A statistical approach to static and dynamic tests for Global Navigation Satellite Systems receivers used in agricultural operations

Abstract

The adaptation of the Global Navigation Satellite Systems (GNSS) technology to fit the needs of farmers requires knowledge of the accuracy level delivered by a GNSS receiver in working conditions. To date, no methodology indicates the minimum number of replications to perform a statistical comparison. This study aims to advance knowledge on the methodological approach for evaluating the static and dynamic performance of GNSS receivers commonly used in agricultural operations. For the static test, a supporting frame in the ground carried all the receivers with coordinates properly transported. In the dynamic test, a circular rail with a 9.55 m radius was installed at ground level with a platform driven by an electric motor to carry the receivers at a constant speed. The transversal error of the receiver to the circular reference line was measured. The error with 95 % probability (E95) to receivers without differential correction ranged between 4.22 m and 0.85 m in the static test, and 2.25 m and 0.98 m in the dynamic test. Receivers with differential correction had E 95 values below 0.10 m in the static test and 0.16 m in the dynamic test. Receivers with C/A code require five replications at minimum and 13 replications are needed for L1/L2 with differential correction signals in the dynamic test. The static test needs nine replications for C/A and five for L1/L2 with differential correction signals.

Academic writing, and how to write in a clear and comprehensible way

“…Minor revision for a nice effect. Source: Maldaner et al (2021) Original This study developed a methodology that allowed the behavior analysis of GNSS receivers of different accuracy levels under static and dynamic conditions.…”

“…To adapt the Global Navigation Satellite Systems (GNSS) technology to farmers' needs, we should learn how accurately a GNSS receiver performs under working conditions. Maldaner et al (2021) article is actually quite nicely written, but I did find some sentences to improve anyway. To be honest, we are likely to find sentences to revise in every single article.…”

Use of Active Sensors in Coffee Cultivation for Monitoring Crop Yield

“…The sensor provides a vegetation index (designated in this work as NS), for which the company does not disclose specifications. All data were georeferenced using a Global Navigation Satellite System (GNSS) receiver (SMART6-L™, NovAtel Inc., Calgary, AB, Canada) with TerraStar-C (NovAtel Inc., Calgary, AB, Canada) correction that allows an accuracy of ±0.09 m [31].…”

High-resolution data for mapping the spatio-temporal variability of sugarcane fields

“…The method involves a learning process from training data to perform a task, relating each input attribute to its associated target focusing to limit the error of prediction or forecasting (ELAVARASAN et al, 2018). ANN (Artificial Neural Network) and RF (Random Forest) are the most common ML methods for developing crop yield prediction models, whereas RF is the most suitable method for sugarcane yield prediction (MALDANER et al, 2021;ABDEL-RAHMAN et al, 2012). RF method is a combination of decision trees that each tree depends on the values of a random vector sampled independently from the input with the same distribution for all trees in the forest.…”

“…All GCP were georeferenced using a GNSS receiver (GPS L1/L2 + Glonass) with differential correction (Novatel Inc., Canada) on ground level (Figure 5B). The accuracy of the GNSS receiver is described by Maldaner et al (2021). All RGB images were captured with a nadir view perspective.…”