基于图像的水稻纹枯病智能测报方法

doi:10.3864/j.issn.0578-1752.2022.08.007

Abstract

Abstract:

【Objective】At present, the forecast of rice sheath blight relies on the number of diseased clusters, the number of rice plants and the severity of each plant to calculate the disease index based on manual surveys. The method is highly professional, time-consuming and laborious. The data is difficult to trace. The objective of this study is to propose a detection model of rice sheath blight lesions and a damage grading model of rice sheath blight based on images, and to provide a theoretical basis for the intelligent forecasting of rice sheath blight.【Method】Images of rice sheath blight in paddy field were collected by a portable image acquisition instrument. Different detection models (Cascade R-CNN and RetinaNet) and feature extraction networks (VGG-16 and ResNet-101) were developed to test the detection effect of disease lesions. The best model was chosen. However, the Cascade R-CNN model appeared some missing detection of sheath blight lesions. Because the sheath blight lesions are irregular in shape, and variable in size and location, the Cascade R-CNN model was improved through adding OHEM structure to balance the hard and easy samples in the network and choosing the bounding box regression loss function. The precision rate, missing rate, average precision and P-R curve were used to evaluate the detection effects of different models. Based on the detection results of the improved Cascade R-CNN-OHEM-GIOU model, two damage grading models based on the area and number of disease lesions were developed, respectively. The determination coefficient (R²) and Kappa value were used to choose the damage level model of rice sheath blight. 【Result】Under the same backbone network conditions, the Cascade R-CNN model had a better detection effect on rice sheath blight than the RetinaNet model. The Cascade R-CNN-ResNet-101 model had the best detection effect on sheath blight lesions. The precision rate was 92.4%, the average precision was 88.2% and the missing rate was 14.9%. The improved Cascade R-CNN-OHEM- GIOU model effectively solved the problem of sample imbalance, and effectively reduced the missing rate by adding a border regression loss function, which was 8.7% lower than the missing rate of the Cascade R-CNN-ResNet-101 model, and the average precision was increased to 92.3%. With the results of manual disease grading as the standard, the grading model of rice sheath blight at 0 to 5 grades based on the area of diseased lesions had the accurate rates of 96.0%, 90.0%, 82.0%, 76.0%, 74.0% and 96.0%, respectively. The average grading accuracy rate was 85.7%, and the Kappa coefficient was 0.83. The damage grade results of rice sheath blight based on images were consistent with the manual grading results.【Conclusion】The intelligent forecasting method of rice sheath blight based on images can automatically detect the disease lesions and calculate the damage grade. This method increases the intelligence level and the results are objective and traceable. It may also provide an idea for intelligent forecasting of other crop diseases.

Key words: rice sheath blight, disease lesion image, intelligent forecasting, Cascade R-CNN model, damage grading model

HAN XiaoTong,YANG BaoJun,LI SuXuan,LIAO FuBing,LIU ShuHua,TANG Jian,YAO Qing. Intelligent Forecasting Method of Rice Sheath Blight Based on Images[J].Scientia Agricultura Sinica, 2022, 55(8): 1557-1567.

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References 26

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数据集 Dataset	有病斑的图像数 Number of images with lesions	无病斑的图像数 Number of images without lesions	病斑数量 Lesion number	每幅图像平均病斑数 Average lesion number per image
训练集 Training set	4122	896	25093	5.0
验证集 Validation set	1766	384	12257	5.7
测试集 Testing set	250	50	1620	5.4

模型种类 Model type	检测数 Number of detected lesions	正确识别数 Number of correct identified lesions	准确率 Precision rate (%)	漏检率 Missing rate (%)	平均精度 AP (%)
RetinaNet-VGG-16	1404	1230	87.6	24.1	/
RetinaNet-ResNet-101	1444	1311	90.8	19.1	/
Cascade R-CNN-VGG-16	1439	1298	90.2	19.9	/
Cascade R-CNN-ResNet-101	1492	1379	92.4	14.9	88.2
Cascade R-CNN-GIOU	1520	1401	92.2	13.5	89.1
Cascade R-CNN-OHEM-GIOU	1644	1519	92.4	6.2	92.3

Intelligent Forecasting Method of Rice Sheath Blight Based on Images

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人工分级 Manual grading	模型分级Model grading
人工分级 Manual grading	0	1	2	3	4	5	总计Total
0	48	2	0	0	0	0	50
1	1	45	4	0	0	0	50
2	0	2	41	7	0	0	50
3	0	0	2	38	10	0	50
4	0	0	0	1	37	12	50
5	0	0	0	0	2	48	50
总计Total	49	49	47	46	49	60	300
Kappa	0.83

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