The estimation of crop emergence in potatoes by UAV RGB imagery

Abstract

BackgroundCrop 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. ResultsIn 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.ConclusionsThe 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.

Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning

“…A few studies have used them for potato growth monitoring and trait estimation. For example, UAV-based RGB imagery was used for potato identification [ 12 ] and emergence estimation [ 13 ]. The leaf chlorophyll content of potato was estimated using multispectral data [ 14 ].…”

Image-based Phenotyping and Genetic Analysis of Potato Skin Set and Color

“…The primary motivation for this research was to develop an image-based phenotyping method for tuber appearance that can be used for breeding and genetics research. Image-based phenotyping is ubiquitous now due to the availability of multi-spectral sensors on UAVs (Li et al, 2019), but imaging studies of plant morphology are also becoming more common (Darrigues et al, 2008;Miller et al, 2017;Moore et al, 2013). Plot-based heritability (h 2 ) is a critical measure of the reliability of the phenotyping method, and we were pleased to estimate values over 0.75 for all three color traits and skinning percentage.…”

Automated crop plant counting from very high-resolution aerial imagery

“…Traditionally crop monitoring required manual labour, as the field manager or the land owner still had to monitor the crops physically. This is time consuming and can be prone to human error (Li et al 2019). During the last decade, precision agriculture (PA) has aided to decrease the manual labour on the fields by providing crop monitoring solutions that are less subjective, cost-efficient and robust.…”

“…In Li et al (2019), the authors estimated the number of seeds that germinated and grew via an unsupervised method that analysed UAV imagery and employed a random forest based approach that provides the actual delineation of the contours of single plants. They computed emergence rate, crop canopy cover, and emergence uniformity, which were compared to manual crop assessment.…”