ArticlePlant methods2022
Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imagery.
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 4 papers.
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Who cites it
4 citing papers in PubMed.
- Wheat growth parameters prediction based on dual output Bayesian neural network using multi-modal information.Frontiers in plant science · 2026Article
- The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset.Plant phenomics (Washington, D.C.) · 2025Article
- Estimating Tea Plant Physiological Parameters Using Unmanned Aerial Vehicle Imagery and Machine Learning Algorithms.Sensors (Basel, Switzerland) · 2025Article
- Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches.Frontiers in plant science · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundLeaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.
methodsTo investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.
resultsThe results show that: (1) the soil background reduced the accuracy of the LAI prediction of wheat, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R
conclusionsThe method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.
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