ArticlePlant methods2018
High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging.
Article in Plant methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Accurate 3D maize ear phenotyping using voxel grids derived from RGB machine vision.Plant phenomics (Washington, D.C.) · 2026Article
- Integrating AI in seed science: Toward an intelligent design paradigm.Plant communications · 2026Review
- OpenEar: an ultra-affordable, high-throughput, and accurate maize ear phenotyping system.Plant methods · 2026Article
- CornPheno: Phenotyping corn ear kernels in the wild via point query transformer.Plant phenomics (Washington, D.C.) · 2025Article
- Genetic analysis of ear, husk, and tassel traits in tropical maize under diverse environments.Frontiers in plant science · 2025Article
- Grain yellowness is an effective predictor of carotenoid content in global sorghum populations.Scientific reports · 2024Article
- Digital techniques and trends for seed phenotyping using optical sensors.Journal of advanced research · 2024Review
- Deciphering individual triticale grain weight patterns: A gaussian mixture model approach.PloS one · 2024Article
- Identifying yield-related genes in maize based on ear trait plasticity.Genome biology · 2023Article
- Genetic resources and breeding of maize forFrontiers in plant science · 2023Review
- Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits.Plant methods · 2022Article
- Incorporating male sterility increases hybrid maize yield in low input African farming systems.Communications biology · 2022Article
- Genomic Regions Associated With Salinity Stress Tolerance in Tropical Maize (Frontiers in plant science · 2022Article
- Integrating Genomic and Phenomic Approaches to Support Plant Genetic Resources Conservation and Use.Plants (Basel, Switzerland) · 2021Review
- DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics.Plant methods · 2021Article
- Beat the stress: breeding for climate resilience in maize for the tropical rainfed environments.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Review
- Scaling up high-throughput phenotyping for abiotic stress selection in the field.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Review
- Heterotic Patterns of Temperate and Tropical Maize by Ear Photometry.Frontiers in plant science · 2021Article
- Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm.Plants (Basel, Switzerland) · 2020Review
- Automatic kernel counting on maize ear using RGB images.Plant methods · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGrain yield, ear and kernel attributes can assist to understand the performance of maize plant under different environmental conditions and can be used in the variety development process to address farmer's preferences. These parameters are however still laborious and expensive to measure.
resultsA low-cost ear digital imaging method was developed that provides estimates of ear and kernel attributes i.e., ear number and size, kernel number and size as well as kernel weight from photos of ears harvested from field trial plots. The image processing method uses a script that runs in a batch mode on ImageJ; an open source software. Kernel weight was estimated using the total kernel number derived from the number of kernels visible on the image and the average kernel size. Data showed a good agreement in terms of accuracy and precision between ground truth measurements and data generated through image processing. Broad-sense heritability of the estimated parameters was in the range or higher than that for measured grain weight. Limitation of the method for kernel weight estimation is discussed.
conclusionThe method developed in this work provides an opportunity to significantly reduce the cost of selection in the breeding process, especially for resource constrained crop improvement programs and can be used to learn more about the genetic bases of grain yield determinants.
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