The estimation of crop emergence in potatoes by UAV RGB imagery

Plant Methods
Bo LiJiangang Liu

Abstract

Crop emergence and canopy cover are important physiological traits for potato (Solanum tuberosum L.) cultivar evaluation and nutrients management. They play important roles in variety screening, field management and yield prediction. Traditional manual assessment of these traits is not only laborious but often subjective. In this study, semi-automated image analysis software was developed to estimate crop emergence from high-resolution RGB ortho-images captured from an unmanned aerial vehicle (UAV). Potato plant objects were extracted from bare soil using Excess Green Index and Otsu thresholding methods. Six morphological features were calculated from the images to be variables of a Random Forest classifier for estimating the number of potato plants at emergence stage. The outputs were then used to estimate crop emergence in three field experiments that were designed to investigate the effects of cultivars, levels of potassium (K) fertiliser input, and new compound fertilisers on potato growth. The results indicated that RGB UAV image analysis can accurately estimate potato crop emergence rate in comparison to manual assessment, with correlation coefficient ( r 2 ) of 0.96 and provide an efficient tool to evaluate emergence...Continue Reading

References

Oct 9, 2012·NeuroImage·Katherine R GrayUNKNOWN Alzheimer's Disease Neuroimaging Initiative
Jun 1, 2017·Frontiers in Plant Science·Shouyang LiuMatthieu Hemmerlé
May 24, 2018·International Journal of Biological Macromolecules·Wei ZhangZhuqing Zhao

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Citations

Apr 24, 2021·Plant Phenomics : a Science Partner Journal·Meiyan ShuYuntao Ma
Jul 2, 2021·Plant Phenomics : a Science Partner Journal·Wei GuoBaskar Ganapathysubramanian
Jul 11, 2021·Environmental Science and Pollution Research International·Viswanath MuthukrishnanNedumaran Damodaran

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Software Mentioned

Pix4D
Otsu
Open Computer Vision library (
Random Forest
OpenCV
mathop
ExG
Genstat
ExR

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