EVALUATION OF FILTERING AND INTERPOLATION OF PRODUCTIVITY DATA IN DEFINING MANAGEMENT ZONES IN PRECISION AGRICULTURE SYSTEMS
DOI:
https://doi.org/10.56238/revgeov16n5-302Keywords:
Precision Agriculture, Data Filtering, Spatial Interpolation, Management Zones, Yield MonitoringAbstract
Precision agriculture has emerged as a key technological approach to improve agricultural management, enabling the identification of spatial and temporal variability in yield-related factors. However, data quality from yield monitors is often compromised by measurement errors and statistical noise, making the filtering process a critical step for generating reliable information. This study aimed to evaluate the influence of data filtering and interpolation methods on the definition of management zones (MZs) in two agricultural areas located in Céu Azul, Brazil, using 11 years of soybean, corn, and wheat yield data. Raw and filtered data were interpolated using Inverse Distance Weighting (IDW) and Moving Average (MA) methods. Management zones were delineated through the Fuzzy C-Means algorithm and assessed using quality indices (FPI, MPE, VR, and ICVI). Results showed that filtered data interpolated by the Moving Average method exhibited greater consistency, reducing the coefficient of variation by 3 to 15%, and improving class distinction and spatial organization. It was concluded that data filtering significantly enhances the accuracy of management zone delineation, supporting more efficient site-specific input application.
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