YOLOrot2.0: A novel algorithm for high-precision rice seed size measurement with real-time processing

Abstract

We present YOLOrot2.0, an automated algorithm for measuring the dimensions of rice seeds, which holds significant importance in evaluating grain quality and genetic breeding research. The current methods for rice seed measurement exhibit various limitations, such as the need for seed preprocessing and selection, specialized equipment for measurement, and prolonged measurement time. To address these issues, YOLOrot2.0 introduces the following improvements: Firstly, it utilizes an anchor-free detection algorithm to optimize the detection of rotated targets. Secondly, the utilization of the Kalman Filter IoU loss function, which combines the horizontal bounding box and smooth L1 loss function, accelerates the convergence speed of the network. Moreover, the YOLOv8 architecture was adjusted by modifying certain convolutional layers and the Context to Fusion module to enhance the detection capabilities for smaller targets. Finally, YOLOrot2.0 enables direct processing of the entire image, eliminating the need for image partitioning and resulting in substantial time and processing savings. Experimental results demonstrate that YOLOrot2.0 achieves an mAP (mean average precision) score of 0.95, surpassing other comparative algorithms. Additionally, YOLOrot2.0 provides highly accurate measurements of seed length and width, closely aligned with ground truth values. Notably, YOLOrot2.0 achieves real-time detection by achieving an average detection time of only 11.5 ms for images sized 3000×4000 pixels.