ArticlePlant methods2022
Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.).
Article in Plant methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Assessing the ecological quality of Rheum tanguticum based on machine learning models and cultivation verification.NPJ science of food · 2025Article
- A panomics-driven framework for the improvement of major food legume crops: advances, challenges, and future prospects.Horticulture research · 2025Article
- Combining UAV multisensor field phenotyping and genome-wide association studies to reveal the genetic basis of plant height in cotton (Plant phenomics (Washington, D.C.) · 2025Article
- Combining a Standardized Growth Class Assessment, UAV Sensor Data, GIS Processing, and Machine Learning Classification to Derive a Correlation with the Vigour and Canopy Volume of Grapevines.Sensors (Basel, Switzerland) · 2025Article
- TflosYOLO+TFSC: an accurate and robust model for estimating flower count and flowering period.Frontiers in plant science · 2025Article
- High-throughput method for improving rice AGB estimation based on UAV multi-source remote sensing image feature fusion and ensemble learning.Frontiers in plant science · 2025Article
- Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Frontiers in plant science · 2025Article
- Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits.The Plant journal : for cell and molecular biology · 2024Article
- A pulse crop dataset of agronomic traits and multispectral images from multiple environments.Data in brief · 2024Article
- Faba bean and pea harvest index estimations using aerial-based multimodal data and machine learning algorithms.Plant physiology · 2024Article
- Estimation of soybean yield based on high-throughput phenotyping and machine learning.Frontiers in plant science · 2024Article
- Construction and validation of risk prediction models for pulmonary embolism in hospitalized patients based on different machine learning methods.Frontiers in cardiovascular medicine · 2024Article
- Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages.Frontiers in plant science · 2024Article
- UAV-based individual Chinese cabbage weight prediction using multi-temporal data.Scientific reports · 2023Article
- Soybean leaf estimation based on RGB images and machine learning methods.Plant methods · 2023Article
- Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB Images.Plant phenomics (Washington, D.C.) · 2023Article
- Estimation of soybean yield parameters under lodging conditions using RGB information from unmanned aerial vehicles.Frontiers in plant science · 2022Article
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Authors and funding
13 authors.
Funding
Abstract
backgroundFaba bean is an important legume crop in the world. Plant height and yield are important traits for crop improvement. The traditional plant height and yield measurement are labor intensive and time consuming. Therefore, it is essential to estimate these two parameters rapidly and efficiently. The purpose of this study was to provide an alternative way to accurately identify and evaluate faba bean germplasm and breeding materials.
resultsThe results showed that 80% of the maximum plant height extracted from two-dimensional red-green-blue (2D-RGB) images had the best fitting degree with the ground measured values, with the coefficient of determination (R
conclusionThe results of this study indicated that it is feasible to monitor the plant height of faba bean during the whole growth period based on UAV imagery. Furthermore, the machine learning algorithms can estimate the yield of faba bean reasonably with the multiple time points data of plant height.
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