Rodrigo Trevisan

A Method to Obtain Orange Crop Geometry Information Using a Mobile Terrestrial Laser Scanner and 3D Modeling

LiDAR (Light Detection and Ranging) technology has been used to obtain geometrical attributes of tree crops in small field plots, sometimes using manual steps in data processing. The objective of this study was to develop a method for estimating canopy volume and height based on a mobile terrestrial laser scanner suited for large commercial orange groves. A 2D LiDAR sensor and a GNSS (Global Navigation Satellite System) receiver were mounted on a vehicle for data acquisition. A georeferenced point cloud representing the laser beam impacts on the crop was created and later classified into transversal sections along the row or into individual trees. The convex-hull and the alpha-shape reconstruction algorithms were used to reproduce the shape of the tree crowns. Maps of canopy volume and height were generated for a 25 ha orange grove. The different options of data processing resulted in different values of canopy volume. The alpha-shape algorithm was considered a good option to represent individual trees whereas the convex-hull was better when representing transversal sections of the row. Nevertheless, the canopy volume and height maps produced by those two methods were similar. The proposed system is useful for site-specific management in orange groves.

Canopy sensor placement for variable-rate nitrogen application in sugarcane fields

Nitrogen (N) fertilization is challenging for sugarcane, and machine-based canopy sensors appear as an alternative to allow variable-rate N fertilization. Top or sidedressing N is applied in each crop row and crop spatial variability behavior must be understood to allow proper sensor placement and applicator configurations in order to optimize N fertilization. Thus, the goal of this study was to investigate sugarcane crop variability and N prescription error when working with various sensor placements and boom sections. The approaches involved post-processing N prescription maps and real-time application, varying the number of sensors used and calculating the N rate for the applicator boom sections. Sugarcane fields show high crop variability due to their semi-perennial cropping system, which causes unpredictability of sensor readings from adjacent rows, ideally suggesting one sensor for each row in order to obtain more detailed plant-vigor information. Moreover, the machine must be able to apply fertilizer for each individual row to allow the most reliable application of N rate, ensuring optimization of crop response to variable-rate N application.

Orange tree canopy volume estimation by manual and LiDAR-based methods

LiDAR (Light detection and ranging) technology is an alternative to current manual methods of canopy geometry estimations in orange trees. The objective of this work was to compare different types of canopy volume estimations of orange trees, some inspired on manual methods and others based on a LiDAR sensor. A point cloud was generated for 25 individual trees using a laser scanning system. The convex-hull and the alpha-shape surface reconstruction algorithms were tested. LiDAR derived models are able to represent orange trees more accurately than traditional methods. However, results differ significantly from the current manual method. In addition, different 3D modeling algorithms resulted in different canopy volume estimations. Therefore, a new standard method should be developed and established.

Variability of physical properties of soil and rice grown under cover crops in crop-livestock integrated system

The production systems of upland rice culture in Mato Grosso are not consolidated yet while the effects of soil physical properties and their correlation with rice yield in crop-livestock integrated systems are not defined as well. Therefore, this study determined the spatial variability of physical properties of soil and rice cultivated in no-tillage system under different cover crops, using principal components analysis and geostatistics. The experiment was conducted in Santa Carmen, northern Mato Grosso. A regular grid with 100 sample points distributed in an area of 26,400 m2 was installed. Soil and rice samples were collected to determine rice variables and soil physical properties. The average rice yield was 1.70 Mg∙ha−1, ranging from 0.70 to 3.12 Mg∙ha−1. The highest yields were observed in consortium with cowpea and brachiaria and were associated with lower incidence of grain spots, despite higher soil density and penetration resistance. The consortium with brachiaria, crotalaria, and sudangrass had lower yields, which was associated with higher incidence of grain spots, despite higher soil macroporosity and total porosity.

Accuracy assessment of a mobile terrestrial laser scanner for tree crops

LiDAR technology is one option to collect spatial data about canopy geometry in many crops. However, the method of data acquisition includes many errors related to the LiDAR sensor, the GNSS receiver and the data acquisition set up. Therefore, the objective of this study was to evaluate the errors involved in the data acquisition from a mobile terrestrial laser scanner (MTLS). Regular shaped objects were scanned with a developed MTLS in two different tests: i) with the system mounted on a vehicle and ii) with the system mounted on a platform running over a rail. The errors of area estimation varied between 0.001 and 0.071 m² for the circle, square and triangle objects. The errors on volume estimations were between 0.0003 and 0.0017 m³, for cylinders and truncated cone.