基于卷积神经网络马铃薯叶片病害识别和病斑检测*

doi:10.13733/j.jcam.issn.20955553.2021.11.19

中国农机化学报 ›› 2021, Vol. 42 ›› Issue (11): 122-129.DOI: 10.13733/j.jcam.issn.20955553.2021.11.19

基于卷积神经网络马铃薯叶片病害识别和病斑检测^*

王林柏¹, 张博¹, 姚竟发¹, 杨志辉², 张君¹, 范晓飞¹

1.河北农业大学机电工程学院,河北保定,071000;
2.河北农业大学植物保护学院, 河北保定,071000

收稿日期:2021-03-02 修回日期:2021-04-14 出版日期:2021-11-15 发布日期:2021-11-15
通讯作者: 范晓飞,男,1976年生,河北张家口人,博士,教授;研究方向为农业人工智能。E-mail: leopardfxf@163.com
作者简介:王林柏,男,1994年生,河北保定人,硕士研究生,研究方向为图像识别与测控装置。E-mail: 542865054@qq.com
基金资助:
*国家自然科学基金面上项目(32072572);河北省重点研发项目(20327403D);河北省高层次人才资助项目(E2019100006);河北农业大学人才引进研究项目(YJ201847);华北作物改良与调控国家重点实验室开放课题资助

Potato leaf disease recognition and potato leaf disease spot detection based on Convolutional Neural Network

Wang Linbai¹, Zhang Bo¹, Yao Jingfa¹, Yang Zhihui², Zhang Jun¹, Fan Xiaofei¹

1. College of Mechanical and Electrical Engineering, Hebei Agriculture University, Baoding, 071000, China;
2. College of Plant Protection, Hebei Agriculture University, Baoding, 071000, China

Received:2021-03-02 Revised:2021-04-14 Online:2021-11-15 Published:2021-11-15

摘要/Abstract

摘要： 针对自然环境下马铃薯叶片病害识别率低和晚疫病斑定位难的问题,基于大田环境中采集的马铃薯叶片图像,首先对马铃薯叶片病害进行识别,对比AlexNet、VGG16、InceptionV3、ResNet50、MobileNet五种神经网络模型,结果表明InceptionV3模型的识别效果准确率最高,可达98.00%。其次对马铃薯叶片的晚疫病斑进行检测,提出一种改进型的CenterNet-SPP模型,该模型通过特征提取网络获取对象的中心点,再通过中心点回归获得中心点偏移量、目标大小等图像信息,训练后的模型在验证集下的mAP可达90.03%,以F₁为评价值分析对比其它目标检测模型,CenterNet-SPP模型的效果最好,准确率为94.93%,召回率为90.34%,F₁值为92.58%,平均检测一张图像耗时0.10 s。为自然环境下马铃薯叶片病害识别和检测提供较为全面的深度学习算法和模型研究基础。

关键词: 马铃薯叶片病害, 图像识别, 目标检测, 深度卷积神经网络

Abstract: This paper aims to solve the problems of low recognition rate of potato leaf diseases and difficulty in localization of late blight spots in the natural environment based on potato leaf images collected in the field environment. Firstly, the potato leaf disease was identified using five neural network models: ALEXNET, VGG16, InceptionV3, RESNET50, and MobileNet. The results show that the InceptionV3 model has the highest recognition accuracy, which can reach 98.00%. Secondly, the late blight spots of potato leaves were detected, and an improved CenterNet-SPP model was proposed. The model obtains the center point of the object through the feature extraction network and then obtains the image information such as the offset of the center point and the size of the target through the regression of the center point. The mAP of the trained model under the verification set was up to 90.03%.Using F₁ as the evaluation value analysis, compared with other objective detection models, the CenterNet-SPP model has the best effect, whose accuracy is 94.93%, recall rate is 90.34%, F₁ value is 92.58%, and the average detection time of an image is 0.10 s.This paper provides a relatively comprehensive deep learning algorithm and model research basis for potato disease recognition and detection in the natural environment.

Key words: potato leaf disease, image recognition, object detection, Deep Convolutional Neural Networks

中图分类号:

王林柏, 张博, 姚竟发, 杨志辉, 张君, 范晓飞. 基于卷积神经网络马铃薯叶片病害识别和病斑检测^*[J]. 中国农机化学报, 2021, 42(11): 122-129.

Wang Linbai, Zhang Bo, Yao Jingfa, Yang Zhihui, Zhang Jun, Fan Xiaofei. Potato leaf disease recognition and potato leaf disease spot detection based on Convolutional Neural Network[J]. Journal of Chinese Agricultural Mechanization, 2021, 42(11): 122-129.

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基于卷积神经网络马铃薯叶片病害识别和病斑检测^*

Potato leaf disease recognition and potato leaf disease spot detection based on Convolutional Neural Network

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