ArticleScientific reports2023
UAV-based individual Chinese cabbage weight prediction using multi-temporal data.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Integrating UAV-Derived Diameter Estimations and Machine Learning for Precision Cabbage Yield Mapping.Sensors (Basel, Switzerland) · 2025Article
- Dense object detection methods in RAW UAV imagery based on YOLOv8.Scientific reports · 2024Article
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Authors and funding
10 authors.
Funding
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Abstract
The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models. However, the application of UAV-based data to individual-level harvest weight prediction is limited by the effectiveness of obtaining individual features. In this study, we propose a method that automatically detects and extracts multitemporal individual plant features derived from UAV-based data to predict harvest weight. We acquired data from an experimental field sown with 1196 Chinese cabbage plants, using two cameras (RGB and multi-spectral) mounted on UAVs. First, we used three RGB orthomosaic images and an object detection algorithm to detect more than 95% of the individual plants. Next, we used feature selection methods and five different multi-temporal resolutions to predict individual plant weights, achieving a coefficient of determination (R
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Registered trials
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