Liang Mao
Papers
1
Total Citations
141
H-Index
1
About
Liang Mao is a researcher at the forefront of agricultural informatics and computer vision, with a focus on precision agriculture and automated fruit detection. His most-cited work, "Detection of passion fruits and maturity classification using Red-Green-Blue Depth images" (2018, 141 citations), exemplifies his major contribution: developing non-destructive, image-based methods for crop monitoring and quality assessment. By integrating RGB-D data with machine learning, Mao’s research enables accurate, real-time identification of fruit maturity, significantly reducing labor costs and improving harvest efficiency. This work has become a foundational reference for studies in agricultural robotics and smart farming, demonstrating his impact in bridging computer vision with practical agronomy. Beyond this, Mao’s broader portfolio explores sensor fusion and deep learning for plant phenotyping, contributing to sustainable food production. His achievements include advancing automated systems that enhance yield prediction and post-harvest sorting, making him a key figure in the digital transformation of agriculture. For students and researchers, Mao’s work offers a compelling model of how interdisciplinary approaches can solve real-world challenges in food security and resource management.
Research Focus
Key Achievements
Top Papers
- 1