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- W4205267225 abstract "High-throughput platforms for plant phenotyping usually demand expensive high-density LiDAR devices with computational intense methods for characterizing several morphological variables. In fact, most platforms require offline processing to achieve a comprehensive plant architecture model. In this paper, we propose a low-cost plant phenotyping system based on the sensory fusion of low-density LiDAR data with multispectral imagery. Our contribution is twofold: (i) an integrated phenotyping platform with embedded processing methods capable of providing real-time morphological data, and (ii) a multi-sensor fusion algorithm that precisely match the 3D LiDAR point-cloud data with the corresponding multispectral information, aiming for the consolidation of four-dimensional plant models. We conducted extensive experimental tests over two plants with different morphological structures, demonstrating the potential of the proposed solution for enabling real-time plant architecture modeling in the field, based on low-density LiDARs." @default.
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- W4205267225 date "2022-01-13" @default.
- W4205267225 modified "2023-09-26" @default.
- W4205267225 title "Four-Dimensional Plant Phenotyping Model Integrating Low-Density LiDAR Data and Multispectral Images" @default.
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- W4205267225 doi "https://doi.org/10.3390/rs14020356" @default.
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