|本期目录/Table of Contents|

[1]李子涵,周省邦,赵戈,等.基于卷积神经网络的农业病虫害识别研究综述[J].江苏农业科学,2023,51(7):15-23.
 Li Zihan,et al.Study on agricultural pest identification based on convolutional neural network: a review[J].Jiangsu Agricultural Sciences,2023,51(7):15-23.
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基于卷积神经网络的农业病虫害识别研究综述(PDF)
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《江苏农业科学》[ISSN:1002-1302/CN:32-1214/S]

卷:
第51卷
期数:
2023年第7期
页码:
15-23
栏目:
专论与综述
出版日期:
2023-04-05

文章信息/Info

Title:
Study on agricultural pest identification based on convolutional neural network: a review
作者:
李子涵周省邦赵戈张克智曾倩吴梦涛
南宁师范大学物理与电子学院,广西南宁 530299
Author(s):
Li Zihanet al
关键词:
卷积神经网络农作物病虫害图像识别病虫害数据集
Keywords:
-
分类号:
TP391.41
DOI:
-
文献标志码:
A
摘要:
农作物病虫害是当今农业生产需要解决的重要问题之一。基于卷积神经网络的图像识别技术是一种新型的图像与数据处理技术,利用该技术,可以在农业种植过程中,及时准确地分析作物病虫害,以便做出快速准确的反应。综述了利用卷积神经网络模型识别农作物病虫害识别技术在国内外的发展情况,讨论了所调研文献中的病虫害识别关键技术,包括数据源选择、数据预处理手段、卷积神经网络模型和算法优化方式的不同与相似之处。提出了数据获取、复杂图像检测、神经网络模型的低泛化性和高复杂性是该项技术现阶段的不足之处。并进一步指出,构建丰富的数据库与高性能的神经网络是未来发展的主要趋势,为今后的相关研究提供参考。
Abstract:
-

参考文献/References:

