of residual blocks, construct a dual branch structure, and extract disease features of different sizes from the input disease image by adjusting the convolution kernel size of each branch. In response to issues such as complex real world environments, small datasets, and overfitting, a total of 5932 rice pest and disease images captured from natural environments was utilized. Using data preprocessing methods such as random brightness and motion blur, as well as data augmentation methods such as mirroring, cropping, and scaling, the dataset was expanded to 20000 pictures. The MSDB-ResNet model was trained to identify four common rice diseases.
Result
MSDB-ResNet had good recognition performance on rice disease and pest datasets, with a recognition accuracy of 99.10%, which was 2.42 percentage points higher than the original ResNet model and obviously superior to classic networks such as AlexNet, VGG, DenseNet, ResNet, etc. This model had good generalization ability and strong robustness.
Conclusion
The MSDB ResNet model is feasible and progressiveness in the identification of rice diseases and pests, which provides a reference for the identification of rice diseases and pests under complex background.
- Smart agriculture,
- Rice diseases and pests,
- Deep learning,
- Image recognition,
- ResNet,
- Data enhancement

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References (25)

References

[1]	姜玉英, 刘万才, 陆明红, 等. 2020年全国农作物重大病虫害发生趋势预报[J]. 中国植保导刊, 2016, 36(2): 37-42.
[2]	边柯橙, 杨海军, 路永华. 深度学习在农业病虫害检测识别中的应用综述[J]. 软件导刊, 2021, 20(3): 26-33.
[3]	汪京京, 张武, 刘连忠, 等. 农作物病虫害图像识别技术的研究综述[J]. 计算机工程与科学, 2014, 36(7): 1363-1370.
[4]	PATRÍCIO D I, RIEDER R. Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review[J]. Computers and Electronics in Agriculture, 2018, 153: 69-81. doi: 10.1016/j.compag.2018.08.001
[5]	胡林, 刘婷婷, 李欢, 等. 机器学习及其在农业中应用研究的展望[J]. 农业图书情报, 2019, 31(10): 12-22.
[6]	翟肇裕, 曹益飞, 徐焕良, 等. 农作物病虫害识别关键技术研究综述[J]. 农业机械学报, 2021, 52(7): 1-18.
[7]	周惠汝, 吴波明. 深度学习在作物病害图像识别方面应用的研究进展[J]. 中国农业科技导报, 2021, 23(5): 61-68.
[8]	陆健强, 林佳翰, 黄仲强, 等. 基于Mixup算法和卷积神经网络的柑橘黄龙病果实识别研究[J]. 华南农业大学学报, 2021, 42(3): 94-101. doi: 10.7671/j.issn.1001-411X.202008041
[9]	黄林生, 罗耀武, 杨小冬, 等. 基于注意力机制和多尺度残差网络的农作物病害识别[J]. 农业机械学报, 2021, 52(10): 264-271.
[10]	骆润玫, 殷惠莉, 刘伟康, 等. 基于YOLOv5-C的广佛手病虫害识别[J]. 华南农业大学学报, 2023, 44(1): 151-160.
[11]	于合龙, 沈金梦, 毕春光, 等. 基于知识图谱的水稻病虫害智能诊断系统[J]. 华南农业大学学报, 2021, 42(5): 105-116. doi: 10.7671/j.issn.1001-411X.202101010
[12]	CHEN J D, CHEN J X, ZHANG D F, et al. Using deep transfer learning for image-based plant disease identification[J]. Computers and Electronics in Agriculture, 2020, 173: 105393. doi: 10.1016/j.compag.2020.105393.
[13]	袁培森, 申成吉, 徐焕良. 基于迁移学习和双线性Inception-ResNet-v2的细粒度菌菇识别方法[J]. 农业机械学报, 2021, 52(7): 1-10.
[14]	SETHY P K, BARPANDA N K, RATH A K, et al. Deep feature based rice leaf disease identification using support vector machine[J]. Computers and Electronics in Agriculture, 2020, 175: 105527. doi: 10.1016/j.compag.2020.105527.
[15]	WU H, WIESNER-HANKS T, STEWART E L, et al. Autonomous detection of plant disease symptoms directly from aerial imagery[J]. The Plant Phenome Journal, 2019, 2(1): 1-9.
[16]	CRUZ A C, LUVISI A, DE BELLIS L, et al. X-FIDO: An effective application for detecting olive quick decline syndrome with deep learning and data fusion[J]. Frontiers in Plant Science, 2017, 8: 1741. doi: 10.3389/fpls.2017.01741.
[17]	LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324. doi: 10.1109/5.726791
[18]	KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90. doi: 10.1145/3065386
[19]	LU Y, YI S J, ZENG N Y, et al. Identification of rice diseases using deep convolutional neural networks[J]. Neurocomputing, 2017, 267: 378-384. doi: 10.1016/j.neucom.2017.06.023
[20]	HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016: 770-778.
[21]	LIU Z, MAO H Z, WU C Y, et al. A ConvNet for the 2020s[C]//2022 IEEE/CVE Conference on Computer Vision and Pattern Recognition. New Orleans, LA, USA: IEEE, 2022: 11966-11976.
[22]	Anon. GitHub repository: ConvNeXt[EB/OL]. [2022-08-01]. https://github.com/facebookresearch/ConvNeXt.
[23]	LIU Z, LIN Y, CAO Y, et al. Swin transformer: Hierarchical vision transformer using shifted windows[C]// 2021 IEEE/CVF International Conference on Computer Vision. Montreal, QC, Canada: IEEE, 2022: 9992-10002.
[24]	Anon. GitHub repository: Swin transformer[EB/OL]. [2022-08-01]. https://github.com/microsoft/Swin-Transformer.
[25]	郭小清, 范涛杰, 舒欣. 基于改进 Multi-Scale AlexNet 的番茄叶部病害图像识别[J]. 农业工程学报, 2019, 35(13): 162-169.

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Rice disease and pest identification based on MSDB-ResNet

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