Open cotton boll detection using LiDAR point clouds and RGB images from unmanned aerial systems

Abstract

Accurate quantification of open bolls and their distribution is crucial for understanding cotton growth, development, and yield in optimized crop management and enhanced plant breeding. Manual boll counting methods are time-consuming, labor-intensive, and subjective. Leveraging the potential of high-resolution images for high-throughput phenotyping offers a promising avenue for efficient trait quantification. The objectives of this study were to develop methods to detect and count open cotton bolls using LiDAR point cloud and RGB images and to compare the effectiveness of these two data sources. A DJI Phantom 4 RTK Unmanned Aerial System (UAS) equipped with a 4 K RGB camera was used to acquire high-resolution RGB images, and a DJI Matrice 300 RTK with a Zenmuse L1 sensor was used to acquire LiDAR point cloud data. The RGB images were converted to point cloud using photogrammetry by measuring multiple points of overlapping images. The boll detection workflow involved data filtering and clustering using the density-based spatial clustering of applications with noise (DBSCAN) method. Evaluation of the methods involved 48 plots representing small, medium, and large plant sizes using metrics including mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (r²). The methods using both data sources performed well in estimating open bolls, with LiDAR point cloud data slightly outperforming those derived from RGB images. Generally, the performance of the DBSCAN method in boll detection improved with decreasing plant sizes. Specifically, LiDAR data yielded MAPE values of 5.03 %, 8.05 %, and 13.46 %, RMSE values of 7.26, 14.33, and 23.40 bolls per m², and r 2 values of 0.93, 0.84, and 0.84 for small, medium, and large plant sizes, respectively. RGB image-based data exhibited MAPE values of 7.21 %, 6.49 %, and 16.41 %, RMSE values of 11.05, 13.66, and 26.49 bolls per m², and r 2 values of 0.82, 0.74, and 0.83 for small, medium, and large plant sizes, respectively. The method demonstrates the potential of RGB imagery and LiDAR data for estimating boll counts, offering valuable tools for enhanced plant phenotyping in plant breeding and site-specific crop management. Both data sources underestimated boll counts, with smaller plants showing less undercounting, likely due to improved light penetration and separation of bolls. These findings highlight the influence of plant structure on boll detection accuracy and the need to address challenges posed by dense canopies to enhance detection reliability. • The study developed methods to count open cotton bolls using LiDAR point clouds and RGB images. • LiDAR slightly outperformed the RGB image-derived point cloud, with better accuracy for smaller plants due to less canopy density. • Dense canopies reduced detection accuracy, highlighting the influence of plant structure.

Plant phenotyping relevance

LiDARとRGB画像を用いて綿花の開花ボール数を検出・計数する手法を開発し、精度比較・検証しており、植物表現型取得が研究の中心です。

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