Lingling An

University of Arizona, Xidian University

Papers

Total Citations

129

H-Index

About

Lingling An is a researcher whose work bridges agricultural technology and artificial intelligence, with a particular focus on machine vision and reinforcement learning. Her most influential contribution, "Lettuce calcium deficiency detection with machine vision computed plant features in controlled environments" (2010), has garnered 109 citations, establishing her as a pioneer in applying computational methods to precision agriculture. This work demonstrated how machine vision can non-invasively detect nutrient deficiencies in crops, offering a scalable solution for controlled-environment agriculture. More recently, An has advanced the field of hierarchical reinforcement learning (HRL), publishing two 2024 papers that address the challenge of sample inefficiency in complex decision-making tasks. Her papers on "Hierarchical reinforcement learning from imperfect demonstrations through reachable coverage-based subgoal filtering" and "Hierarchical Reinforcement Learning from Demonstration via Reachability-Based Reward Shaping" each have 10 citations, proposing novel methods to leverage imperfect human demonstrations for more efficient HRL training. By integrating reachability analysis with reward shaping, An's work reduces the computational burden of training autonomous systems in long-horizon tasks. Her research trajectory—from agricultural sensing to AI-driven decision-making—showcases a versatile ability to apply machine learning across domains, making her work relevant to both plant scientists and AI researchers seeking practical, data-efficient solutions.