High-throughput soybean pods high-quality segmentation and seed-per-pod estimation for soybean plant breeding

Abstract

Accurately identifying soybean pods is a crucial prerequisite for retrieving multi-phenotypic traits (such as number of pods per plant, number of seeds per pod, pod size, pod color, and pod shape). However, the traditional manual measurement approach for pod phenotype investigation is time-consuming and labor-intensive, particularly when counting the number of seeds per pod. Furthermore, existing instance segmentation methods designed for coarse-grained classification are inadequate for precise seed-per-pod estimation. To address these challenges, we modified an instance segmentation network for high-throughput soybean pods high-quality segmentation and accurate seed-per-pod estimation. We modified the classification branch of the instance segmentation network Mask Transfiner (Ke et al., 2022) by increasing the resolution of feature map of each Region of Interest (RoI) region, incorporating a dual attention mechanism, and leveraging a center loss function, named RefinePod. To overcome the limitation of scarce labeled data, we modified our synthesizing image method to automatedly generate fine-labeled multi-class soybean pods images. We then train RefinePod purely with these synthetic images. Subsequently, we evaluate the trained model on both synthetic and real test images. Experimental results demonstrate a significant improvement in the accuracy of seed-per-pod estimation achieved by RefinePod. Additionally, we conduct ablation experiments to analyze the individual contributions of each strategy employed in RefinePod. In summary, RefinePod achieves remarkable results in seed-per-pod estimation accuracy by integrating advanced techniques and leveraging synthetic images for training. Our findings highlight the potential of RefinePod for accelerating soybean phenotype investigation, enabling more efficient agricultural research and crop improvement initiatives.