Yi Ning

Guilin University of Electronic Technology

Papers

Total Citations

H-Index

About

Yi Ning is a rising researcher at the forefront of precision agriculture and computer vision, with a primary focus on developing intelligent systems for automated crop harvesting. Their most notable contribution is the SN-YOLO framework, a rotation detection method specifically designed to identify and localize tomato fruits in complex greenhouse environments. This work addresses critical challenges in vision-guided robotics, such as variable lighting and background clutter, by enhancing detection accuracy for occluded or irregularly oriented fruits. With 3 citations since its 2025 publication, SN-YOLO has already garnered attention for its practical potential in reducing labor costs and improving harvest efficiency. Ning’s research bridges deep learning and agricultural robotics, offering scalable solutions for real-world farming. Their work is particularly impactful for students and researchers interested in applying computer vision to sustainable agriculture, as it demonstrates how tailored detection algorithms can overcome environmental variability. As a young scholar, Ning’s focus on deployable, rotation-aware models marks them as a promising contributor to the growing field of automated horticulture.