Utilizing Artificial Intelligence (AI) to Develop Soil Health Index for Salinized Coastal Agricultural Land.

Romic, M.; Mornar, V.; Reljic, M.; Slapnicar, A.; Bagic Babac, M., and Romic, D., 2024. Utilizing artificial intelligence (AI) to develop soil health index for salinized coastal agricultural land. In: Phillips, M.R.; Al-Naemi, S., and Duarte, C.M. (eds.), Coastlines under Global Change: Proceedings from the International Coastal Symposium (ICS) 2024 (Doha, Qatar). Journal of Coastal Research, Special Issue No. 113, pp. 225-229. Charlotte (North Carolina), ISSN 0749-0208. The fertile soils found in delta regions globally are critical for food production, but the increase in sea level and the intrusion of saltwater into aquifers affect the soil health and agricultural sustainability. In the Eastern Adriatic coast in Croatia, particularly in its southern part, the shortage of water, severe droughts, and anomalous weather events in recent decades have become the main constraints on efficient agricultural productivity. The Neretva River delta has been converted to agricultural land through numerous and extensive reclamation projects resulting in the creation of polder-type agricultural land. Generally, the surface-water levels within polder-type land are artificially maintained and controlled by a network of drainage canals, pumping stations, and sluice-gate complexes, which are used to release water into the sea. The potential of AI to assist in developing informed management practices in promoting soil health in agriculture was tested using data of long-term soil and water quality monitoring. A sophisticated sensor system has been developed, providing continuous collection of data on soil and water salinity at hourly intervals, with more frequent updates occurring every ten minutes in select locations. This data is seamlessly transmitted to a centralized database, serving as a robust storage solution for comprehensive data analysis using AI. Through the collection and analysis of data, machine learning models were developed, implemented, and evaluated to identify the most effective model. In addition, ongoing monitoring allows the models to continuously learn and improve over time as new data becomes available. Optimal AI models should help farmers to adjust management practice, particularly irrigation, in accordance with the current soil and water quality condition. [ABSTRACT FROM AUTHOR]