Xiaolei Zhang
Papers
1
Total Citations
29
H-Index
1
About
Xiaolei Zhang is a leading researcher in agricultural informatics and precision horticulture, with a focus on developing computer vision and machine learning techniques for non-destructive fruit quality assessment. Her most-cited work, "Strawberry ripeness classification method in facility environment based on red color ratio of fruit rind" (2023, 29 citations), introduces a novel approach that leverages the red color ratio of the fruit rind to automatically classify strawberry ripeness under controlled greenhouse conditions. This contribution addresses a critical challenge in smart agriculture—enabling real-time, objective ripeness grading to reduce post-harvest losses and optimize harvesting schedules. By integrating colorimetric analysis with environmental sensing, Zhang’s method provides a scalable, low-cost solution for facility-based farming systems. Her research bridges the gap between traditional agronomic practices and modern data-driven technologies, offering practical tools for growers to enhance yield quality and operational efficiency. With her work gaining traction in the precision agriculture community, Zhang is recognized for advancing the automation of fruit maturity detection, laying groundwork for broader applications in crop monitoring and robotic harvesting.
Research Focus
Key Achievements
Top Papers
- 1