Spatial interpolators for Delineating management zones to mitigate Mucuna pruriens in sugarcane plantations in the Eastern Amazon

Abstract

Unmanned Aerial Vehicles (UAVs) have emerged as essential tools in precision agriculture, employing aerial photogrammetry concepts to aid producers in various decision-making processes. This study evauates different spatial interpolators to define management zones in sugarcane fields, aiming to control potential infestations by Mucuna pruriens. We collected images using the EbeeSQ UAV, equipped with a multispectral sensor, and calculated vegetation indices, including NDVI, SAVI, NDRE, and GNDVI. Analysis revealed that GNDVI yielded the most favorable results, with a mean value of 0.304 and a coefficient of variation of 11.747 %. Using regular and random sampling grids, we applied Ordinary Kriging (OK) and Support Vector Machine (SVM) interpolators to assess spatial variability across 13 survey zones. The results indicated a Degree of Spatial Dependence averaging 57.197 % and a Moran Index of 0.609, confirming moderate spatial dependence. Cross-validation showed that OK with random sampling outperformed other methods, achieving a Root Mean Square Error (RMSE) of 0.064 and a coefficient of determination (r²) averaging 0.347. Furthermore, the relationship between the Fuzzy Performance Index (averaging 0.069) and Normalized Classification Entropy (averaging 0.077) enabled the creation of management zone maps. These maps effectively identify distinct classes within the study areas, enhancing decision-making for producers in managing velvet bean weed during critical developmental phases.