Operations Analytics and Optimization for Unstructured Systems: Cyber Collaborative Algorithms and Protocols for Agricultural Systems

Food security is a major concern of human civilization. A way to ensure food security is to grow plants in a greenhouse under controlled conditions. Even under careful greenhouse production, stress in plants can emerge, and can cause damaging disease. To prevent yield loss farmers, apply resources, e.g., water, fertilizers, pesticides, higher/lower humidity, lighting, and temperature, uniformly in the infected areas. Research, however, shows that the practice leads to non-optimal profit and environmental protection.

Precision agriculture (PA) is an approach to address such challenges. It aims to apply the right amount or recourses at the right time and place. PA has been able to maximize crop yield while minimizing operation cost and environmental damage. The problem is how to obtain timely, precise information at each location to optimally treat the plants. There is scant research addressing strategies, algorithms, and protocols for analytics in PA. A monitoring and treating systems are the foci of this dissertation.

The designed systems comprise of agent- and system-level protocols and algorithms. There are four parts: (1) Collaborative Control Protocol for Cyber-Physical System (CCP-CPS); (2) Collaborative Control Protocol for Early Detection of Stress in Plants (CCP-ED); (3) Optimal Inspection Profit for Precision Agriculture; and (4) Multi-Agent System Optimization in Greenhouse for Treating Plants. CCP-CPS, a backbone of the system, establishes communication line among agents. CCP-ED optimizes the local workflow and interactions of agents. Next, the Adaptive Search algorithm, a key algorithm in CCP-ED, has analyzed to obtain the optimal procedure. Lastly, when stressed plants are detected, specific agents are dispatched to treat plants in a particular location with specific treatment.

Experimental results show that collaboration among agents statistically and significantly improves performance in terms of cost, efficiency, and robustness. CCP-CPS stabilizes system operations and significantly improves both robustness and responsiveness. CCP-ED enabling collaboration among local agents, significantly improves the number of infected plants found, and system efficiency. Also, the optimal Adaptive Search algorithm, which considers system errors and plant characteristics, significantly reduces the operation cost while improving performance. Finally, with collaboration among agents, the system can effectively perform a complex task that requires multiple agents, such as treating stressed plants with a significantly lower operation cost compared to the current practice.