Year: 2024
Type: conference-paper
Abstract: Plant diseases are a major biosecurity threat to food production and the bio-energy industry. Early detection and control of plant diseases can improve producers’ profitability and reduce environmental impacts from chemical inputs. We proposed to develop a cyber-physical system with three major components: an AI-driven imaging system for early stress detection, an autonomous robotic system to collect plant samples, and a sequencing pipeline to detect molecular signatures of pathogens for disease confirmation. This system is envisioned to control a detected disease by removing or pruning infected plants. This manuscript describes the major milestones achieved by this CPS project and provides a future perspective on disease control automation in agriculture.
Authors: Abhisesh Silwal, Xuemei M. Zhang, Thomas Hadlock, Jacob Neice, Shadab Haque, Adwait Kaundanya, Chang Lu, Boris A. Vinatzer, George Kantor, Song Li
Institutions: Carnegie Mellon University, Virginia Tech
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: diamond
Funder: U.S. Department of Agriculture
Award: G6455654904
OpenAlex ID: W4404187830