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ZHANG Z X, FENG Q S, LI Z X, LI Y H, WANG R J, ZHANG X F, LI Y Z, LIANG T G. Pest recognition model of cultivated alfalfa based on deep learning. Pratacultural Science, 2024, 41(6): 1519-1532. DOI: 10.11829/j.issn.1001-0629.2023-0064
Citation: ZHANG Z X, FENG Q S, LI Z X, LI Y H, WANG R J, ZHANG X F, LI Y Z, LIANG T G. Pest recognition model of cultivated alfalfa based on deep learning. Pratacultural Science, 2024, 41(6): 1519-1532. DOI: 10.11829/j.issn.1001-0629.2023-0064

Pest recognition model of cultivated alfalfa based on deep learning

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  • Corresponding author:

    FENG Qisheng E-mail: fengqsh@lzu.edu.cn

  • Received Date: February 10, 2023
  • Accepted Date: June 03, 2023
  • Available Online: July 23, 2023
  • Alfalfa (Medicago sativa) is a high-quality forb that is important for the development of animal husbandry in China. Pests and diseases are the main factors affecting alfalfa growth and quality; Therefore, accurate identification of these pests is crucial to alfalfa cultivation. One-stage object detection algorithms such as YOLO (You Only Look Once) performs target detection from end to end and the two-stage target detection algorithm based on RCNN (Region Convolutional Neural Network) generates candidate regions for feature extraction. To effectively identify alfalfa pests, YOLOv5 and Faster-RCNN are used to identify six common pests of alfalfa based on feature recognition in this study. The optimal algorithm and model for identifying alfalfa pests are determined based on four evaluation metrics: Recall, Precision, mAP, and F1 score. Recall is the proportion of positive cases in the sample that are correctly predicted, and F1 is the weighted average of R and P. The results Recall that YOLOv5 outperformed Faster-RCNN in identifying alfalfa pests, with higher precision scores on both the testing and validation sets. This study provides scientific and theoretical support for the selection of algorithms for identifying alfalfa pests, which has important implications for alfalfa cultivation management.

  • [1]
    王文信. 中国苜蓿种植业发展的对策. 北京农学院学报, 2022, 37(1): 117-120. doi: 10.13473/j.cnki.issn.1002-3186.2022.0120

    WANG W X. Countermeasures for the development of alfalfa planting industry in China. Journal of Beijing University of Agriculture, 2022, 37(1): 117-120. doi: 10.13473/j.cnki.issn.1002-3186.2022.0120
    [2]
    胡菊玲. 苜蓿害虫识别与综合防治技术. 现代畜牧科技, 2016(1): 36-37. doi: 10.3969/j.issn.1673-1921.2016.01.034

    HU J L. Identification and comprehensive control of alfalfa pests. Modern Animal Husbandry Science & Technology, 2016(1): 36-37. doi: 10.3969/j.issn.1673-1921.2016.01.034
    [3]
    ZHANG S W, JING R Z, SHI X L. Crop pest recognition based on a modified capsule network. Systems Science & Control Engineering, 2022, 10(1): 552-561.
    [4]
    LIU Y W, ZHANG X, GAO Y X, QU T G, SHI Y Q. Improved CNN method for crop pest identification based on transfer learning. Computational Intelligence and Neuroscience, 2022: https://doi.org/10.1155/2022/9709648.
    [5]
    KONG J L, WANG H X, YANG C C, JIN X B, ZUO M, ZHANG X. A spatial feature-enhanced attention neural network with high-order pooling representation for application in pest and disease recognition. Agriculture, 2022, 12(4): https://doi.org/10.3390/agriculture12040500.
    [6]
    TAO Z Y, HU Y L, LIN S. Finger vein recognition based on improved AlexNet. Laser & Optoelectronics Progress, 2020, 57(8): https://doi.org/10.3788/LOP57.081005.
    [7]
    WANG K, LIU M Z. Object recognition at night scene based on DCGAN and Faster R-CNN. IEEE Access, 2020, 8: 193168-193182. doi: 10.1109/ACCESS.2020.3032981
    [8]
    DU L, SUN Y Q, CHEN S, FENG J D, ZHAO Y D, YAN Z G, ZHANG X W, BIAN Y CH. A novel object detection model based on Faster R-CNN for Spodoptera frugiperda according to feeding trace of corn leaves. Agriculture, 2022, 12(2): 248. doi: 10.3390/agriculture12020248
    [9]
    郭阳, 许贝贝, 陈桂鹏, 丁建, 严志雁, 梁华, 吴昌华. 基于卷积神经网络的水稻害虫识别方法. 中国农业科技导报, 2021, 23(11): 99-109.

