AI in Agriculture
Конференцијски прилог (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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.
Кључне речи:
soil organic carbon (SOC) / transfer learning / neural networkИзвор:
1st Serbian International Conference on Applied Artificial Intelligence (SICAAI), Kragujevac, Serbia, 2022Финансирање / пројекти:
- CERES - Eo-Based Information for Smarter Agriculture and Carbon Farming (RS-ScienceFundRS-AI-6527073)
Колекције
Институција/група
GraFarTY - 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 .