Severity Grading Model for Camellia Oleifera Anthracnose Infection Based on Improved YOLACT

doi:10.12133/j.smartag.SA202402002

Abstract

Abstract:

[Objective] Camellia oleifera is one of the four major woody oil plants in the world. Diseases is a significant factor leading to the decline in quality of Camellia oleifera and the financial loss of farmers. Among these diseases, anthracnose is a common and severe disease in Camellia oleifera forests, directly impacting yields and production rates. Accurate disease assessment can improve the prevention and control efficiency and safeguarding the farmers' profit. In this study, an improved You Only Look at CoefficienTs (YOLACT) based method was proposed to realize automatic and efficient grading of the severity of Camellia oleifera leaf anthracnose. [Methods] High-resolution images of Camellia oleifera anthracnose leaves were collected using a smartphone at the National Camellia oleifera Seed Base of Jiangxi Academy of Forestry, and finally 975 valid images were retained after a rigorous screening process. Five data enhancement means were applied, and a data set of 5 850 images was constructed finally, which was divided into training, validation, and test sets in a ratio of 7:2:1. For model selection, the Camellia-YOLACT model was proposed based on the YOLACT instance segmentation model, and by introducing improvements such as Swin-Transformer, weighted bi-directional feature pyramid network, and HardSwish activation function. The Swin Transformer was utilized for feature extraction in the backbone network part of YOLACT, leveraging the global receptive field and shift window properties of the self-attention mechanism in the Transformer architecture to enhance feature extraction capabilities. Additionally, a weighted bidirectional feature pyramid network was introduced to fuse feature information from different scales to improve the detection ability of the model for objects at different scales, thereby improving the detection accuracy. Furthermore, to increase the the model's robustness against the noise in the input data, the HardSwish activation function with stronger nonlinear capability was adopted to replace the ReLu activation function of the original model. Since images in natural environments usually have complex background and foreground information, the robustness of HardSwish helped the model better handling these situations and further improving the detection accuracy. With the above improvements, the Camellia-YOLACT model was constructed and experimentally validated by testing the Camellia oleifera anthracnose leaf image dataset. [Results and Discussions] A transfer learning approach was used for experimental validation on the Camellia oleifera anthracnose severity grading dataset, and the results of the ablation experiments showed that the mAP₇₅ of Camellia-YOLACT proposed in this study was 86.8%, mAP_all was 78.3%, mAR was 91.6% which were 5.7%, 2.5% and 7.9% higher than YOLACT model. In the comparison experiments, Camellia-YOLACT performed better than Segmenting Objects by Locations (SOLO) in terms of both accuracy and speed, and its detection speed was doubled compared to Mask R-CNN algorithm. Therefore, the Camellia-YOLACT algorithm was suitable in Camellia oleifera gardens for anthracnose real-time segmentation. In order to verify the outdoors detection performance of Camellia-YOLACT model, 36 groups of Camellia oleifera anthracnose grading experiments were conducted. Experimental results showed that the grading correctness of Camellia oleifera anthracnose injection severity reached 94.4%, and the average absolute error of K-value was 1.09%. Therefore, the Camellia-YOLACT model proposed in this study has a better performance on the grading of the severity of Camellia oleifera anthracnose. [Conclusions] The Camellia-YOLACT model proposed got high accuracy in leaf and anthracnose segmentation of Camellia oleifera, on the basis of which it can realize automatic grading of the severity of Camellia oleifera anthracnose. This research could provide technical support for the precise control of Camellia oleifera diseases.

Key words: Camellia oleifera, leaf disease, anthracnose, BiFPN, YOLACT, Transformer, deep learning

NIE Ganggang, RAO Honghui, LI Zefeng, LIU Muhua. Severity Grading Model for Camellia Oleifera Anthracnose Infection Based on Improved YOLACT[J]. Smart Agriculture, 2024, 6(3): 138-147.

