Akademik Veri Yönetim Sistemi
2024 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2024, California, Amerika Birleşik Devletleri, 28 - 31 Temmuz 2024, (Tam Metin Bildiri)
As precision agriculture gains prominence in optimizing resource utilization and crop yields, the role of system identification becomes increasingly vital. This study explores the application of Nonlinear AutoRegressive Moving Average –L2 (NARMA-L2) system modelling in the context of greenhouse dynamics modelling. NARMA-L2, a powerful nonlinear system identification methodology, proves to be a versatile tool for predicting and regulating environmental variables within a greenhouse setting. The study delves into the unique challenges posed by the dynamic and nonlinear nature of greenhouse environments, where factors such as temperature, humidity, and CO2 levels intricately interact. By harnessing the capabilities of NARMA-L2 modelling, the research aims to develop a robust system identification that can adapt to the complex dynamics of a greenhouse, ensuring optimal conditions for plant growth and resource utilization. The study emphasizes the potential of NARMA-L2 in providing precise and adaptive system modelling, leading to enhanced crop productivity, resource efficiency, and sustainability in greenhouse agriculture. Through a series of simulations and empirical validations, the study showcases the effectiveness of NARMA-L2 in responding dynamically to environmental changes. The findings contribute to the growing body of knowledge on advanced system identification strategies for precision agriculture, with specific implications for greenhouse management.