ArticleFrontiers in plant science2021
Crop Performance Evaluation of Chickpea and Dry Pea Breeding Lines Across Seasons and Locations Using Phenomics Data.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- A panomics-driven framework for the improvement of major food legume crops: advances, challenges, and future prospects.Horticulture research · 2025Article
- UAV-Based LiDAR and Multispectral Imaging for Estimating Dry Bean Plant Height, Lodging and Seed Yield.Sensors (Basel, Switzerland) · 2025Article
- A pulse crop dataset of agronomic traits and multispectral images from multiple environments.Data in brief · 2024Article
- Genome-wide association study as a powerful tool for dissecting competitive traits in legumes.Frontiers in plant science · 2023Review
- Remote sensing for field pea yield estimation: A study of multi-scale data fusion approaches in phenomics.Frontiers in plant science · 2023Article
- Combining NDVI and Bacterial Blight Score to Predict Grain Yield in Field Pea.Frontiers in plant science · 2022Article
- Omics for the Improvement of Abiotic, Biotic, and Agronomic Traits in Major Cereal Crops: Applications, Challenges, and Prospects.Plants (Basel, Switzerland) · 2021Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Pacific Northwest is an important pulse production region in the United States. Currently, pulse crop (chickpea, lentil, and dry pea) breeders rely on traditional phenotyping approaches to collect performance and agronomic data to support decision making. Traditional phenotyping poses constraints on data availability (e.g., number of locations and frequency of data acquisition) and throughput. In this study, phenomics technologies were applied to evaluate the performance and agronomic traits in two pulse (chickpea and dry pea) breeding programs using data acquired over multiple seasons and locations. An unmanned aerial vehicle-based multispectral imaging system was employed to acquire image data of chickpea and dry pea advanced yield trials from three locations during 2017-2019. The images were analyzed semi-automatically with custom image processing algorithm and features were extracted, such as canopy area and summary statistics associated with vegetation indices. The study demonstrated significant correlations (
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