Real-time approaches for characterization of fully and partially scanned canopies in groves

Efficient information management in orchard characterization leads to more efficient agricultural processes. In this brief, a set of computational geometry methods are presented and evaluated for orchard characterization; in particular, for the estimation of canopy volume and shape in groves and orchards using a LiDAR (Light Detection And Ranging) sensor mounted on an agricultural service unit. The proposed approaches were evaluated and validated in the field, showing they are convergent in the estimation process and that they are able to estimate the crown volume for fully scanned canopies in real time; for partially observed tree crowns, accuracy decreases up to 30% (the worst case). The latter is the major contribution of this brief since it implies that the automated service unit does not need to cover all alley-ways for an accurate modeling of the orchard, thus saving valuable resources.The authors would like to thank to CONICYT (Chile): FONDECYT Grant 1140575 and Basal Grant FB0008. Also, this research was partially funded by the Spanish Ministry of Science and Innovation and by the European Union through the FEDER funds (projects Optidosa-AGL2007-66093-C04-03 and Safespray-AGL2010-22304-C04-03)

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.We thank Citrosuco and Jacto companies for supporting this project, the São Paulo Research Foundation (FAPESP) for providing a scholarship to the first author (grant: 2013/18853-0) and the Coordination for the Improvement of Higher Education Personnel (CAPES), for funding the first author as an exchange visitor at the University of Lleida (grant: bex_3751/15-5

Georeferenced LiDAR 3D Vine Plantation Map Generation

The use of electronic devices for canopy characterization has recently been widely discussed. Among such devices, LiDAR sensors appear to be the most accurate and precise. Information obtained with LiDAR sensors during reading while driving a tractor along a crop row can be managed and transformed into canopy density maps by evaluating the frequency of LiDAR returns. This paper describes a proposed methodology to obtain a georeferenced canopy map by combining the information obtained with LiDAR with that generated using a GPS receiver installed on top of a tractor. Data regarding the velocity of LiDAR measurements and UTM coordinates of each measured point on the canopy were obtained by applying the proposed transformation process. The process allows overlap of the canopy density map generated with the image of the intended measured area using Google Earth®, providing accurate information about the canopy distribution and/or location of damage along the rows. This methodology was applied and tested on different vine varieties and crop stages in two important vine production areas in Spain. The results indicate that the georeferenced information obtained with LiDAR sensors appears to be an interesting tool with the potential to improve crop management processes

Analyzing and overcoming the effects of GNSS error on LiDAR based orchard parameters estimation

Currently, 3D point clouds are obtained via LiDAR (Light Detection and Ranging) sensors to compute vegetation parameters to enhance agricultural operations. However, such a point cloud is intrinsically dependent on the GNSS (global navigation satellite system) antenna used to have absolute positioning of the sensor within the grove. Therefore, the error associated with the GNSS receiver is propagated to the LiDAR readings and, thus, to the crown or orchard parameters. In this work, we first describe the error propagation of GNSS over the laser scan measurements. Second, we present our proposal to overcome this effect based only on the LiDAR readings. Such a proposal uses a scan matching approach to reduce the error associated with the GNSS receiver. To accomplish such purpose, we fuse the information from the scan matching estimations with the GNSS measurements. In the experiments, we statistically analyze the dependence of the grove parameters extracted from the 3D point cloud -specifically crown surface area, crown volume, and crown porosity- to the localization error. We carried out 150 trials with positioning errors ranging from 0.01 meters (ground truth) to 2 meters. When using only GNSS as a localization system, the results showed that errors associated with the estimation of vegetation parameters increased more than 100 when positioning error was equal or bigger than 1 meter. On the other hand, when our proposal was used as a localization system, the results showed that for the same case of 1 meter, the estimation of orchard parameters improved in 20 overall. However, in lower positioning errors of the GNSS, the estimation of orchard parameters were improved up to 50% overall. These results suggest that our work could lead to better decisions in agricultural operations, which are based on foliar parameter measurements, without the use of external hardware.This work was partly funded by CONICYT FB0008, CONICYT FONDECYT 1171431, PIIC 030/2018 DGIIP-UTFSM Chile, the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya (grant 2017 SGR 646), the Spanish Ministry of Science, Innovation and Universities (project RTI2018- 094222-B-I00). The Spanish Ministry of Education is thanked for Mr. J. Gené’s pre-doctoral fellowship (FPU15/03355)

3D characterization of a Boston Ivy double-skin green building facade using a LiDAR system

On the way to more sustainable and resilient urban environments, the incorporation of urban green infrastructure (UGI) systems, such as green roofs and vertical greening systems, must be encouraged. Unfortunately, given their variable nature, these nature-based systems are difficult to geometrically characterize, and therefore there is a lack of 3D objects that adequately reflect their geometry and analytical properties to be used in design processes based on Building Information Modelling (BIM) technologies. This fact can be a disadvantage, during the building's design phase, of UGIs over traditional grey solutions. Areas of knowledge such as precision agriculture, have developed technologies and methodologies that characterize the geometry of vegetation using point cloud capture. The main aim of this research was to create the 3D characterization of an experimental double-skin green facade, using LiDAR technologies. From the results it could be confirmed that the methodology used was precise and robust, enabling the 3D reconstruction of the green facade's outer envelope. Detailed results were that foliage volume differences in height were linked to plant growth, whereas differences in the horizontal distribution of greenery were related to the influence of the local microclimate and specific plant diseases on the south orientation. From this research, along with complementary previous research, it could be concluded that, generally speaking, with vegetation volumes of 0.2 m3/m2, using Boston Ivy (Parthenocissus Tricuspidata) under Mediterranean climate, reductions in external building surface temperatures of around 13 °C can be obtained and used as analytic parameter in a future 3D-BIM-object.The authors at GREiA research group would like to thank the Catalan Government for the quality accreditation given to their research group (2017 SGR 1537). GREiA is a certified agent TECNIO in the category of technology developers from the Government of Catalonia. The authors at GRAP research group would like to thank the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya (grant 2017 SGR 646), the Spanish Ministry of Economy and Competitiveness (project AGL2013-464 48297-C2-2-R) and the Spanish Ministry of Science, Innovation and Universities (project RTI2018-094222-B-I00). This work is partially supported by ICREA under the ICREA Academia programme. The authors also wish to thank Dr. Jaume Arnó for his contribution in the statistical analysis of the results

Review of Variable-Rate Sprayer Applications Based on Real- Time Sensor Technologies

Precision variable rate spray is one of the research hotspots in the field of modern agriculture spraying applications. Variable rate spraying of the canopy allows growers to apply adjusted volume rate of pesticides to the target, based on canopy size, and to apply plant protection products in an economical and environmentally sound manner. In the field of pesticide application, knowledge of the geometrical characteristics of plantations will guarantee a better adjustment of the dosage of the agrochemicals applied. This technology is integrated with intelligent real-time sensors, which have a high potential for agricultural precision spray applications. This book chapter presents the foundations and applications in agriculture of the primary systems used for real-time spray target detection of the geometrical characterization of tree plantations. Systems based on infrared, ultrasonic, light detection and ranging (LIDAR), and stereo vision sensors were discussed, respectively, on their performances to detect spray targets. Among them, laser scanners and stereo vision systems are probably the most promising and complementary techniques for achieving three-dimensional (3D) pictures and maps of plants and canopies. The advantages of data fusion applied in real-time target detection and its accuracy in density estimation of the plants were stressed