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和RetinaNet)和特征提取网络(VGG-16和ResNet-101)对水稻纹枯病病斑的检测效果,筛选出具有较好检测效果的模型。针对Cascade R-CNN模型检测纹枯病病斑存在漏检现象,根据纹枯病病斑呈现形状不规则、大小和位置多变的复杂情况,对Cascade R-CNN进行改进,添加OHEM结构均衡难易样本,选择边框回归损失函数,通过精准率、漏检率、平均精度和P-R曲线来评价不同模型的检测效果。在改进的Cascade R-CNN-OHEM-GIOU模型检测结果基础上,分别建立基于病斑面积和病斑数的水稻纹枯病丛发生危害分级模型,通过决定系数(R^(2))和Kappa值筛选分级模型。【结果】在相同主干网络条件下,Cascade R-CNN模型较RetinaNet模型对水稻纹枯病具有更好的检测效果,其中Cascade R-CNN-ResNet-101目标检测模型效果最佳,病斑检测准确率为92.4%,平均精度为88.2%,但漏检率为14.9%。改进的Cascade R-CNN-OHEM-GIOU检测模型有效解决了样本不均衡问题,添加边框回归损失函数有效降低了漏检率,较Cascade R-CNN-ResNet-101模型降低8.7%,平均精度提高到92.3%。以人工分级结果作为标准,基于病斑面积的水稻纹枯病发生危害分级模型在0—5级分级准确率分别为96.0%、90.0%、82.0%、76.0%、74.0%和96.0%,平均分级准确率为85.7%,Kappa系数为0.83,基于图像的水稻纹枯病丛发生危害分级与人工分级结果具有较高的一致性。【结论】基于图像的水稻纹枯病智能测报方法可实现病斑自动检测和发生危害自动分级,提高了测�
【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 probl
作者
韩晓彤
杨保军
李苏炫
廖福兵
刘淑华
唐健
姚青
HAN XiaoTong;YANG BaoJun;LI SuXuan;LIAO FuBing;LIU ShuHua;TANG Jian;YAO Qing(School of Information Science and Technology,Zhejiang Sci-Tech University,Hangzhou 310018;State Key Laboratory of Rice Biology,China National Rice Research Institute,Hangzhou 311401)
出处
《中国农业科学》
CAS
CSCD
北大核心
2022年第8期1557-1567,共11页
Scientia Agricultura Sinica
基金
国家重点研发计划(2021YFD1401100)
浙江省自然科学基金(LY20C140008)
所级统筹基本科研业务费项目(CPSIBRF-CNRRI-202123)。
