ArticlePlant methods2019
The estimation of crop emergence in potatoes by UAV RGB imagery.
Article in Plant methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed.
- Integrating image-based phenotyping and QTL mapping to enhance genetic resistance and accelerate breeding for bacterial grain rot resistance in rice.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Automated weed segmentation with knowledge based labeling for machine learning applications.Scientific reports · 2026Article
- TTADDA-UAV: A multi-season RGB and multispectral UAV dataset of potato fields collected in Japan and the Netherlands.Data in brief · 2025Article
- Automated Rice Seedling Segmentation and Unsupervised Health Assessment Using Segment Anything Model with Multi-Modal Feature Analysis.Sensors (Basel, Switzerland) · 2025Article
- A statistical method for high-throughput emergence rate calculation for soybean breeding plots based on field phenotypic characteristics.Plant methods · 2025Article
- Field-scale detection of Bacterial Leaf Blight in rice based on UAV multispectral imaging and deep learning frameworks.PloS one · 2025Article
- Article
- Phenology analysis for trait prediction using UAVs in a MAGIC rice population with different transplanting protocols.Frontiers in artificial intelligence · 2024Article
- Multispectral-derived genotypic similarities from budget cameras allow grain yield prediction and genomic selection augmentation in single and multi-environment scenarios in spring wheat.Molecular breeding : new strategies in plant improvement · 2024Article
- Improving the accuracy of cotton seedling emergence rate estimation by fusing UAV-based multispectral vegetation indices.Frontiers in plant science · 2024Article
- MS-YOLOv8: multi-scale adaptive recognition and counting model for peanut seedlings under salt-alkali stress from remote sensing.Frontiers in plant science · 2024Article
- Image-Based High-Throughput Phenotyping in Horticultural Crops.Plants (Basel, Switzerland) · 2023Review
- Identifying and mapping individual medicinal plant Lamiophlomis rotata at high elevations by using unmanned aerial vehicles and deep learning.Plant methods · 2023Article
- Mixed-Species Cover Crop Biomass Estimation Using Planet Imagery.Sensors (Basel, Switzerland) · 2023Article
- Effect of varying UAV height on the precise estimation of potato crop growth.Frontiers in plant science · 2023Article
- Estimating yield-contributing physiological parameters of cotton using UAV-based imagery.Frontiers in plant science · 2023Article
- Study of High-Temperature-Induced Morphological and Physiological Changes in Potato Using Nondestructive Plant Phenotyping.Plants (Basel, Switzerland) · 2022Article
- Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives.Plant communications · 2022Review
- High-throughput field crop phenotyping: current status and challenges.Breeding science · 2022Article
- A review of remote sensing for potato traits characterization in precision agriculture.Frontiers in plant science · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCrop emergence and canopy cover are important physiological traits for potato (
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 (
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.
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