Figures/Tables 13

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References 23

1	张立伟, 王辽卫. 我国油茶产业的发展现状与展望[J]. 中国油脂, 2021, 46(6): 6-9, 27.
	ZHANG L W, WANG L W. Prospect and development status of oil-tea camellia industry in China[J]. China oils and fats, 2021, 46(6): 6-9, 27.
2	吴鹏飞, 姚小华. 种植密度对普通油茶炭疽病病害发生的影响[J]. 中国油料作物学报, 2019, 41(3): 455-460.
	WU P F, YAO X H. Effect of planting density on anthracnose occurrence of Camellia oleifera [J]. Chinese journal of oil crop sciences, 2019, 41(3): 455-460.
3	张蕊, 李锦涛. 基于深度学习的场景分割算法研究综述[J]. 计算机研究与发展, 2020, 57(4): 859-875.
	ZHANG R, LI J T. A Survey on Algorithm Research of Scene Parsing Based on Deep Learning[J]. Journal of computer research and development, 2020, 57(4): 859-875.
4	HAQUE M A, MARWAHA S, ARORA A, et al. A lightweight convolutional neural network for recognition of severity stages of maydis leaf blight disease of maize[J]. Frontiers in plant science, 2022, 13: ID 1077568.
5	PRABHAKAR M, PURUSHOTHAMAN R, AWASTHI D P. Deep learning based assessment of disease severity for early blight in tomato crop[J]. Multimedia tools and applications, 2020, 79(39): 28773-28784.
6	TENDANG S, CHAMNONGTHAI K. Rice-disease severity level estimation using deep convolutional neural network[C]// 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC). Piscataway, New Jersey, USA: IEEE, 2021: 1-4.
7	万军杰, 祁力钧, 卢中奥, 等. 基于迁移学习的GoogLeNet果园病虫害识别与分级[J]. 中国农业大学学报, 2021, 26(11): 209-221.
	WAN J J, QI L J, LU Z A, et al. Recognition and grading of diseases and pests in orchard by GoogLeNet based on transfer learning[J]. Journal of China agricultural university, 2021, 26(11): 209-221.
8	LIU B, DING Z, TIAN L, et al. Grape leaf disease identification using improved deep convolutional neural networks[J]. Frontiers in plant science, 2020, 11: ID 1082.
9	王振, 张善文, 赵保平. 基于级联卷积神经网络的作物病害叶片分割[J]. 计算机工程与应用, 2020, 56(15): 242-250.
	WANG Z, ZHANG S W, ZHAO B P. Crop diseases leaf segmentation method based on cascade convolutional neural network[J]. Computer engineering and applications, 2020, 56(15): 242-250.
10	GARG K, BHUGRA S, LALL B. Automatic quantification of plant disease from field image data using deep learning[C]// 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). Piscataway, New Jersey, USA: IEEE, 2021: 1965-1972.
11	GONÇALVES JULIANO P, PINTO FRANCISCO A C, QUEIROZ DANIEL M, et al. Deep learning architectures for semantic segmentation and automatic estimation of severity of foliar symptoms caused by diseases or pests[J]. Biosystems engineering, 2021, 210: 129-142.
12	茹佳棋, 吴斌, 翁翔, 等. 基于改进UNet++模型的葡萄黑腐病病斑分割和病害程度分级[J]. 浙江农业学报, 2023, 35(11): 2720-2730.
	RU J Q, WU B, WENG X, et al. Disease spot segmentation and disease degree classification of grape black rot based on improved UNet++ model[J]. Acta agriculturae Zhejiangensis, 2023, 35(11): 2720-2730.
13	邓朝, 纪苗苗, 任永泰. 基于Mask R-CNN的马铃薯叶片晚疫病量化评价[J]. 扬州大学学报(农业与生命科学版), 2022, 43(1): 135-142.
	DENG Z, JI M M, REN Y T. Quantitative evaluation of potato late blight disease based on Mask R-CNN[J]. Journal of Yangzhou university (agricultural and life science edition), 2022, 43(1): 135-142.
14	LIU Z, LIN Y T, CAO Y, et al. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows[C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway, New Jersey, USA: IEEE, 2021: 10012-10022.
15	TAN M X, PANG R M, LE Q V. EfficientDet: scalable and efficient object detection[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New Jersey, USA: IEEE, 2020: 10781-10790.
16	安徽省市场监督管理局. 茶炭疽病测报调查与防治技术规程: DB34/T 3863—2021 [S].
17	SHU J H, NIAN F D, YU M H, et al. An improved mask R-CNN model for multiorgan segmentation[J]. Mathematical Problems in Engineering, 2020, 2020: 1-11.
18	ZHANG Z, HUANG S, LIU X, et al. Adversarial attacks on YOLACT instance segmentation[J]. Computers & Security, 2022, 116: ID 102682.
19	SHAFIQ M, GU Z. Deep residual learning for image recognition: A survey[J]. Applied sciences, 2022, 12(18): ID 8972.
20	杨毅, 桑庆兵. 多尺度特征自适应融合的轻量化织物瑕疵检测[J]. 计算机工程, 2022, 48(12): 288-295.
	YANG Y, SANG Q B. Lightweight-fabric defect detection based on adaptive fusion of multiscale features[J]. Computer engineering, 2022, 48(12): 288-295.
21	ZHU L, LEE F, CAI J, et al. An improved feature pyramid network for object detection[J]. Neurocomputing, 2022, 483: 127-139.
22	蓝金辉, 王迪, 申小盼. 卷积神经网络在视觉图像检测的研究进展[J]. 仪器仪表学报, 2020, 41(4): 167-182.
	LAN J H, WANG D, SHEN X P. Research progress on visual image detection based on convolutional neural network[J]. Chinese journal of scientific instrument, 2020, 41(4): 167-182.
23	WANG X, KONG T, SHEN C, et al. Solo: Segmenting objects by locations[C]// Computer Vision-ECCV 2020. ECCV 2020. Lecture Notes in Computer Science. Cham, Germany: Springer, 2020: 649-665.

