Digital mapping of soil electrical conductivity for paddy field
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Abstract
The digital soil electrical conductivity (EC) map has been widely applied in agriculture globally due to its ability to explain various soil characteristics. However, the Mekong Delta lacks comprehensive data on soil EC. This study aims to address this gap by using the common interpolation method —K-Nearest Neighbors (KNN), Inverse Distance Weighting (IDW), Kriging interpolation, and Convolutional Neural Networks (CNN)—to map soil EC over an area of approximately 1.4 hectares. Using 228 data samples, the study found that the Gaussian model within Kriging was the most effective for interpolating soil EC, achieving the highest R-squared values (0.79 with test data and 0.96 with full data) and the lowest RMSE values (0.049 with test data and 0.022 with full data). Additionally, GPS data collection using the U-blox ZED-F9P-01B GPS module, paired with the U-blox ANN-MB-00 antenna, yielded better accuracy and reliability under rice field conditions (Q=1) compared to the performance in orchard settings. This research provides valuable insights into soil management and agricultural practices in the Mekong Delta.
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