Growth monitoring of field-grown onion and garlic by CIE Lab color space and region-based crop segmentation of UAV RGB images

• Published in: Precision agriculture Vol. 24; no. 5; pp. 1982 - 2001
• Main Authors: Kim, Dong-Wook, Jeong, Sang Jin, Lee, Won Suk, Yun, Heesup, Chung, Yong Suk, Kwon, Young-Seok, Kim, Hak-Jin
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2023; Springer Nature B.V
• Subjects: Agriculture; Algorithms; Atmospheric Sciences; Biomedical and Life Sciences; Canopies; canopy; Chemistry and Earth Sciences; cold; Cold weather; color; Color imagery; Complexity; Computer Science; Crop growth; Crops; Damage prevention; Garlic; geometry; Growing season; Height; Illumination; Image processing; Image segmentation; Life Sciences; lighting; Mathematical models; Monitoring; Onions; Parameters; Physics; plant height; Plants (botany); plastic film mulches; precision; Regression analysis; Regression models; Remote Sensing/Photogrammetry; soil; Soil Science & Conservation; Statistics for Engineering; temporal variation; Unmanned aerial vehicles; Vegetables; vegetation; wind; CIE Lab color space; Crop segmentation; Growth monitoring; Unmanned aerial vehicle; Remote sensing; Crop with long narrow leaves
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-023-10026-8

Canopy coverage-based crop growth monitoring is highly dependent on the performance of crop segmentation algorithms. Under field conditions, crop segmentation for unmanned aerial vehicle (UAV) imagery should be sophisticated considering geometric distortion of images by wind and illumination variations. Under Korean cultivation conditions, a plastic mulch used to restrict weeds and prevent cold weather damage increases the complexity of the image background. In particular, on-site monitoring of onion and garlic growth has been limited by their morphology because they have long narrow leaves. The ultimate goal of this study was to quantify the growth parameters of onion and garlic at multiple growth stages using red, green, and blue (RGB) imagery obtained with UAVs. Canopy coverage and plant height were used as predictor variables to develop mathematical models to estimate the fresh weights of onion and garlic. The use of a CIE L*a*b* color space and mean shift (MS) algorithm enhanced the extraction of the canopy coverage of onion and garlic from complex backgrounds, including plastic mulch, soil, and shadows under varying illumination conditions. Multiple linear regression models consisting of the a* band-based vegetation fraction (VF) and structure from motion (SfM)-based plant height (PH) fitted the fresh weight data of onion and garlic well with high coefficients of determination (R 2 ) ranging from 0.82 to 0.92. The validation results showed an almost 1:1 slope with highly linear relationships (R 2  > 0.82) between the onion and garlic fresh weights obtained with the UAV RGB imagery and actual fresh weights, confirming that the UAV-RGB imagery based on the use of the a*band and PH can be used to quantify the spatial and temporal variability of onion and garlic growth parameters during the growing season.