TY - JOUR
T1 - Automated segmentation of individual leafy potato stems after canopy consolidation using YOLOv8x with spatial and spectral features for UAV-based dense crop identification
AU - Jiang, Hanhui
AU - Gilbert Murengami, Bryan
AU - Jiang, Liguo
AU - Chen, Chi
AU - Johnson, Ciarán
AU - Auat-Cheein, Fernando
AU - Fountas, Spyros
AU - Li, Rui
AU - Fu, Longsheng
PY - 2024/4
Y1 - 2024/4
N2 - High throughput phenotyping of potatoes after canopy consolidation is crucial to crop breeding and management. A prior step is to segment their leafy potato stems, which is challenging after canopy consolidation because potato stems are dense and intertwined. Current methods for dense crop segmentation are manual. This study equipped unmanned aerial vehicles with a high-resolution RGB sensor in ultra-low flight as a high-throughput alternative. An end-to-end method was proposed to segment their leafy potato stems using YOLOv8x and five kinds of band combinations, i.e., RGB, RGB-DSM, RGB-CHM, RGB-DSM × 3, RGB-ExG. The YOLOv8x model with the RGB-DSM combination achieved superior performance with F1 score of 0.86 and Intersection over Union (IoU) of 0.83. Both F1 score and IoU improved by more than 16 %, when adding DSM or CHM to RGB images. Results demonstrated that height mutation at the edge of leafy potato stems played a crucial role in improving the segmentation of leafy potato stems. Millimeter-level ground sampling distance facilitates high throughput phenotyping of potatoes. The accuracy and efficiency of YOLOv8x has great potential for guiding the phenotypic automation of potatoes as well as other arable crops through remote sensing.
AB - High throughput phenotyping of potatoes after canopy consolidation is crucial to crop breeding and management. A prior step is to segment their leafy potato stems, which is challenging after canopy consolidation because potato stems are dense and intertwined. Current methods for dense crop segmentation are manual. This study equipped unmanned aerial vehicles with a high-resolution RGB sensor in ultra-low flight as a high-throughput alternative. An end-to-end method was proposed to segment their leafy potato stems using YOLOv8x and five kinds of band combinations, i.e., RGB, RGB-DSM, RGB-CHM, RGB-DSM × 3, RGB-ExG. The YOLOv8x model with the RGB-DSM combination achieved superior performance with F1 score of 0.86 and Intersection over Union (IoU) of 0.83. Both F1 score and IoU improved by more than 16 %, when adding DSM or CHM to RGB images. Results demonstrated that height mutation at the edge of leafy potato stems played a crucial role in improving the segmentation of leafy potato stems. Millimeter-level ground sampling distance facilitates high throughput phenotyping of potatoes. The accuracy and efficiency of YOLOv8x has great potential for guiding the phenotypic automation of potatoes as well as other arable crops through remote sensing.
KW - Deep learning
KW - Instance segmentation
KW - Potato phenotyping
KW - Spectral feature
KW - UAV imagery
UR - http://www.scopus.com/inward/record.url?scp=85186623323&partnerID=8YFLogxK
U2 - 10.1016/j.compag.2024.108795
DO - 10.1016/j.compag.2024.108795
M3 - Article
SN - 0168-1699
VL - 219
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
M1 - 108795
ER -