Long Zhou
Papers
1
Total Citations
12
H-Index
1
About
Long Zhou is a researcher at the forefront of agricultural artificial intelligence, with a primary focus on computer vision and deep learning for precision agriculture. His most notable contribution is the development of YOLOC-tiny, a generalized lightweight real-time detection model specifically designed for challenging unstructured environments. This work, published in 2024 and already garnering 12 citations, addresses a critical bottleneck in agricultural robotics: the accurate detection of large, non-green-ripe citrus fruits across multiple ripeness levels and varieties. By building upon the YOLOv7 architecture and introducing efficiency enhancements, Zhou’s model achieves high precision while maintaining a lightweight footprint, enabling deployment on resource-constrained edge devices. This innovation has significant implications for automated fruit harvesting and yield estimation, overcoming the limitations of previous models that struggled with generalization in complex, real-world orchard settings. Zhou’s work represents a meaningful step toward bridging the gap between state-of-the-art object detection and practical agricultural applications, making him a promising voice in the field of smart farming and agricultural robotics.
Research Focus
Key Achievements
Top Papers
- 1