[1]吴孔明. 中国农作物病虫害防控科技的发展方向[J]. 农学学报,2018,8(1):35-38.
[2]翟肇裕,曹益飞,徐焕良,等. 农作物病虫害识别关键技术研究综述[J]. 农业机械学报,2021,52(7):1-18.
[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]Lawrence C,Moataz A,Mohammed A. Recent advances in image processing techniques for automated leaf pest and disease recognition:a review[J]. 农业信息处理(英文),2021(1):27-51.
[5]孙成会,薛凯鑫. 基于人工智能的图像识别技术分析[J]. 电子测试,2020(16):139-140.
[6]Goodfellow I,Bengio Y,Courville A. Deep learning[M]. Cambridge:MIT Press,2016:326-366.
[7]Gu J X,Wang Z,Kuen J,et al. Recent advances in convolutional neural networks[J]. Pattern Recognition,2018,77:354-377.
[8]Boulent J,Foucher S,Théau J,et al. Convolutional neural networks for the automatic identification of plant diseases[J]. Frontiers in Plant Science,2019,10:941.
[9]Vaishnnave M P,Devi K S,Ganeshkumar P. Automatic method for classification of groundnut diseases using deep convolutional neural network[J]. Soft Computing,2020,24(21):16347-16360.
[10]Priyadharshini R A,Arivazhagan S,Arun M,et al. Maize leaf disease classification using deep convolutional neural networks[J]. Neural Computing and Applications,2019,31(12):8887-8895.
[11]Liang W J,Zhang H,Zhang G F,et al. Rice blast disease recognition using a deep convolutional neural network[J]. Scientific Reports,2019,9:2869.
[12]Yin H L,Gu Y H,Park C J,et al. Transfer learning-based search model for hot pepper diseases and pests[J]. Agriculture,2020(10):439.
[13]Zheng Y Y,Kong J L,Jin X B,et al. CropDeep:the crop vision dataset for deep-learning-based classification and detection in precision agriculture[J]. Sensors,2019,19(5):1058.
[14]Jiang P,Chen Y H,Liu B,et al. Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks[J]. IEEE Access,2019,7:59069-59080.
[15]Tahir M B,Khan M A,Javed K,et al. WITHDRAWN:recognition of apple leaf diseases using deep learning and variances-controlled features reduction[J]. Microprocessors and Microsystems,2021:104027.
[16]Baranidharan B,Kumar C N S V,Babu M V. An improved inception layer-based convolutional neural network for identifying rice leaf diseases[M]//Intelligent learning for computer vision.Singapore:Springer Singapore,2021:119-129.
[17]Liu B,Ding Z F,Tian L L,et al. Grape leaf disease identification using improved deep convolutional neural networks[J]. Frontiers in Plant Science,2020,11:1082.
[18]Oppenheim D,Shani G. Potato disease classification using convolution neural networks[J]. Advances in Animal Biosciences,2017,8(2):244-249.
[19]Vallabhajosyula S,Sistla V,Kolli V K K. Transfer learning-based deep ensemble neural network for plant leaf disease detection[J]. Journal of Plant Diseases and Protection,2022,129(3):545-558.
[20]Mohanty S P,Hughes D P,Salathé M. Using deep learning for image-based plant disease detection[J]. Frontiers in Plant Science,2016,7:1419.
[21]Arsenovic M,Karanovic M,Sladojevic S,et al. Solving current limitations of deep learning based approaches for plant disease detection[J]. Symmetry,2019,11(7):939.
[22]Turkoglu M,Aslan M,Ar A,et al. A multi-division convolutional neural network-based plant identification system[J]. PeerJ,2021,7:e572.
[23]Sladojevic S,Arsenovic M,Anderla A,et al. Deep neural networks based recognition of plant diseases by leaf image classification[J]. Computational Intelligence and Neuroscience,2016,2016:3289801.
[24]Thenmozhi K,Reddy U S. Crop pest classification based on deep convolutional neural network and transfer learning[J]. Computers and Electronics in Agriculture,2019,164:104906.
[25]Saleem M H,Potgieter J,Arif K M. Plant disease classification:a comparative evaluation of convolutional neural networks and deep learning optimizers[J]. Plants,2020,9(10):1319.
[26]Krishnaswamy Rangarajan A,Purushothaman R. Disease classification in eggplant using pre-trained VGG16 and MSVM[J]. Scientific Reports,2020,10:2322.
[27]Rahat M,Hasan M,Hasan M M,et al. Deep CNN-based mango insect classification[M]//Algorithms for intelligent systems.Singapore:Springer Singapore,2021:67-85.
[28]Tian G L,Liu C,Liu Y,et al. Research on plant diseases and insect pests identification based on CNN[J]. IOP Conference Series Earth and Environmental Science,2020,594(1):012009.
[29]Mim T T,Sheikh M H,Chowdhury S,et al. Deep learning based sponge gourd diseases recognition for commercial cultivation in Bangladesh[M]//Advances in intelligent systems and computing.Cham:Springer International Publishing,2020:415-427.
[30]Singh P,Verma A,Alex J,et al. Disease and pest infection detection in coconut tree through deep learning techniques[J]. Computers and Electronics in Agriculture,2021,182:105986.
[31]Duan Y L,Li D D,Bi C K. Deep learning based pest identification on mobile[M]//Lecture notes of the Institute for Computer Sciences,Social Informatics and Telecommunications Engineering.Cham:Springer International Publishing,2020:123-128.
[32]Li D S,Wang R J,Xie C J,et al. A recognition method for rice plant diseases and pests video detection based on deep convolutional neural network[J]. Sensors,2020,20(3):578.
[33]He Y,Zhou Z Y,Tian L H,et al. Brown rice planthopper (Nilaparvata lugens Stal) detection based on deep learning[J]. Precision Agriculture,2020,21(6):1385-1402.
[34]Liu J,Wang X W. Tomato diseases and pests detection based on improved yolo V3 convolutional neural network[J]. Frontiers in Plant Science,2020,11:898.
[35]Barbedo J G. Plant disease identification from individual lesions and spots using deep learning[J]. Biosystems Engineering,2019,180:96-107.
[36]Luaibi A R,Salman T M,Miry A H. Detection of citrus leaf diseases using a deep learning technique[J]. International Journal of Electrical and Computer Engineering,2021,11(2):1719.
[37]Wang Q M,Qi F,Sun M H,et al. Identification of tomato disease types and detection of infected areas based on deep convolutional neural networks and object detection techniques[J]. Computational Intelligence and Neuroscience,2019,2019:9142753.
[38]Liu J,Wang X W. Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model[J]. Plant Methods,2020,16:83.
[39]翁杨,曾睿,吴陈铭,等. 基于深度学习的农业植物表型研究综述[J]. 中国科学(生命科学),2019,49(6):698-716.
[40]Li W Y,Wand D,Li M,et al. Field detection of tiny pests from sticky trap images using deep learning in agricultural greenhouse[J]. Computers and Electronics in Agriculture,2021,183:106048.
[41]Nieuwenhuizen A T,Hemming J,Suh H K,et al. Detection and classification of insects on stick-traps in a tomato crop using Faster R-CNN[C]//The Netherlands Conference on Computer Vision. 2018.
[42]王振华,李静,张鑫月,等. 面向视频数据的深度学习目标识别算法综述[J]. 计算机工程,2022,48(4):1-15.
[43]Goodfellow I J,Pouget-Abadie J,Mirza M,et al. Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems,New York:ACM,2014:2672-2680.
[44]Cubuk E D,Zoph B,Mané D,et al. AutoAugment:learning augmentation strategies from data[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Long Beach:IEEE,2019:113-123.
[45]Chen C J,Wu J S,Chang C Y,et al. Agricultural pests damage detection using deep learning[M]//Advances in networked-based information systems.Cham:Springer International Publishing,2019:545-554.
[46]Yosinski J,Clune J,Bengio Y,et al. How transferable are features in deep neural networks?[EB/OL]. (2014-11-06)[2022-04-10]. https://arxiv.org/pdf/1411.1792.pdf.
[47]乔梅生. 管涔山森林资源和土壤类型调查[J]. 陕西林业科技,2021,49(5):46-49,62.
[48]门秋雷. 昆虫世界的伪装大师[J]. 大自然探索,2018(5):36-45.

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备注/Memo

备注/Memo:
收稿日期:2022-05-31
基金项目:广西高校中青年教师科研基础能力提升项目(编号:2022KY0381);广西自然科学基金青年基金(编号:2020GXNSFBA297097)。
作者简介:李子涵(1996—),男,安徽滁州人,硕士研究生,研究方向为病虫害图像识别。E-mail:353885933@qq.com。
通信作者:周省邦,硕士,工程师,研究方向为计算机视觉,E-mail:zhoushengbang@foxmail.com;张克智,博士,讲师,研究方向为人工智能、图像处理、电子材料,E-mail:zhang_kz@126.com。
更新日期/Last Update: 2023-04-05