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Maize Seedling Leave Counting Based on Semi-Supervised Learning and UAV RGB Images

Author

Listed:
  • Xingmei Xu

    (College of Information and Technology, Jilin Agricultural University, Changchun 130118, China)

  • Lu Wang

    (College of Information and Technology, Jilin Agricultural University, Changchun 130118, China)

  • Xuewen Liang

    (College of Information and Technology, Jilin Agricultural University, Changchun 130118, China)

  • Lei Zhou

    (College of Information and Technology, Jilin Agricultural University, Changchun 130118, China)

  • Youjia Chen

    (College of Land Science and Technology, China Agricultural University, Beijing 100193, China)

  • Puyu Feng

    (College of Land Science and Technology, China Agricultural University, Beijing 100193, China)

  • Helong Yu

    (College of Information and Technology, Jilin Agricultural University, Changchun 130118, China)

  • Yuntao Ma

    (College of Information and Technology, Jilin Agricultural University, Changchun 130118, China
    College of Land Science and Technology, China Agricultural University, Beijing 100193, China)

Abstract

The number of leaves in maize seedlings is an essential indicator of their growth rate and status. However, manual counting of seedlings is inefficient and limits the scope of the investigation. Deep learning has shown potential for quickly identifying seedlings, but it requires larger, labeled datasets. To address these challenges, we proposed a method for counting maize leaves from seedlings in fields using a combination of semi-supervised learning, deep learning, and UAV digital imagery. Our approach leveraged semi-supervised learning and novel methods for detecting and counting maize seedling leaves accurately and efficiently. Specifically, we used a small amount of labeled data to train the SOLOv2 model based on the semi-supervised learning framework Noisy Student. This model can segment complete maize seedlings from UAV digital imagery and generate foreground images of maize seedlings with background removal. We then trained the YOLOv5x model based on Noisy Student with a small amount of labeled data to detect and count maize leaves. We divided our dataset of 1005 images into 904 training images and 101 testing images, and randomly divided the 904 training images into four sets of labeled and unlabeled data with proportions of 4:6, 3:7, 2:8, and 1:9, respectively. The results indicated that the SOLOv2 Resnet101 outperformed the SOLOv2 Resnet50 in terms of segmentation performance. Moreover, when the labeled proportion was 30%, the student model SOLOv2 achieved a similar segmentation performance to the fully supervised model with a mean average precision (mAP) of 93.6%. When the labeled proportion was 40%, the student model YOLOv5x demonstrated comparable leaf counting performance to the fully supervised model. The model achieved an average precision of 89.6% and 57.4% for fully unfolded leaves and newly appearing leaves, respectively, with counting accuracy rates of 69.4% and 72.9%. These results demonstrated that our proposed method based on semi-supervised learning and UAV imagery can advance research on crop leaf counting in fields and reduce the workload of data annotation.

Suggested Citation

  • Xingmei Xu & Lu Wang & Xuewen Liang & Lei Zhou & Youjia Chen & Puyu Feng & Helong Yu & Yuntao Ma, 2023. "Maize Seedling Leave Counting Based on Semi-Supervised Learning and UAV RGB Images," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9583-:d:1171148
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    References listed on IDEAS

    as
    1. Han, Congying & Zhang, Baozhong & Chen, He & Wei, Zheng & Liu, Yu, 2019. "Spatially distributed crop model based on remote sensing," Agricultural Water Management, Elsevier, vol. 218(C), pages 165-173.
    2. Shenglian Lu & Zhen Song & Wenkang Chen & Tingting Qian & Yingyu Zhang & Ming Chen & Guo Li, 2021. "Counting Dense Leaves under Natural Environments via an Improved Deep-Learning-Based Object Detection Algorithm," Agriculture, MDPI, vol. 11(10), pages 1-16, October.
    3. Chuandong Zhang & Huali Ding & Qinfeng Shi & Yunfei Wang, 2022. "Grape Cluster Real-Time Detection in Complex Natural Scenes Based on YOLOv5s Deep Learning Network," Agriculture, MDPI, vol. 12(8), pages 1-12, August.
    4. Liang, Zhengyuan & van der Werf, Wopke & Xu, Zhan & Cheng, Jiali & Wang, Chong & Cong, Wen-Feng & Zhang, Chaochun & Zhang, Fusuo & Groot, Jeroen C.J., 2022. "Identifying exemplary sustainable cropping systems using a positive deviance approach: Wheat-maize double cropping in the North China Plain," Agricultural Systems, Elsevier, vol. 201(C).
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    Cited by:

    1. Zishang Yang & Jiawei Liu & Lele Wang & Yunhui Shi & Gongpei Cui & Li Ding & He Li, 2024. "Fast and Precise Detection of Dense Soybean Seedlings Images Based on Airborne Edge Device," Agriculture, MDPI, vol. 14(2), pages 1-21, January.
    2. Rui Zhang & Mingwei Yao & Zijie Qiu & Lizhuo Zhang & Wei Li & Yue Shen, 2024. "Wheat Teacher: A One-Stage Anchor-Based Semi-Supervised Wheat Head Detector Utilizing Pseudo-Labeling and Consistency Regularization Methods," Agriculture, MDPI, vol. 14(2), pages 1-21, February.

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