Rice Seedling Counting in Complex Environments Based on Domain-Adaptive NWD-YOLOv5

Complex environments, such as green algae, that interfere with the counting of microscopic rice seedlings are often encountered in the early stages of rice cultivation, making it difficult to distinguish microscopic rice seedlings from the background, which can in turn degrade the performance of detection and counting models. However, current general-purpose deep learning methods face challenges in detecting tiny seedlings in complex cross-domain scenarios. Therefore, this paper proposes a domain-adaptive Normalized Gaussian Wasserstein Distance (NWD)-YOLOv5 model based on Mean Teacher to solve the problem of counting tiny rice seedlings from the perspective of an Unmanned Aerial Vehicle (UAV). To improve the detection and counting ability of tiny seedlings in complex backgrounds, a semi-supervised domain-adaptive training strategy based on the Mean Teacher model is integrated into the YOLOv5 network. Furthermore, as the loss function of YOLOv5, a prediction box metric based on NWD is used to improve the accuracy of positive and negative sample assignment for tiny objects. Experimental results show that the improved model has better generalizability compared with the original YOLOv5 model. The mAP@0.5 increases from 60.0% to 95.9%. Compared with other object detection models, the proposed domain adaptive model has greater advantages. Compared with the traditional manual method, the designed rice seedling counting method has an accuracy of 98.6%, achieves an R2 value of 0.900 3, and requires only one-fifth of the counting time required by the manual method. Ablation experiments show that the proposed domain-adaptive model achieves a performance that is comparable to that of Oracle, a supervised learning method, and is significantly superior to that of Source Only, a baseline method. This study provides insights to improve the accuracy of rice plant counting in complex and variable application environments and can serve as technical support for rice crop management methods.