Swin-Roleaf: A new method for characterizing leaf azimuth angle in large-scale maize plants

•Creating a maize leaf image dataset with orientation details using a customized UAV-based phenotyping platform.•Developing an automated pipeline for precise detection and quantification of leaf azimuth angle in large-scale maize plants.•The proposed method effectively tackles the challenges of detecting dense and overlapping maize leaves in the field. Maize is the most productive grain crop in the world in terms of total production and yield. Leaf azimuth angle, the directional orientation of the maize leaf, is an important characteristic that affects the efficiency of sunlight reception and photosynthesis in maize canopies. To study maize plant architecture, most of the previous studies have been focusing on leaf vertical angle characterization due to the inherent difficulty of measuring leaf azimuth angle accurately. Achieving accurate and efficient measurement of leaf azimuth angle in maize is important for yield improvement. Traditionally, leaf azimuth angle is measured manually using a protractor or manual annotations of images, which is time-consuming, costly, and prone to human error. Recent advances in Unmanned Aerial Vehicle (UAV) and artificial intelligence have shown great potential in leaf azimuth angle detection. However, most of the deep learning algorithms for object detection are developed for Horizontal Bounding Boxes (HBBs). To measure the azimuth angle of maize leaves, a robust Oriented Bounding Boxes (OBBs) detection algorithm is needed. In this study, a dataset containing top-view images of maize plants was collected using an UAV. An automated tool, Swin-Roleaf, was developed for maize leaf detection. It is an improvement of the YOLOv5 model, uniquely integrating the Swin Transformer (ST) and Circular Smooth Label (CSL). Swin-Roleaf was able to accurately detect OBBs of individual leaves and thereby enable the estimation of leaf azimuth angle. The experimental results showed that Swin-Roleaf was able to accurately detect maize leaves and measure the azimuth angle with a model OBBAP50 of 90.59% on OBBs. The system-derived leaf azimuth angle was highly correlated with the ground truth with a correlation coefficient larger than 0.96. Our study demonstrates for the first time the feasibility of using UAVs coupled with deep learning algorithms to detect leaf azimuth angles under field conditions. The system can significantly speed up the measurement of maize leaf azimuth angles and provide a new tool to accelerate breeding programs.