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当前位置: 首页 > 文章 > 基于卷积神经网络的油茶害虫生态识别 广西林业科学 2023,52 (3) 361-366
Position: Home > Articles > Ecological Identification of Camellia spp.Pests Based on Convolutional Neural Networks Guangxi Forestry Science 2023,52 (3) 361-366

基于卷积神经网络的油茶害虫生态识别

作  者：
梁秀豪;杨丽萍;廖旺姣;黄丽芸;陈健武;阳文林;蒙芳;黄超航;韦维;王国全
单  位：
广西大学农学院;广西壮族自治区林业科学研究院广西特色经济林培育与利用重点实验室广西林业有害生物天敌繁育工程技术研究中心;浙江师范大学
关键词：
害虫识别;目标检测算法;油茶
摘  要：
害虫是影响油茶(Camellia spp.)产量的主要因素之一,对其进行准确识别有助于及时防控,减少损失.目前,油茶害虫识别研究缺少相关的数据集,限制了深度学习技术在油茶害虫识别中的应用.为给在生态环境下准确识别油茶害虫提供1种新范式,构建包含1116张7类害虫的油茶害虫识别图像数据集,采用4种目标检测算法(SSD、YOLOv3、YOLOX和RetinaNet)在该数据集上进行试验.结果表明,IOU阈值为0.5时,SSD的平均精度为93.50%,YOLOX为93.50%,RetinaNet为86.80%,YOLOv3为96.60%;SSD的平均召回率为73.20%,YOLOX为75.10%,RetinaNet为78.00%,YOLOv3为76.80%.综合分析,YOLOv3的检测和分类能力最优.
译  名：
Ecological Identification of Camellia spp.Pests Based on Convolutional Neural Networks
作  者：
Liang Xiuhao;Yang Liping;Liao Wangjiao;Huang Liyun;Chen Jianwu;Yang Wenlin;Meng Fang;Huang Chaohang;Wei Wei;Wang Guoquan;Guangxi Forestry Research Institute, Guangxi Key Laboratory of Special Non-wood Forests Cultivation and Utilization, Guangxi Forest Pests Natural Enemies Breeding Research Center of Engineering Technology;Zhejiang Normal University;College of Agriculture, Guangxi University;
关键词：
identification of pests;;object detection algorithm;;Camellia spp
摘  要：
Pests are one of the most significant factors affecting yields of Camellia spp., and accurate identification of pests is helpful to control timely and reduce losses. However, relevant datasets were lacked in identification researches of Camellia spp. pests, which limited application of deep learning technology. To provide a new model for accurate identification of Camellia spp. pests in ecological environment, a Camellia spp. pests recognition dataset was constructed, which contained 1 116 images of 7 classes of pests. Object detection algorithms SSD, YOLOv3, YOLOX and RetinaNet were experimented based on the dataset. Results showed that when IOU threshold was 0.5, average accuracy of SSD was 93.50%, YOLOX was 93.50%, RetinaNet was 86.80%, and YOLOv3 was 96.60%; average recall of SSD was 73.20%, YOLOX was 75.10%, RetinaNet was 78.00%, and YOLOv3 was 76.80%. YOLOv3 had the best abilities of detection and classification by comprehensive analysis.

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