Abstract
This paper presents the use of a terrestrial light detection and ranging (LiDAR) system to scan the vegetation of tree crops to estimate the so-called pixelated leaf wall area (PLWA). Scanning rows laterally and considering only the half-canopy vegetation to the line of the trunks, PLWA refers to the vertical projected area without gaps detected by LiDAR. As defined, PLWA may be different depending on the side from which the LiDAR is applied. The system is completed by a real-time kinematic global positioning system (RTK-GPS) sensor and an inertial measurement unit (IMU) sensor for positioning. At the end, a total leaf wall area (LWA) is computed and assigned to the X, Y position of each vertical scan. The final value of the area depends on the distance between two consecutive scans (or horizontal resolution), as well as the number of intercepted points within each scan, since PLWA is only computed when the laser beam detects vegetation. To verify system performance, tests were conducted related to the georeferencing task and synchronization problems between GPS time and central processing unit (CPU) time. Despite this, the overall accuracy of the system is generally acceptable. The Leaf Area Index (LAI) can then be estimated using PLWA as an explanatory variable in appropriate linear regression models.
Highlights
Precision agriculture (PA) can be defined as the practice of managing agronomic systems that takes into consideration the field variability
PA relies on technologies, such as proximal and remote sensors, global navigation satellite systems (GNSS), decision support systems and variable-rate agricultural machinery
In [2], a mixed system, including a light detection and ranging (LiDAR) sensor and hyperspectral image sensor mounted on a ground-based vehicle, was used to distinguish between conifers and deciduous varieties in a population of 168 trees
Summary
Precision agriculture (PA) can be defined as the practice of managing agronomic systems that takes into consideration the field variability. PA relies on technologies, such as proximal and remote sensors, global navigation satellite systems (GNSS), decision support systems and variable-rate agricultural machinery. Light detection and ranging (LiDAR) has been used in agriculture and forestry in three main areas. It has been used to differentiate between areas with and without vegetation, as in the case of [1]. LiDAR has been used in machinery guidance systems [3]. LiDAR sensors have been used to inventory vegetation [4,5,6,7] and biomass [8]
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