Soil organic carbon represents the main nutrient source for crop yields, which is of great importance to agricultural production. This research investigates the usage of a transfer learning-based neural network model to predict SOC values from geochemical soil parameters. The results on datasets representing five European countries showed that the model was able to capture the valuable information contained in grassland soil samples when predicting the SOC values in cropland areas.
Keywords:
soil organic carbon (SOC) / transfer learning / neural network
Source:
1st Serbian International Conference on Applied Artificial Intelligence (SICAAI), Kragujevac, Serbia, 2022
TY - CONF
AU - Kovačević, Miloš
AU - Bursać, Petar
AU - Bajat, Branislav
AU - Kilibarda, Milan
PY - 2022
UR - https://grafar.grf.bg.ac.rs/handle/123456789/2804
AB - Soil organic carbon represents the main nutrient source for crop yields, which is of great importance to agricultural production. This research investigates the usage of a transfer learning-based neural network model to predict SOC values from geochemical soil parameters. The results on datasets representing five European countries showed that the model was able to capture the valuable information contained in grassland soil samples when predicting the SOC values in cropland areas.
C3 - 1st Serbian International Conference on Applied Artificial Intelligence (SICAAI), Kragujevac, Serbia
T1 - AI in Agriculture
UR - https://hdl.handle.net/21.15107/rcub_grafar_2804
ER -
@conference{
author = "Kovačević, Miloš and Bursać, Petar and Bajat, Branislav and Kilibarda, Milan",
year = "2022",
abstract = "Soil organic carbon represents the main nutrient source for crop yields, which is of great importance to agricultural production. This research investigates the usage of a transfer learning-based neural network model to predict SOC values from geochemical soil parameters. The results on datasets representing five European countries showed that the model was able to capture the valuable information contained in grassland soil samples when predicting the SOC values in cropland areas.",
journal = "1st Serbian International Conference on Applied Artificial Intelligence (SICAAI), Kragujevac, Serbia",
title = "AI in Agriculture",
url = "https://hdl.handle.net/21.15107/rcub_grafar_2804"
}
Kovačević, M., Bursać, P., Bajat, B.,& Kilibarda, M.. (2022). AI in Agriculture. in 1st Serbian International Conference on Applied Artificial Intelligence (SICAAI), Kragujevac, Serbia.
https://hdl.handle.net/21.15107/rcub_grafar_2804
Kovačević M, Bursać P, Bajat B, Kilibarda M. AI in Agriculture. in 1st Serbian International Conference on Applied Artificial Intelligence (SICAAI), Kragujevac, Serbia. 2022;.
https://hdl.handle.net/21.15107/rcub_grafar_2804 .
Kovačević, Miloš, Bursać, Petar, Bajat, Branislav, Kilibarda, Milan, "AI in Agriculture" in 1st Serbian International Conference on Applied Artificial Intelligence (SICAAI), Kragujevac, Serbia (2022),
https://hdl.handle.net/21.15107/rcub_grafar_2804 .
