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

Xingmei Xu, Lu Wang, Xuewen Liang, Lei Zhou, Youjia Chen, Puyu Feng, Helong Yu () and Yuntao Ma ()
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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

Sustainability, 2023, vol. 15, issue 12, 1-17

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.

Keywords: maize; leaf counting; semi-supervised learning; UAV; SOLOv2; YOLOv5x (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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