SYSTEM MANAGEMENT OF FORAGE PRODUCTION BY SIMULATING MILD DROUGHT PRIMING - Razzaqi Hatmawira Rabbani Raharto; Yehezkiel Reynard Wibowo; Purwasih Ajoe Febry; Muchamad Muchlas (Center for Environmental Studies, Universitas Islam Negeri Ar-Raniry Banda Aceh, 2023)
This study presents a case study analysis that examines the use of artificial intelligence and internet of to enhance forage production. The study based on case study of Ngajum’s Dairy Farm in Malang regency, which faceslimitations in forage production during the dry season. Through a qualitative and quantitative investigation, this research aims to provide insights into the multifaceted effects of how the application of Artificial Intelligence and the Internet of Things modulate genetic imprinting in forage production. Mild drought priming involves subjecting plantsto controlled, suboptimal water conditions for a short period. This technique triggers a series of physiological responses in plants, leading to increased stress tolerance and improved overall performance when exposed to subsequent drought stress. AI, with its data analysis capabilities, can process a vast amount of environmental data,including soil moisture levels, weather forecasts, and plant responses. IoT devices, equipped with sensors and actuators, provide the means to collect and transmit essential data from the field. The present results deduced that priming with mild drought using artificial intelligence and internet of things might effectively improve drought tolerance in forage, thus increasing the forage production to support dairy farmers in Indonesia.