摘要
中国现行的水稻冠层害虫为害状田间调查方法需要测报人员下田目测为害状发生情况,此种人工调查方法存在客观性差、效率低与劳动强度大等问题。近几年,诸多学者开始利用深度学习方法来识别植物病虫为害状,但大多针对单株或单个叶片上病虫害种类进行识别研究。该研究采集了水稻冠层多丛植株上稻纵卷叶螟和二化螟为害状图像,提出一种改进RetinaNet的水稻冠层害虫为害状自动检测模型。模型中采用ResNeXt101作为特征提取网络,组归一化(Group Normalization,GN)作为归一化方法,改进了特征金字塔网络(Feature Pyramid Network,FPN)结构。改进后的RetinaNet模型对2种害虫为害状区域检测的平均精度均值达到93.76%,为实现水稻害虫为害状智能监测提供了理论依据和技术支持。
In China,the current field survey methods of pest damage symptoms on rice canopy mainly rely on the forecasting technicians to estimate the sizes and numbers of damage symptoms by visual inspection for estimating the damage level of pests in paddy fields.The manual survey method is subjective,time-consuming,and labor-intensive.In this study,an improved RetinaNet model was proposed to automatically detect the damage symptom regions of two pests(Cnaphalocrocis medinalis and Chilo suppressalis)on rice canopy.This model was composed of one ResNeXt network,an improved feature pyramid network,and two fully convolutional networks(one was class subnet and the other was regression subnet).In this model,ResNeXt101 and Group Normalization were used as the feature extraction network and the normalization method respectively.The feature pyramid network was improved for achieving a higher detection rate of pest damage symptoms.The focal loss function was adopted in this model.All images were divided into two image sets including a training set and a testing set.The training images were augmented by flipping horizontally,enhancing contrast,and adding Gaussian noise methods to prevent overfitting problems.The damage symptom regions in training images were manually labeled by a labeling tool named LabelImg.6 RetinaNet models based on VGG16,ResNet101,ResNeXt101,data augmentation,improved feature pyramid network,and different normalization methods respectively were developed and trained on the training set.These models were tested on the same testing set.Precision-Recall curves,average precisions and mean average precisions of six models were calculated to evaluate the detection effects of pest damage symptoms on 6 RetinaNet models.All models were trained and tested under the deep learning framework PyTorch and the operating system Ubuntu16.04.The Precision-Recall curves showed that the improved RetinaNet model could achieve higher precision in the same recall rates than the other 5 models.The mean average precision of the model ba
作者
姚青
谷嘉乐
吕军
郭龙军
唐健
杨保军
许渭根
Yao Qing;Gu Jiale;Lyu Jun;Guo Longjun;Tang Jian;Yang Baojun;Xu Weigen(Department of Information,Zhejiang Sci-Tech University,Hangzhou 310016,China;China National Rice Research Institute,Hangzhou 311400,China;Plant Protection,Quarantine and Pesticide Management Station of Zhejiang,Hangzhou 310020,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2020年第15期182-188,共7页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家“863”计划项目(2013AA102402)
浙江省公益性项目(LGN18C140007)
浙江省自然科学基金(Y20C140024)。