    GUO Y, XU B B, CHEN G P, DING J, YAN Z Y, LIANG H, WU C H. Rice insect pest recognition method based on convolutional neural network. Journal of Agricultural Science and Technology, 2021, 23(11): 99-109.
    [10]
    LV H H, YAN H B, LIU K Y, ZHOU Z W, JING J J. YOLOv5-AC: Attention mechanism-based light weight YOLOv5 for track pedestrian detection. Sensors, 2022, 22(15): 5903. doi: 10.3390/s22155903
    [11]
    ASAD M H, KHALIQ S, YOUSAF M H, ULLAH M O, AHMAD A. Pothole detection using deep learning: A real-time and ai-on-the-edge perspective. Advances in Civil Engineering, 2022: https://doi.org/10.1155/2022/9221211.
    [12]
    REDMON J, FARHADI A. YOLOv3: An incremental improvement. Arxiv E-Prints, 2018: https://doi.org/10.48550/arXiv.1804.02767.
    [13]
    KOLCHEV A, PASYNKOV D, EGOSHIN I, KLIOUCHKIN I, PASYNKOVA O, TUMAKOV D. YOLOv4-based CNN model versus nested contours algorithm in the suspicious lesion detection on the mammography image: A direct comparison in the real clinical settings. Journal of Imaging, 2022, 8(4): https://doi.org/10.3390/jimaging8040088.
    [14]
    ADIBHATLA V A, CHIH H C, HSU C C, CHENG J, ABBOD M F, SHIEH J S. Applying deep learning to defect detection in printed circuit boards via a newest model of you-only-look-once. Mathematical Biosciences and Engineering, 2018, 18(4): 4411-4428.
    [15]
    谷永立, 宗欣欣. 基于深度学习的目标检测研究综述. 现代信息科技, 2022, 6(11): 76-81. doi: 10.19850/j.cnki.2096-4706.2022.011.020

    GU Y L, ZONG X X. A review of object detection study based on deep learning. Modern Information Technology, 2022, 6(11): 76-81. doi: 10.19850/j.cnki.2096-4706.2022.011.020
    [16]
    CAO M L, FU H, ZHU J Y, CAI C G. Lightweight tea bud recognition network integrating GhostNet and YOLOv5. Mathematical Biosciences and Engineering, 2022, 19(12): 12897-12914. doi: 10.3934/mbe.2022602
    [17]
    FU H X, LI Y, WANG Y C, LI P. Maritime Ship Targets Recognition with Deep Learning. //CHEN X, ZHAO Q C. 37th Chinese Control Conference. Wuhan: Inst Electrical Electronics Engineers, 2018: 9297-9302.
    [18]
    杨文姬, 胡文超, 赵应丁, 钱文彬. 基于改进Yolov5植物病害检测算法研究. 中国农机化学报, 2023, 44(1): 108-115. doi: 10.13733/j.jcam.issn.2095-5553.2023.01.016

    YANG W J, HU W C, ZHAO Y D, QIAN W B. Research on plant disease detection algorithm based on improved Yolov5. Journal of Chinese Agricultural Mechanization, 2023, 44(1): 108-115. doi: 10.13733/j.jcam.issn.2095-5553.2023.01.016
    [19]
    LIU J, WANG X W. Early Recognition of tomato gray leaf spot disease based on Mobilenetv2-Yolov3 model. Plant Methods, 2021, 17(1): https://doi.org/10.1186/s13007-021-00708-7.
    [20]
    WU J, LI B, WU Z. Detection of crop pests and diseases based on deep convolutional neural network and improved algorithm. //ICMLT 2019. Advances in Proceedings of the 4th International Conference on Machine Learning Technologies. New York: Association for Computing Machinery, 2019: 20-27.
    [21]
    LIU J, WANG X W. Plant diseases and pests detection based on deep learning: A review. Plant Methods, 2021, 17(1): https://doi.org/10.1186/s13007-021-00722-9.
    [22]
    马琳琳, 马建新, 韩佳芳, 李雅迪. 基于YOLOv5s目标检测算法的研究. 电脑知识与技术, 2021, 17(23): 100-103.

