Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (3): 1131-1139.doi: 10.13229/j.cnki.jdxbgxb20200116

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Automatic recognition and classification of field insects based on lightweight deep learning model

Zhe-ming YUAN1,2(),Hong-jie YUAN1,2,Yu-xuan YAN2,Qian LI2,3,Shuang-qing LIU4,Si-qiao TAN1,2()   

  1. 1.Hunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making，Hunan Agricultural University，Changsha 410128，China
    2.Hunan Engineer Research Center for Information Technology in Agriculture，Hunan Agricultural University，Changsha 410128，China
    3.Business School，Hunan Agricultural University，Changsha 410128，China
    4.College of Plant Protection，Hunan Agricultural University，Changsha 410128，China
  • Received:2020-02-25 Online:2021-05-01 Published:2021-05-07
  • Contact: Si-qiao TAN E-mail:zhmyuan@sina.com;tsq@hunau.edu.cn

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Abstract:

Due to the complexity of the insect environment in the field and the imbalance in the number of samples among insect categories， the existing automatic identification and classification methods for field insects have high misidentification rates and low efficiency. In this paper， a new field insect automatic identification and classification algorithm is developed based on a lightweight deep learning model. First， preprocessing applied to the picture， then those images were input to the lightweight algorithm for feature extraction， and multi-scale feature fusion were adopted to output prediction networks of different sizes； then introduce joint cross-comparison for automatic identification and classification of field insects， and finally compare with the reference The algorithm has been simulated and compared. The results show that the field insect automatic identification and classification of the algorithm in this paper has high accuracy， less time， and strong robustness. It effectively solves the problems of insect accumulation and background interference and can identify field insects in real-time and online.

Key words: pattern recognition, deep learning, object detection, insect classification, lightweight algorithm, image preprocessing

CLC Number: 

  • TP181

Fig.1

Framework of lightweight insect detection and classification model"

Fig.2

Principle of depthwise separable convolution"

Table 1

Network parameters of feature extraction"

类型/步长卷积核尺寸输出尺寸
Conv/s23*3*3*32304*240*32
Conv_dw/s13*3*32304*240*32
Conv_dw/s11*1*32*64304*240*64
Conv_dw/s23*3*64152*120*64
Conv_dw/s11*1*64*128152*120*128
Conv_dw/s13*3*128152*120*128
Conv_dw/s11*1*128*128152*120*128
Conv_dw/s23*3*12876*60*128
Conv_dw/s11*1*128*25676*60*256
Conv_dw/s13*3*25676*60*256
Conv_dw/s11*1*256*25676*60*256
Conv_dw/s23*3*25638*30*256
Conv_dw/s11*1*256*51238*30*512
5×Conv_dw/s13*3*51238*30*512
Conv_dw/s11*1*512*51238*30*512
Conv_dw/s23*3*51219*15*512
Conv_dw/s11*1*512*102419*15*1024
Conv_dw/s13*3*102419*15*1024
Conv_dw/s11*1*1024*102419*15*1024

Fig.3

Comparison of prediction results of IoU and GIoU"

Fig.4

Some insect training set samples"

Fig.5

Sample of each classification"

Fig.6

Training loss function curve"

Table 2

Prediction effect of the object detection model in this paper on insects in each test set"

昆虫类别AP/%
均值70.98
Chilo suppressalis70.69
Lepidoptera64.96
Hydrophilidae latreille87.38
Coleoptera71.02
Gryllotalpidae orientalis87.71
Naranga aenescens53.82
Kirkaldyia deyrollei62.04
Cicadellidae70.21

Fig.7

Model detection results in different scenarios"

Fig.8

Model feature extraction layer visualization"

Table 3

Average recognition accuracy, detection speed and model size of different models for insects"

模型mAP/%检查速度/ms参数数量/ 百万模型体积/MB
YOLOv369.275361236
YOLOv3-TINY52.5132933
Faster R-CNN68.19325136532
本文70.9852830

Fig.9

Comparison of prediction results between YOLOV3 and the proposed algorithm"

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