Abstract
Plant modeling can provide a more detailed overview regarding the basis of plant development throughout the life cycle. Three-dimensional processing algorithms are rapidly expanding in plant phenotyping programmes and in decision-making for agronomic management. Several methods have already been tested, but for practical implementations the trade-off between equipment cost, computational resources needed and the fidelity and accuracy in the reconstruction of the end-details needs to be assessed and quantified. This study examined the suitability of two low-cost systems for plant reconstruction. A low-cost Structure from Motion (SfM) technique was used to create 3D models for plant crop reconstruction. In the second method, an acquisition and reconstruction algorithm using an RGB-Depth Kinect v2 sensor was tested following a similar image acquisition procedure. The information was processed to create a dense point cloud, which allowed the creation of a 3D-polygon mesh representing every scanned plant. The selected crop plants corresponded to three different crops (maize, sugar beet and sunflower) that have structural and biological differences. The parameters measured from the model were validated with ground truth data of plant height, leaf area index and plant dry biomass using regression methods. The results showed strong consistency with good correlations between the calculated values in the models and the ground truth information. Although, the values obtained were always accurately estimated, differences between the methods and among the crops were found. The SfM method showed a slightly better result with regard to the reconstruction the end-details and the accuracy of the height estimation. Although the use of the processing algorithm is relatively fast, the use of RGB-D information is faster during the creation of the 3D models. Thus, both methods demonstrated robust results and provided great potential for use in both for indoor and outdoor scenarios. Consequently, these low-cost systems for 3D modeling are suitable for several situations where there is a need for model generation and also provide a favourable time-cost relationship.
Highlights
Digital models allow information gathering about crop status for agricultural management or breeding programmess
The studied methods have shown that deriving crop height or leaf area is possible though three-dimensional modeling, creating models of high value for plant breeding programmes or precision agriculture
These models tend to underestimate crop height and leaf area with differing results, whereby the RGB-D modeling approach created realistic models; the underestimation was higher than those models created by the Structure from Motion (SfM)
Summary
Digital models allow information gathering about crop status for agricultural management or breeding programmess. Plant models can measure and characterize complex plant shapes, providing essential information to plant breeding programmes that is necessary for modifying traits related to physiology, architecture, stress or agronomical management [3]. Plant modeling makes information, such as plant treatment or growth assessments, more accessible to agricultural managers, providing managers with a detailed and comprehensive understanding of plant development throughout the life cycle. The creation of digital models allows for a better understanding of the internal processes in plant growing, they require technological developments for sensing and capturing purposes. Most of the sensing technologies used are based on two-dimensional characterization, from visible imagery to thermal, multispectral imaging or fluorescence sensors [4]. Fluorescence sensors are typically used for the extraction of parameters related to leaf composition; they expose plants to a specific wavelength of visible or ultraviolet (UV)
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