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Monitoring of Soybean Maturity Using UAV Remote Sensing and Deep Learning

Author

Listed:
  • Shanxin Zhang

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China)

  • Hao Feng

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China)

  • Shaoyu Han

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
    Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China)

  • Zhengkai Shi

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China)

  • Haoran Xu

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China)

  • Yang Liu

    (Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
    Key Lab of Smart Agriculture System, Ministry of Education, China Agricultural University, Beijing 100083, China)

  • Haikuan Feng

    (Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
    College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China)

  • Chengquan Zhou

    (Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
    Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences (ZAAS), Hangzhou 310000, China)

  • Jibo Yue

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China)

Abstract

Soybean breeders must develop early-maturing, standard, and late-maturing varieties for planting at different latitudes to ensure that soybean plants fully utilize solar radiation. Therefore, timely monitoring of soybean breeding line maturity is crucial for soybean harvesting management and yield measurement. Currently, the widely used deep learning models focus more on extracting deep image features, whereas shallow image feature information is ignored. In this study, we designed a new convolutional neural network (CNN) architecture, called DS-SoybeanNet, to improve the performance of unmanned aerial vehicle (UAV)-based soybean maturity information monitoring. DS-SoybeanNet can extract and utilize both shallow and deep image features. We used a high-definition digital camera on board a UAV to collect high-definition soybean canopy digital images. A total of 2662 soybean canopy digital images were obtained from two soybean breeding fields (fields F1 and F2). We compared the soybean maturity classification accuracies of (i) conventional machine learning methods (support vector machine (SVM) and random forest (RF)), (ii) current deep learning methods (InceptionResNetV2, MobileNetV2, and ResNet50), and (iii) our proposed DS-SoybeanNet method. Our results show the following: (1) The conventional machine learning methods (SVM and RF) had faster calculation times than the deep learning methods (InceptionResNetV2, MobileNetV2, and ResNet50) and our proposed DS-SoybeanNet method. For example, the computation speed of RF was 0.03 s per 1000 images. However, the conventional machine learning methods had lower overall accuracies (field F2: 63.37–65.38%) than the proposed DS-SoybeanNet (Field F2: 86.26%). (2) The performances of the current deep learning and conventional machine learning methods notably decreased when tested on a new dataset. For example, the overall accuracies of MobileNetV2 for fields F1 and F2 were 97.52% and 52.75%, respectively. (3) The proposed DS-SoybeanNet model can provide high-performance soybean maturity classification results. It showed a computation speed of 11.770 s per 1000 images and overall accuracies for fields F1 and F2 of 99.19% and 86.26%, respectively.

Suggested Citation

  • Shanxin Zhang & Hao Feng & Shaoyu Han & Zhengkai Shi & Haoran Xu & Yang Liu & Haikuan Feng & Chengquan Zhou & Jibo Yue, 2022. "Monitoring of Soybean Maturity Using UAV Remote Sensing and Deep Learning," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
    • Handle: RePEc:gam:jagris:v:13:y:2022:i:1:p:110-:d:1020839
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    References listed on IDEAS

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