The estimation of crop emergence in potatoes by UAV RGB imagery

• Published in: Plant methods 2019-02, Vol.15 (1), p.15-15, Article 15
• Main Authors: Li, Bo, Xu, Xiangming, Han, Jiwan, Zhang, Li, Bian, Chunsong, Jin, Liping, Liu, Jiangang
• Format: Article
• Language: English
• Subjects: Agricultural production; Analysis; Automation; Compound fertilizers; Corn; Correlation coefficient; Correlation coefficients; Crop development; Crop diseases; Crop emergence; Crops; Cultivars; Developmental stages; Emergence; Experiments; Fertilizers; Field tests; Future predictions; Image analysis; Image processing; Image resolution; Management; Mathematical analysis; Methods; Morphology; Nutrients; Object recognition; Phenotyping; Physiological aspects; Plant extracts; Plant physiology; Potassium; Potato; Potatoes; Random Forest; Random variables; Remote sensing; Software; Studies; Unmanned aerial vehicle (UAV); Unmanned aerial vehicles; Vegetables; Vegetation; Wheat
• ISSN: 1746-4811; 1746-4811
• DOI: 10.1186/s13007-019-0399-7

Crop emergence and canopy cover are important physiological traits for potato ( 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 ( ) of 0.96 and provide an efficient tool to evaluate emergence uniformity. The proposed UAV image analysis method is a promising tool for use as a high throughput phenotyping method for assessing potato crop development at emergence stage. It can also facilitate future studies on optimizing fertiliser management and improving emergence consistency.