Four-Dimensional Plant Phenotyping Model Integrating Low-Density LiDAR Data and Multispectral Images

Manuel García Rincón, Diego Mendez, Julian D. Colorado
<span title="2022-01-13">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;
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
more &raquo; ... rm 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs14020356">doi:10.3390/rs14020356</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wqxjrbrajrhltcrakgxgbqqllq">fatcat:wqxjrbrajrhltcrakgxgbqqllq</a> </span>
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