Ai-driven drone optimization for precision agriculture and land management
Cal Poly researchers apply AI methods—planning, scheduling, machine learning, and image processing—to improve drone use in precision agriculture. The work optimizes flight paths, scheduling, and resource utilization for applications including cattle tracking, equipment monitoring, and post-wildfire land mapping.
Overview
Drones are a key enabler of precision agriculture, but they are often underutilized due to high acquisition costs, limited operator experience, and suboptimal flight strategies. This project applies artificial intelligence methods—including planning, scheduling, machine learning, and image processing—to improve how drone fleets are deployed in agricultural settings. The goal is to maximize the value of drone-collected data for decision-making related to crop yield estimation, resource utilization, and pest control, while reducing operational costs and improving data quality.
Technical specifications
Key features:
- AI-based planning and scheduling to optimize drone fleet deployment
- Machine learning and image processing for improved data collection and analysis
- Optimization of flight paths, routing strategies, and resource allocation under constraints such as flight time, load weight, and personnel overhead
- Experimental validation across multiple use cases, including cattle tracking on pastures, equipment monitoring in oil fields, and mapping of wildfire-affected areas
- Interdisciplinary collaboration combining Computer Science, Operations Research, and Agriculture
- Student-driven experimentation using a fleet of drones available through Cal Poly's PolyGAIT facility
Technology readiness level
The project builds on prior experiments demonstrating that flight path and routing strategies significantly affect drone task success. Simple patterns such as lawnmower routes or narrowing spirals can cause task failure, while routes that minimize flight distance while anticipating target object movement succeed. A student team is currently expanding these experiments to incorporate additional constraints and a broader range of AI and machine learning methods. Future validation will include mapping Cal Poly's Swanton Pacific Ranch areas affected by the 2020 wildfires, covering rangeland, forest, an apple orchard, vegetable fields, and protected wetlands, with significant involvement of graduate and undergraduate students.
About California Polytechnic State University, San Luis Obispo
California Polytechnic State University (Cal Poly) is a comprehensive public polytechnic in San Luis Obispo known for applied learning and industry-relevant research. Facilities include extensive maker and prototyping spaces, pilot-scale labs for product and process development, and an on-campus research park that co-locates companies with faculty and students. Partnerships grow through sponsored senior design, internships, and capstone collaborations, reinforced by Learn by Doing and a Central Coast location between the Bay Area and Southern California. Research is supported by competitive federal and state funding, including the National Science Foundation and the U.S. Department of Agriculture, plus corporate contracts. A dedicated technology transfer office and entrepreneurship programs guide IP, licensing, and incubation.
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