    MA L L, MA J X, HAN J F, LI Y D. Intelligent garbage recognition based on target detection algorithm. Computer Knowledge and Technology, 2021, 17(23): 100-103.
    [23]
    XU X W, ZHANG X L, ZHANG T W. Lite-YOLOv5: A lightweight deep learning detector for on-board ship detection in large-scene Sentinel-1 SAR images. Remote Sensing, 2022, 14(4): https://doi.org/10.3390/rs14041018.
    [24]
    RAMALINGAM B, MOHAN R E, POOKKUTTATH S, GOMEZ B F, BORUSU CSC S, TENG T W, TAMILSELVAM Y K. Remote insects trap monitoring system using deep learning framework and IoT. Sensors, 2020, 20(18): https://doi.org/10.3390/s20185280.
    [25]
    LUO Y, ZHANG Y F, SUN X Z, DAI H W, CHEN X H. Intelligent solutions in chest abnormality detection based on YOLOv5 and ResNet50. Journal of Healthcare Engineering, 2021: https://doi.org/10.1155/2021/2267635.
    [26]
    鲍文霞, 邱翔, 胡根生, 梁栋, 黄林生. 基于椭圆型度量学习空间变换的水稻虫害识别. 华南理工大学学报, 2020, 48(10): 136-144.

    BAO W X, QIU X, HU G S, LIANG D, HUANG L S. Identification of rice pests based on space transformation by elliptic metric learning. Journal of South China University of Technology (Natural Science Edition), 2020, 48(10): 136-144.
    [27]
    李非非, 杨帆, 余飞, 季猛, 舒智慧, 徐杰. 基于人工智能的竹类主要害虫识别系统开发与应用. 世界竹藤通讯, 2021, 19(2): 27-33.

    LI F F, YANG F, YU F, JI M, SHU Z H, XU J. Development and application of main bamboo pests recognition system based on artificial intelligence. World Bamboo and Rattan, 2021, 19(2): 27-33.
    [28]
    王林惠, 兰玉彬, 刘志壮, 岳学军, 邓述为, 郭宜娟. 便携式柑橘虫害实时检测系统的研制与试验. 农业工程学报, 2021, 37(9): 282-288. doi: 10.11975/j.issn.1002-6819.2021.09.032

    WANG L H, LAN Y B, LIU Z Z, YUE X J, DENG S W, GUO Y J. Development and experiment of the portable real-time detection system for citrus pests. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(9): 282-288. doi: 10.11975/j.issn.1002-6819.2021.09.032
    [29]
    YANG F, WANG M. Deep learning-based method for detection of external air conditioner units from street view images. Remote Sensing, 2021, 13(18): https://doi.org/10.3390/rs13183691.
    [30]
    GU Y, WANG S C, YAN Y, TANG S J, ZHAO S D. Identification and analysis of emergency behavior of cage-reared laying ducks based on YOLOV5. Agriculture-Basel, 2022, 12(4): https://doi.org/10.3390/agriculture12040485.
    [31]
    LI W, ZHU T F, LI X Y, DONG J Z, LIU J. Recommending advanced deep learning models for efficient insect pest detection. Agriculture-Basel, 2022, 12(7): https://doi.org/10.3390/agriculture12071065.
    [32]
    LINJORDET T, BALOG K. Impact of training dataset size on neural answer selection models. //AZZOPARDI L. (eds). Advances in Information Retrieval. Cham: Springer International Publishing, 2019: 828-835.
    [33]
    WANG H, JIN Y, KE H, ZHANG X P. DDH-YOLOv5: Improved YOLOv5 algorithm based on double Iou: Aware decoupled head for object detection. Journal of Real-Time Image Processing, 2022, 19: 1023-1033. doi: 10.1007/s11554-022-01241-z
    [34]
    LU F, XIE F, SHEN S B, YANG J Q, ZHAO J, SUN R, HUANG L. The One-Stage detector algorithm based on background prediction and group normalization for vehicle detection. Applied Sciences-Basel, 2020, 10(17): https://doi.org/10.3390/app10175883.
    [35]
    郭文娟, 冯全, 李相周. 基于农作物病害检测与识别的卷积神经网络模型研究进展. 中国农机化学报, 2022, 43(10): 157-166. doi: 10.13733/j.jcam.issn.2095-5553.2022.10.023

    GUO W J, FENG Q, LI X Z. Research progress of convolutional neural network model based on crop disease detection and recognition. Journal of Chinese Agricultural Mechanization, 2022, 43(10): 157-166. doi: 10.13733/j.jcam.issn.2095-5553.2022.10.023

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