UK: Advancing soybean yield through high-throughput UAV phenotyping and dynamic modeling

Soybean

Published Mar 20, 2024

Tridge summary

A study in Plant Phenomics has shown the effectiveness of using UAV technology, deep learning, and dynamic modeling techniques for phenotyping various soybean genotypes. The research utilized a multimodal deep learning model, RIFSeg-Net, for soybean canopy segmentation and a SAM model for leaf extraction. The findings revealed significant differences in canopy cover across different soybean subgroups and emphasized the importance of early vigor in yield outcomes. This method could help in identifying soybean germplasm resources with beneficial traits for breeding more productive and resilient varieties.

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Original content

Soybeans, valued for their use as both oilseeds and grains, encounter yield limitations compared to crops like maize and rice, emphasizing the necessity for developing higher-yielding varieties.However, the relationship between the early growth of soybean canopies and overall yield remains inadequately understood, indicating a significant research gap. While advances in high-throughput phenotyping, particularly through UAV technology, have improved monitoring efficiency, they face challenges in data analysis accuracy, particularly in image segmentation.Plant Phenomics published research titled "Time-Series Field Phenotyping of Soybean Growth Analysis by Combining Multimodal Deep Learning and Dynamic Modelling."In this study, the effectiveness of RIFSeg-Net for soybean canopy segmentation was assessed using a multimodal deep learning model tailored specifically for analyzing UAV-captured multisource phenotypic data.The research involved comparative accuracy evaluation against ...

Source: Phys