Influence of Wind Speed on RGB-D Images in Tree Plantations

Abstract

Weather conditions can affect sensors’ readings when sampling outdoors. Although sensors are usually set up covering a wide range of conditions, their operational range must be established. In recent years, depth cameras have been shown as a promising tool for plant phenotyping and other related uses. However, the use of these devices is still challenged by prevailing field conditions. Although the influence of lighting conditions on the performance of these cameras has already been established, the effect of wind is still unknown. This study establishes the associated errors when modeling some tree characteristics at different wind speeds. A system using a Kinect v2 sensor and a custom software was tested from null wind speed up to 10 m·s−1. Two tree species with contrasting architecture, poplars and plums, were used as model plants. The results showed different responses depending on tree species and wind speed. Estimations of Leaf Area (LA) and tree volume were generally more consistent at high wind speeds in plum trees. Poplars were particularly affected by wind speeds higher than 5 m·s−1. On the contrary, height measurements were more consistent for poplars than for plum trees. These results show that the use of depth cameras for tree characterization must take into consideration wind conditions in the field. In general, 5 m·s−1 (18 km·h−1) could be established as a conservative limit for good estimations.

Three-Dimensional Modeling of Weed Plants Using Low-Cost Photogrammetry

“…This system has some advantages related to its low cost and the high detail obtained in the models aimed at plant phenotyping. Using the Kinect, Azzari et al [ 31 ] and Andújar et al [ 32 ] properly reconstructed vegetal structures under indoor and outdoor conditions. However, these cameras failed when reconstruction was to a high detail level.…”

Low-Cost Three-Dimensional Modeling of Crop Plants

“…These authors concluded that the low-cost sensors could replace an expensive laser scanner in many of the plant phenotyping scenarios. Andújar, et al [19] reconstructed trees at different wind speeds to estimate the LAI and the tree volume; the resolution potentials of this sensor from null wind speed to high wind speeds were successfully demonstrated as the 3D models were properly reconstructed at high wind speeds. Far from being used only for plant characterization, a Kinect sensor could be used to estimate plant health.…”

Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping

“…The performances related to each combination of quantity and quality of images are not easily generalizable but rather depend on specific parameters. In this context, a low number of images with poor spatial resolution negatively affected the accuracy of the 3D reconstruction, leading to higher errors in smaller objects (i.e., stem diameters and leaf area) [57]. In particular, the lower the ratio between the target size and the pixel, the higher the estimation error of the parameter [58].…”