Modeling and Testing of Growth Status for Chinese Cabbage and White Radish with UAV-Based RGB Imagery

Remote Sensing

Hee Sup Yun¹  Won Suk Lee¹  Sang-Jin Jeong²  Young-Seok Kwon³  Suk-Gu Kim⁴  Dong-Wook Kim⁴  Hak-Jin Kim⁵ 

[1] Biomaterials Science and Engineering, College of Agriculture and Life Sciences, Seoul National University, Seoul KS013, Korea;;ADAS Segment, Software Solution R&D Center, PLK Technologies, Seoul KS013, Korea;;Department of Biosystems &Department of Horticultural Crop Research, National Institute of Horticultural and Herbal Science, Muan KS005, Korea;

关键词: unmanned aerial vehicle;    RGB;    vegetation fraction;    plant height;    structure from motion;    crop surface model;   

DOI  :  10.3390/rs10040563

来源: DOAJ

【 摘 要 】

Conventional crop-monitoring methods are time-consuming and labor-intensive, necessitating new techniques to provide faster measurements and higher sampling intensity. This study reports on mathematical modeling and testing of growth status for Chinese cabbage and white radish using unmanned aerial vehicle-red, green and blue (UAV-RGB) imagery for measurement of their biophysical properties. Chinese cabbage seedlings and white radish seeds were planted at 7–10-day intervals to provide a wide range of growth rates. Remotely sensed digital imagery data were collected for test fields at approximately one-week intervals using a UAV platform equipped with an RGB digital camera flying at 2 m/s at 20 m above ground. Radiometric calibrations for the RGB band sensors were performed on every UAV flight using standard calibration panels to minimize the effect of ever-changing light conditions on the RGB images. Vegetation fractions (VFs) of crops in each region of interest from the mosaicked ortho-images were calculated as the ratio of pixels classified as crops segmented using the Otsu threshold method and a vegetation index of excess green (ExG). Plant heights (PHs) were estimated using the structure from motion (SfM) algorithm to create 3D surface models from crop canopy data. Multiple linear regression equations consisting of three predictor variables (VF, PH, and VF × PH) and four different response variables (fresh weight, leaf length, leaf width, and leaf count) provided good fits with coefficients of determination (R2) ranging from 0.66 to 0.90. The validation results using a dataset of crop growth obtained in a different year also showed strong linear relationships (R2 > 0.76) between the developed regression models and standard methods, confirming that the models make it possible to use UAV-RGB images for quantifying spatial and temporal variability in biophysical properties of Chinese cabbage and white radish over the growing season.

【 授权许可】

Unknown