[1]牛学德,高丙朋,南新元,等.基于改进DenseNet卷积神经网络的番茄叶片病害检测[J].江苏农业学报,2022,38(01):129-134.[doi:doi:10.3969/j.issn.1000-4440.2022.01.015]
　NIU Xue-de,GAO Bing-peng,NAN Xin-yuan,et al.Detection of tomato leaf disease based on improved DenseNet convolutional neural network[J].,2022,38(01):129-134.[doi:doi:10.3969/j.issn.1000-4440.2022.01.015]
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基于改进DenseNet卷积神经网络的番茄叶片病害检测()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
38
期数:
2022年01期
页码:
129-134
栏目:
农业信息工程
出版日期:
2022-02-28

文章信息/Info

Title:
Detection of tomato leaf disease based on improved DenseNet convolutional neural network
作者:
牛学德; 高丙朋; 南新元; 石跃飞
（新疆大学电气工程学院,乌鲁木齐830047）
Author(s):
NIU Xue-de; GAO Bing-peng; NAN Xin-yuan; SHI Yue-fei
(College of Electrical Engineering, Xinjiang University, Urumqi 830047， China)
关键词:
图像识别; 番茄; 病害; 迁移学习; DenseNet卷积神经网络
Keywords:
image recognition; tomato; disease; transfer learning; DenseNet convolutional neural network
分类号:
TP391.4
DOI:
doi:10.3969/j.issn.1000-4440.2022.01.015
文献标志码:
A
摘要:
针对传统的图像识别方法存在人工提取特征困难、识别耗时长和准确率低等问题，本研究以感染病害的番茄叶片和健康番茄叶片共10类图像为研究对象，提出了1种迁移学习和DenseNet卷积神经网络相结合的模型，实现了对番茄叶部病害的准确分类。首先将所有的图像数据进行预处理修改尺寸，对部分数量不均衡样本作随机变换；然后将DenseNet网络从ImageNet数据集上学习获得的先验知识应用到番茄病害图片数据集上，进而构建出基于迁移学习的深度卷积网络，经过微调训练得到番茄叶部病害识别模型。结果表明，该模型与AlexNet网络、VGG网络+迁移学习和MobileNet网络+迁移学习3种深度卷积模型相比，识别精度更高，测试准确率达到97.76%，实现了对10种番茄叶部图像的有效分类，为番茄等农作物病害的识别技术以及智慧农业的发展提供了新的思路与方法。
Abstract:
Aiming at the problems of traditional image recognition methods such as difficulty in manually extracting features, long recognition time and low accuracy, ten types of images of healthy tomato leaves and tomato leaves infected with diseases were taken as the research objects, and a model combining transfer learning and DenseNet convolutional neural network was proposed to realize the accurate classification of tomato leaf diseases. Firstly, all image data were preprocessed to modify the size, and some unbalanced samples were randomly transformed. Then, the prior knowledge learned by DenseNet network from ImageNet dataset was applied to tomato disease image dataset, and a deep convolutional network based on transfer learning was constructed. Moreover, the tomato leaf disease identification model was obtained by fine-tuning training. The results showed that the recognition accuracy of this model was higher than that of AlexNet network, VGG network+transfer learning and MobileNet network+transfer learning, and the test accuracy was 97.76%. In short, the model constructed in this study can realize the effective classification of ten types of tomato leaf disease images, and can provide new ideas and methods for the identification technology of tomato and other crop diseases and the development of intelligent agriculture.

