Low-cost differential positioning for implementation of precision agriculture in the less developed areas

Precision agriculture is rapidly growing worldwide, which is mainly based on positioning. While with the evolution of technology the accuracy of GPS data is continuously improved, the use of these sensors in the less developed countries is still challenging. The direct price link with the accuracy of the GPSs has led to attempts to use low-cost GPSs. The use of low-cost GPSs comes with various sources of error. In this study, a new method has been proposed to overcome the challenge of improving the accuracy of positioning while reducing equipment prices. The proposed method is based on the nature of the GPS error (random noise and bias) and is divided into two parts. First, the random noise was eliminated by employing the simple classic filter. In this regard, a comparison was made between the Kalman filter, Low-pass filter and the Moving Average filter. The settings of the filters were made in such a way that in addition to reducing the power of random error, it reaches the desired error range in the shortest possible time. The results showed that the Kalman filter was more efficient than the other filters. Bias removal is not possible except by using a DGPS receiver that can receive corrections sent from base stations. To solve this problem, a base station was designed that could send the corrections needed by low-cost receivers. The proposed low-cost differential positioning method leads to a 89% reduction in the horizontal error index, which is the accuracy obtained is suitable for various operations in precision agriculture. Second, the bias was removed by inspiring from DGPS technique, via providing a base station, incorporating three low-cost GPSs. in noise reduction and the proposed low-cost differential positioning at best resulted in 89% reduction in the error index. Such an enhanced accuracy can be used for many applications in precision agriculture.