ArticlePlant phenomics (Washington, D.C.)2021
Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature.
Article in Plant phenomics (Washington, D.C.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Review
- High-Throughput Phenotyping: Status and Applications in Rice Breeding.Plants (Basel, Switzerland) · 2026Review
- Multi-scale lodging in rice and wheat: bridging biomechanical traits, phenotypic detection, and intelligent harvesting systems.Frontiers in plant science · 2026Review
- Overcoming Challenges in Plant Biomechanics: Methodological Innovations and Technological Integration.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Fine classification of rice fields in high-resolution remote sensing images.Scientific reports · 2024Article
- DCPlant phenomics (Washington, D.C.) · 2024Article
- Field phenotyping for African crops: overview and perspectives.Frontiers in plant science · 2023Review
- High-throughput field crop phenotyping: current status and challenges.Breeding science · 2022Article
- Estimation of amino acid contents in maize leaves based on hyperspectral imaging.Frontiers in plant science · 2022Article
- Evaluation of important phenotypic parameters of tea plantations using multi-source remote sensing data.Frontiers in plant science · 2022Article
- Multispectral Drone Imagery and SRGAN for Rapid Phenotypic Mapping of Individual Chinese Cabbage Plants.Plant phenomics (Washington, D.C.) · 2022Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-yield rice cultivation is an effective way to address the increasing food demand worldwide. Correct classification of high-yield rice is a key step of breeding. However, manual measurements within breeding programs are time consuming and have high cost and low throughput, which limit the application in large-scale field phenotyping. In this study, we developed an accurate large-scale approach and presented the potential usage of hyperspectral data for rice yield measurement using the XGBoost algorithm to speed up the rice breeding process for many breeders. In total, 13 japonica rice lines in regional trials in northern China were divided into different categories according to the manual measurement of yield. Using an Unmanned Aerial Vehicle (UAV) platform equipped with a hyperspectral camera to capture images over multiple time series, a rice yield classification model based on the XGBoost algorithm was proposed. Four comparison experiments were carried out through the intraline test and the interline test considering lodging characteristics at the midmature stage or not. The result revealed that the degree of lodging in the midmature stage was an important feature affecting the classification accuracy of rice. Thus, we developed a low-cost, high-throughput phenotyping and nondestructive method by combining UAV-based hyperspectral measurements and machine learning for estimation of rice yield to improve rice breeding efficiency.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.