参考文献/References:

[1]周蓉,赵统敏,赵丽萍，等.高光泽番茄育种及果实表面光泽度[J].江苏农业学报,2018,34(6):1437-1440.
[2]王翔宇,温皓杰,李鑫星,等.农业主要病害检测与预警技术研究进展分析[J].农业机械学报,2016,47(9):266-277.
[3]孙红,李松,李民赞,等.农业信息成像感知与深度学习应用研究进展[J].农业机械学报,2020,51(5):1-17.
[4]YANE D.Design of intelligent agriculture management information system based on IoT [C].Shenzhen:IEEE, 2011.
[5]许良凤,徐小兵,胡敏,等.基于多分类器融合的玉米叶部病害识别[J].农业工程学报,2015,31(14):194-201，315.
[6]黄双萍,孙超,齐龙,等.基于深度卷积神经网络的水稻穗瘟病检测方法[J].农业工程学报,2017,33(20):169-176.
[7]李文勇,李明,陈梅香,等.基于机器视觉的作物多姿态害虫特征提取与分类方法[J].农业工程学报,2014, 30(14):154-162.
[8]IQBAL M A, TALUKDER K H. Detection of potato disease using image segmentation and machine learning[C]. Chennai:IEEE, 2020.
[9]MONDAL D, KOLE D K, ROY K. Gradation of yellow mosaic virus disease of okra and bitter gourd based on entropy based binning and Naive Bayes classifier after identification of leaves[J]. Computers and Electronics in Agriculture, 2017，142:485-493.
[10]张建华,祁力钧,冀荣华,等.基于粗糙集和BP神经网络的棉花病害识别[J].农业工程学报,2012,28(7):161-167.
[11]贾少鹏,高红菊,杭潇.基于深度学习的农作物病虫害图像识别技术研究进展[J].农业机械学报,2019,50(S1): 313-317.
[12]王文朋,毛文涛,何建樑,等.基于深度迁移学习的烟雾识别方法[J].计算机应用,2017,37(11):3176-3181，3193.
[13]HUANG G,LIU Z,MAATEN V D,et al.Densely connected convolutional networks[C]. Honolulu:IEEE, 2017: 2261-2269.
[14]DONG H,ZHANG L,ZOU B.Densely connected convolutional neural network based polarimetric SAR image classification[C].Yokohama:IEEE,2019.
[15]齐永锋,侯璐璐,段友放.基于DenseNet-BC网络的皮肤镜下皮肤损伤分割[J].计算机工程与科学,2020, 42(6):1060-1067.
[16]张永鹏,张春梅,白静.基于DenseNet-Attention模型的高光谱图像分类[J].图学学报,2020,41(6):897-904.
[17]PAN S J,YANG Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010,22(10):1345-1359.
[18]TIWARI D, ASHISH M, GANGWAR N,et al. Potato leaf diseases detection using deep learning[C]. Madurai:IEEE, 2020.
[19]张建华,孔繁涛,吴建寨,等.基于改进VGG卷积神经网络的棉花病害识别模型[J].中国农业大学学报,2018, 23(11):161-171.
[20]OPPENHEIM D, SHANI G, ERLICH O, et al. Using deep learning for image-based potato tuber disease detection[J]. Hytopathology,2019, 109(6):1083-1087.
[21]王艳玲,张宏立,刘庆飞,等.基于迁移学习的番茄叶片病害图像分类[J].中国农业大学学报,2019,24(6):124-130.
[22]柴帅,李壮举.基于迁移学习的番茄病虫害检测[J].计算机工程与设计,2019,40(6):1701-1705.
[23]DURMUS H, GUNES E O, KLRCL M. Disease detection on the leaves of the tomato plants by using deep learning[C]. Fairfax:IEEE, 2017: 1-5.
[24]郭小清,范涛杰,舒欣.基于改进Multi-Scale AlexNet的番茄叶部病害图像识别[J].农业工程学报,2019, 35(13):162-169.
[25]刘鹏鹏.基于深度学习的番茄叶面型病虫害识别研究[D].南昌：南昌大学,2020.
[26]胡志伟,杨华,黄济民,等.基于注意力残差机制的细粒度番茄病害识别[J].华南农业大学学报,2019,40(6):124-132.

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备注/Memo

备注/Memo:
收稿日期：2021-05-04基金项目：新疆维吾尔自治区自然科学基金项目（2019D01C079）作者简介：牛学德（1994-），男，河南周口人，硕士研究生，研究方向为深度学习在农作物病虫害识别上的应用。（E-mail）1176951424@qq.com通讯作者：高丙朋，（E-mail）xju1028@163.com
更新日期/Last Update: 2022-03-04