Abstract
ABSTRACT The global navigation satellite system (GNSS) is the basis for localized crop management by allowing the georeferencing of collected data and the generation of maps by different systems that compose precision agriculture. There is a demand for low-cost navigation systems to enable their use in agriculture. Therefore, the objective of this study is to integrate a low-cost GNSS module to a single-board computer using Kalman filtering to obtain navigation data. The system was evaluated by performing one static and two kinematic experiments, with three repetitions each. In the static experiment, the mean error was 3.25 m with a root mean square error (RMSE) of 3.73 m. In the first kinematic experiment, data variability was lower at a velocity of 1.39 m s−1. In the second kinematic experiment, the mean error was 1.26 and 1.13 m, and the RMSE was 1.45 and 1.27 m for data obtained before and after filtering, respectively. In conclusion, the system reduces the lateral errors in linear sections but is not indicated for sections that change direction. Moreover, this system can be used in agricultural applications such as soil sampling and crop yield monitoring.
Highlights
Global navigation satellite systems (GNSS) are one of the pillars of precision agriculture (PA) (De Oliveira, 2016)
The objective of this study is to evaluate the accuracy of an integrated system composed of a lowcost GNSS module, a BeagleBoard Black (BBB) single-board computer, and Kalman filtering to obtain navigation data
Epk is the kinematic positioning error; a is the angular coefficient of the line on the x-axis; xi is the east UTM coordinate; b is the angular coefficient on the y-axis; yi is the north UTM coordinate; c is the distance from the origin (0,0); i is the number of points collected
Summary
Global navigation satellite systems (GNSS) are one of the pillars of precision agriculture (PA) (De Oliveira, 2016). GNSS are the basis for localized crop management and are used during the stages of planting, agrochemical application, and harvesting (Chen et al, 2005; Suprem et al, 2013). GNSS allow the georeferencing of the collected data and the generation of maps by the different systems that compose PA. The localized management of agricultural processes distinguishes PA from conventional agriculture (Mondal et al, 2011). The cost of navigation systems needs to be reduced to make robotic systems more feasible, especially in small farms (De Oliveira, 2016). Technologies that facilitate access to GNSS are fundamental to enable the navigation of small agricultural machines. Single-board computers, such as BeagleBoard Black (BBB; model Revision C, Michigan, USA) are low-cost computers that allow the rapid development of new tools for PA (Olesen et al, 2016)
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