Newswise — A research team has developed the SCAG algorithm for accurate branch detection and angle calculation in soybean plants using LiDAR data. SCAG achieved high accuracy in branch detection ( F-score =0.77) and angle calculation ( r =0.
84), outperforming traditional methods. This algorithm identified novel, heritable traits for evaluating soybean density tolerance, such as the average angle to height ratio (AHR) and the angle to stem length ratio (ALR). The open-source SCAG can be applied to other crops, enhancing plant architecture characterization and aiding in ideal variety selection for improved agricultural outcomes.
Soybean is a major source of plant oil and protein, crucial for meeting the demands of a growing global population. Increasing soybean productivity is a long-term breeding goal, but limited arable land and various stresses, particularly nutrient stress, pose challenges. Traditional stress monitoring methods are labor-intensive and inefficient, whereas remote sensing techniques have their own limitations.
Current research highlights the potential of deep learning, particularly Transformer architecture, to enhance hyperspectral imaging analysis. However, combining deep learning with hyperspectral imaging for identifying soybean nutrient stress remains underexplored, which necessitates further investigation. A study (DOI: 10.
34133/plantphenomics.0190) published in Plant Phenomics on 19 May 2024, introduces the SCAG algorithm for accurate branch detectio.
