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

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.

Mapping Maize Planting Densities Using Unmanned Aerial Vehicles, Multispectral Remote Sensing, and Deep Learning Technology

“…For instance, Vong et al and Gao et al [35] employed the U-Net and Mask R-CNN models, respectively, for image segmentation. In another approach involving the use of object detection techniques in deep learning, Liu et al [34] selected YOLOv3, while Xu et al [36] and Cardellicchio et al [67] opted for YOLOv5 for accurate crop quantity estimations. In this study, UHDI-OD employed YOLOv8 for maize object detection and, subsequently, when combined with the measured planting areas, the maize planting densities were obtained (Figure 6).…”

“…Additionally, training YOLO models imposes certain demands on computational resources. In previous studies applying deep learning models for maize quantity estimations, the UAV flight heights were typically chosen to be in the range of 5-20 m [34,36,70], indicating the strict requirements of the UHDI-OD method for ultrahigh-definition digital images. Acquiring such ultrahigh-definition digital images often involves the use of UAV-carried RGB digital cameras for low-altitude image collection, which might pose challenges for monitoring large-scale field planting densities within a reasonable period.…”

“…Gao et al [35] fine-tuned the Mask Region-convolutional neural network (R-CNN) model for automatic maize identification, achieving an average precision (AP) @ 0.5 intersection over union (IOU) of 0.729 and an average recall (AR)@ 0.5IOU of 0.837. Xu et al [36], Xiao et al [22], and others used the YOLOv5 model to estimate the maize plant counts from UAV digital images. Lucas et al [37] proposed a CNN-based deep learning method for maize plant counting, with a precision of 85.6% and a recall of 90.5%.…”

Wheat Teacher: A One-Stage Anchor-Based Semi-Supervised Wheat Head Detector Utilizing Pseudo-Labeling and Consistency Regularization Methods

“…Casado et al [41] extended object detection to semi-supervised semantic segmentation, aiming to segment mature grapes in vineyards. Xu et al [42], utilizing YOLOv5X, constructed a semi-supervised semantic segmentation model for Maize Seedling Leaf counting. Johanson et al [43] introduced S3AD, a semi-supervised detection system based on contextual attention and selective tiling, addressing small apple detection.…”

YOSBG: UAV image data-driven high-throughput field tobacco leaf counting method

“…It showed superiority in wheat ear count, corn tassel count, and validation of sorghum ear count, but had a slower detection speed. Xu X et al [24] proposed a semi-supervised learning framework based on Noisy Student that uses YOLOv5x to identify and count maize leaves after first segmenting entire maize seedlings and creating foreground images with background eliminated using the SOLOv2 model. This method requires only a modest amount of labeled data, reducing the amount of work required for early data annotation.…”