Date of Award
8-2024
Degree Type
Report
Degree Name
Master of Science (MS)
Department
Computer Science
Committee Chair(s)
Curtis Dyreson (Chair)
Committee
Curtis Dyreson
Committee
Steve Petruzza
Committee
Chad Mano
Abstract
Sustainable farm management practice is a multifaceted challenge. Uncovering the optimal state for production while reduction of environmental negative impacts and guaranteed inter-generational assets supervision needs balanced management. Also, considering lots of different factors (cost, profit, employment etc), the agricultural based management technique requires rigorous concentration. In this project machine learning models are applied to develop, achieve and improve the farm management techniques. This experiment ensures the resultant impacts being environment friendly and necessary resource availability and efficiency. Predicting the type of crop and rotational recommendations will disclose potentiality of productive agricultural based farming. Additionally, this project is designed to find the optimized farm operations that will show a stable state combining the agricultural efficiency, better resource management and lowering ecologically unfriendly properties. Additionally, generative AI is used to create data for farming management practices.
Recommended Citation
Samrose, Samira, "Leveraging Generative AI For Sustainable Farm Management Techniques Correspond To Optimization and Agricultural Efficiency Prediction" (2024). All Graduate Reports and Creative Projects, Fall 2023 to Present. 57.
https://digitalcommons.usu.edu/gradreports2023/57
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