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Acta Agronomica Sinica ›› 2020, Vol. 46 ›› Issue (11): 1771-1779.doi: 10.3724/SP.J.1006.2020.94187

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

Classification of soybean pods using deep learning

YAN Zhuang-Zhuang2(), YAN Xue-Hui2, SHI Jia2, SUN Kai2, YU Jiang-Lin2, ZHANG Zhan-Guo1, HU Zhen-Bang3, JIANG Hong-Wei3, XIN Da-Wei3, LI Yang1, QI Zhao-Ming3, LIU Chun-Yan3, WU Xiao-Xia3, CHEN Qing-Shan3, ZHU Rong-Sheng1,*()   

  1. 1 College of Arts and Sciences, Northeast Agricultural University, Harbin 150030, Heilongjiang, China
    2 Engineering College Northeast Agricultural University, Harbin 150030, Heilongjiang, China
    3 Soybean Research Institute, Northeast Agricultural University, Harbin 150030, Heilongjiang, China
  • Received:2019-11-26 Accepted:2020-07-02 Online:2020-11-12 Published:2020-07-13
  • Contact: Rong-Sheng ZHU E-mail:zhuangyanneau@163.com;rshzhu@126.com
  • Supported by:
    This study was supported by the National Natural Science Foundation of China(31471516);the National Natural Science Foundation of China Youth Project(31400074)

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Abstract:

Crop phenotype investigation is a key task in the selection and breeding of crop varieties. The traditional phenotypic survey mainly relies on human labors, which makes the results of the phenotypic survey difficult to meet the requirements of automation, high precision and high reliability. In the investigation of soybean phenotypes, the correct identification of pod types is the key and premise for the accurate extraction of phenotypes such as the number, length and width of pods. This study focused on the pictures of mature soybean pods by using deep learning to migrate five different network models [AlexNet, VggNet (Vgg16, Vgg19), GoogleNet, ResNet-50], to identify one-pod, two-pod, three-pod, and four-pod. In order to improve training speed and accuracy, this experiment fine-tuning the model and selected different optimizers (SGD, Adam) to optimize the network model. Adam’s performance was better than SGD in the problem of pod identification. With the Vgg16 network model and the Adam optimizer, the test accuracy of the pod category reached 98.41%, which reflected the best performance in the selected network model. In the 10-fold cross-validation test, the Vgg16 network model had good stability. Therefore, this study indicates that the Vgg16 network model can be applied to the actual identification of pods, and provide an important solution for further automatic extraction of pod phenotypes.

Key words: soybean breeding, pod identification, deep learning, transfer learning

Fig. 1

Data augmentation a: rotate 90 degrees left and right clockwise; b: rotate 90 degrees counterclockwise; c: adjust brightness; d: rotate left and right; e: rotate up and down; f: adjust saturation; g: rotate 180 degrees counterclockwise."

Fig. 2

Network architecture of AlexNet"

Fig. 3

Deep network model performance under different algorithms a: the verification accuracy of the AlexNet model under the Adam algorithm and SGD algorithm; b: the verification accuracy of the Vgg16 model under the Adam algorithm and SGD algorithm; c: the verification accuracy of the GoogleNet model under the Adam algorithm and SGD algorithm; d: ResNet-50 The verification accuracy of the model under Adam algorithm and SGD algorithm. The horizontal axis is the number of iterations, and the vertical axis is the verification accuracy."

Fig. 4

Model accuracy (Accuracy ) and loss (Loss) diagrams a: the verification accuracy of the five models with the Adam training algorithm under the same data set; b: the verification loss of the five models with the Adam training algorithm under the same data set."

Table 1

Experimental results from different transfer models combined with Adam algorithm"

模型
Model		 层数
Layer		 迭代次数
Iteration		 时间
Time (min)		 准确率
Accuracy (%)
AlexNet (Adam) 8 2000 22.48 97.11
Vgg16 (Adam) 16 2000 144.56 98.41
Vgg19 (Adam) 19 2000 255.23 98.35
GoogleNet (Adam) 22 2000 18.13 95.83
ResNet-50 (Adam) 50 2000 24.25 87.15

Table 2

10-fold cross-validation results"

折次
k-fold		 验证准确率
Accuracy		 平均损失
Average loss
1-fold 0.9652 0.1574
2-fold 0.9552 0.2024
3-fold 0.9788 0.1779
4-fold 0.9623 0.1307
5-fold 0.9811 0.1523
6-fold 0.9623 0.2104
7-fold 0.9735 0.1903
8-fold 0.9782 0.1531
9-fold 0.9717 0.1658
10-fold 0.9693 0.1675
均值Mean 0.9697 0.1707
标准差Standard deviation 0.0085 0.0234

Fig. 5

Confusion matrix heat map of the model The columns in the confusion matrix are predicted class, and the rows are true class. The rightmost column of the figure is the precision, and the bottom row is the recall."

Table 3

Classification performance of the model"

豆荚种类
Pod class		 精确率
Precision (%)		 召回率
Recall (%)		 F1分数
F1-score (%)
一粒荚One-pod 99.5 98.1 98.8
二粒荚Two-pod 95.8 96.8 96.3
三粒荚Three-pod 95.6 95.6 95.6
四粒荚Four-pod 98.3 98.6 98.4
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