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.

Vegetation indexes are important indicators of the health and yield of agricultural crops. Among the sensors used to evaluate vegetation indexes, proximal sensors can be used for real-time decision-making. Thus, the objective of this study was to develop a proximal sensor system based on phototransistors to acquire and store the following vegetation indexes: normalized difference vegetation index, simple ratio, wide dynamic range vegetation index, soil-adjusted vegetation index, and optimized soil-adjusted vegetation index. The sensor system was developed using an analog circuit to acquire reflectance data from red and near-infrared bands. The sensor system was calibrated according to the results of a spectroradiometer, using Zoysia japonica grass as the target. An algorithm that calculates and stores vegetation indexes in a file was developed. The Pearson correlation between the vegetation indexes obtained with the sensor system and the spectroradiometer was evaluated. The vegetation indexes presented a Pearson correlation higher than 0.92 to the estimated values by the spectroradiometer. Under the evaluation conditions, the proposed sensor system could be used to determine all vegetation indexes evaluated.