| 基于图像的水稻纹枯病智能测报方法 |
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| 引用本文： | 韩晓彤,杨保军,李苏炫,廖福兵,刘淑华,唐健,姚青.基于图像的水稻纹枯病智能测报方法[J].中国农业科学,2022,55(8):1557-1567. |
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| 作者姓名： | 韩晓彤 杨保军 李苏炫 廖福兵 刘淑华 唐健 姚青 |
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| 作者单位： | 1浙江理工大学信息学院,杭州 3100182中国水稻研究所水稻生物学国家重点实验室,杭州 311401 |
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| 基金项目： | 国家重点研发计划（2021YFD1401100）；;浙江省自然科学基金（LY20C140008）； |
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| 摘 要： | 目的]目前水稻纹枯病测报依赖人工调查水稻发病丛数、株数和每株严重度来计算其病情指数,操作专业性强,费时费力且数据难以追溯.本研究提出基于图像的水稻纹枯病病斑检测模型和发生危害分级模型,为水稻纹枯病智能测报提供理论依据.方法]利用便携式图像采集仪采集田间水稻纹枯病图像,研究不同目标检测模型(Cascade R-CNN...
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| 关 键 词： | 水稻纹枯病 病斑图像 智能测报 Cascade R-CNN模型 危害分级模型 |
| 收稿时间： | 2021-11-03 |
Intelligent Forecasting Method of Rice Sheath Blight Based on Images |
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| 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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| Authors: | HAN XiaoTong YANG BaoJun LI SuXuan LIAO FuBing LIU ShuHua TANG Jian YAO Qing |
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| Institution: | 1School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 3100182State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 311401 |
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| 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. |
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| Keywords: | rice sheath blight disease lesion image intelligent forecasting Cascade R-CNN model damage grading model |
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