严重程度分级	分级标准
0级	无病斑
1级	K≤25%
2级	25%< K≤50%
3级	50%< K≤75%
4级	K >75%

编号	主干网络	BiFPN	HardSwish	mAP₇₅/%	mAP_all/%	mAR/%
0	ResNet50	×	×	81.1	75.8	83.7
1	Swin-T	×	×	86.7	78.0	91.2
2	Swin-T	√	×	86.5	78.1	91.3
3	Swin-T	√	√	86.8	78.3	91.6

模型	mAP₇₅/%	mAP_all/%	mAR/%	td/ms
YOLACT	81.1	75.8	83.7	46.85
Mask R-CNN	90.4	81.5	92.3	168.16
SOLO	87.1	77.8	90.4	75.73
Camellia-YOLACT	86.8	78.3	91.6	53.50

编号	真实值		预测值		K绝对误差/%	编号	真实值		预测值		K绝对误差/%
编号	K/%	等级	K/%	等级	K绝对误差/%	编号	K/%	等级	K/%	等级	K绝对误差/%
1	58.31	3	57.28	3	1.03	19	80.68	4	77.11	4	3.57
2	43.14	2	43.05	2	0.09	20	23.92	1	21.31	1	2.61
3	0.00	0	0.00	0	0.00	21	6.26	1	6.24	1	0.02
4	7.32	1	6.84	1	0.48	22	16.55	1	13.69	1	2.86
5	26.08	2	24.56	1	1.52	23	22.32	1	20.28	1	2.04
6	8.54	1	8.03	1	0.51	24	7.83	1	7.04	1	0.79
7	63.88	3	63.03	3	0.85	25	21.80	1	21.50	1	0.30
8	10.39	1	9.34	1	1.05	26	0.00	0	0.00	0	0.00
9	52.65	3	49.12	2	3.53	27	8.18	1	7.67	1	0.51
10	76.44	4	75.34	4	1.10	28	12.97	1	10.95	1	2.02
11	53.34	3	53.26	3	0.08	29	19.50	1	18.38	1	1.12
12	0.00	0	0.00	0	0.00	30	13.77	1	13.55	1	0.22
13	0.00	0	0.00	0	0.00	31	28.68	2	27.64	2	1.04
14	12.19	1	11.29	1	0.90	32	7.21	1	4.50	1	2.71
15	18.18	1	17.99	1	0.20	33	14.81	1	13.07	1	1.74
16	47.29	2	46.20	2	1.09	34	5.04	1	4.72	1	0.32
17	62.31	3	59.92	3	2.39	35	11.06	1	10.50	1	0.56
18	36.39	2	35.86	2	0.53	36	18.22	1	16.85	1	1.37

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