Jingguo Liu

Papers

Total Citations

189

H-Index

About

Jingguo Liu is a leading researcher in agricultural computer vision and precision farming, with a focus on deep learning applications for crop monitoring. His most impactful work, "Maize seedling detection under different growth stages and complex field environments based on an improved Faster R–CNN" (2019), has garnered 189 citations, establishing him as a pioneer in applying object detection algorithms to real-world agricultural challenges. Liu's major contribution lies in developing robust, adaptable models that can accurately identify maize seedlings across varying growth stages and challenging field conditions—such as variable lighting, occlusions, and soil backgrounds—significantly advancing the automation of crop management. By enhancing the Faster R-CNN architecture with feature fusion and attention mechanisms, he has improved detection precision and recall, enabling more reliable early-stage crop assessment. This work has direct implications for smart agriculture, including weed control, yield estimation, and autonomous weeding robots. Liu's research bridges the gap between state-of-the-art computer vision and practical farming needs, making him a key figure in the digital transformation of agriculture. His ongoing efforts continue to push the boundaries of how AI can support sustainable and efficient food production.