Mobile terrestrial laser scanner for site-specific management in orange crop

• Ano de defesa: 2016
• Autor(a) principal: Colaço, André Freitas
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Agricultura de precisão; Canopy geometry; Geometria de copa; LiDAR sensor; Precision agriculture; Sensor LiDAR; Tecnologia de taxa variada; Variable-rate technology
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/11/11152/tde-23012017-151317/

Sensors based on LiDAR (Light Detection and Ranging) technology have the potential to provide accurate 3D models of the trees retrieving information such as canopy volume and height. This information can be used for diagnostics and prescriptions of fertilizers and plant protection products on a site-specific basis. This research aimed to investigate the use of LiDAR sensors in orange crops. Orange is one of the most important tree crop in Brazil. So far, research have developed and tested LiDAR based systems for several tree crops. However, usually individual trees or small field plots have been used. Therefore, several aspects related to data acquisition and processing must still be developed for large-scale application. The first study reported in this document (Chapter 3) aimed to develop and test a mobile terrestrial laser scanner (MTLS) and new data processing methods in order to obtain 3D models of large commercial orange groves and spatial information about canopy geometry. A 2D laser sensor and a RTK-GNSS receiver (Real Time Kinematics - Global Navigation Satellite System) were mounted on a vehicle. The data processing was based on generating a georeferenced point cloud, followed by the filtering, classification and surface reconstruction steps. A 25 ha commercial orange grove was used for field validation. The developed data acquisition and processing system was able to produce a reliable point cloud of the grove, providing high resolution canopy volume and height information. The choice of the type of point cloud classification (by individual trees or by transversal sections of the row) and the surface reconstruction algorithm is discussed in this study. The second study (Chapter 4) aimed to characterize the spatial variability of canopy geometry in commercial orange groves. Understanding such variability allows sensor-based variable rate application of inputs (i.e, applying proportional rates of inputs based on the variability of canopy size) to be considered as a suitable strategy to optimize the use of fertilizers and plant protection products. Five commercial orange groves were scanned with the developed MTLS system. According to the variability of canopy volume found in those groves, the input savings as a result of implementing sensor-based variable rate technologies were estimated in about 40%. The second goal of this study was to understand the relationship between canopy geometry and several other relevant attributes of the groves. The canopy volume and height maps of three groves were analyzed against historical yield maps, elevation, soil electrical conductivity, organic matter and clay content maps. The correlations found between canopy geometry and yield or soil maps varied from poor to strong correlations, depending on the grove. When classifying the groves into three classes according to canopy size, the yield performance and soil features inside each class was found to be significantly different, indicating that canopy geometry is a suitable variable to guide management zones delineation in one grove. Overall results from this research show the potential of MTLS systems and subsequent data analysis in orange crops indicating how canopy geometry information can be used in site-specific management practices.