Guoliang Yang

Jiangxi University of Science and Technology, University of Science and Technology Beijing

Papers

Total Citations

326

H-Index

About

Guoliang Yang is a researcher whose work bridges agricultural automation and affective computing, demonstrating a versatile approach to solving real-world problems through computational methods. His most impactful contribution lies in precision agriculture, specifically through his 2023 paper on a lightweight YOLOv8 tomato detection algorithm, which has garnered 311 citations. This work integrates feature enhancement and attention mechanisms to enable accurate, real-time tomato detection, directly addressing the low automation level in harvesting and classification—a critical bottleneck in agricultural productivity. By optimizing deep learning models for efficiency, Yang provides practical technical support for autonomous farming systems. Earlier in his career, he explored human-computer interaction through an affective computing model based on emotional psychology (2006, 15 citations), reflecting an interest in how machines can interpret and respond to human emotions. While his earlier work laid conceptual groundwork, his recent high-impact publication underscores a shift toward applied AI in agriculture, where his contributions are already shaping the development of intelligent harvesting robots. Yang’s research exemplifies how targeted algorithmic improvements can drive tangible advancements in field automation.