Author ORCID Identifier
https://orcid.org/0009-0002-2150-8930
Date Available
12-10-2025
Year of Publication
2025
Document Type
Master's Thesis
Degree Name
Master of Computer and Information Science (MCIS)
College
Engineering
Department/School/Program
Computer Science
Faculty
Simone Silvestri
Abstract
Dynamic integration of Cyber-Physical Systems (CPS) and Artificial Intelligence (AI) has become a vital component for unlocking the potential of smart agriculture. Currently, limitations such as limited computational resources, poor network connectivity, and rigid treatment strategies stifle optimal agricultural outcomes. This creates a challenge of leveraging the capabilities of modern artificial intelligence to combat the natural and artificial constraints of the smart agriculture environment. The primary contribution of this thesis is the development of frameworks to alleviate the overhead data and computational demand for AI within smart agriculture settings. The first framework, iCrop+, utilizes TinyML and LoRa to guarantee high-precision disease detection for resource-constrained and low connectivity environments. The second framework, AgriSmart, leverages optimization techniques with differential evolution and process-based modeling tools to optimize agricultural resource applications. Strategies for irrigation and fertilizer are dynamically updated based on changing weather and soil conditions, allowing precise resource allocation. These frameworks were deployed on real-world testbeds and agricultural modeling software to showcase the potential improvements for smart agriculture. The extensive experimentation performed showcased significant improvements for low-cost high accuracy crop disease detection and high-yielding efficient agricultural resource allocation.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2025.567
Funding Information
This study was supported by the National Science Foundation Smart and Connected Communities-funded project “Smart Integrated Farm Network for Rural Agricultural Communities” (SIRAC), award number 1952045, in 2024-2025. The study was also supported by the National Science Foundation Cyber Physical Systems-funded project "CAREER:Energy Management for Smart Residential Environments through Human-in-the-loop Algorithm Design", award number 1943035, in 2024-2025
Recommended Citation
Butcher, Jackson K., "Exploiting Artificial Intelligence and Optimization for Smart Agriculture" (2025). Theses and Dissertations--Computer Science. 155.
https://uknowledge.uky.edu/cs_etds/155
