Papers
1
Total Citations
20
H-Index
1
About
Hao Feng is a researcher specializing in agricultural robotics, computer vision, and precision spraying technologies, with a particular focus on applying deep learning to horticultural automation challenges. His most notable work centers on the development of intelligent orchard spraying systems, where he has made significant contributions to solving one of the field's most persistent problems: accurate detection and segmentation of fruit tree canopies in complex, unstructured environments. In his highly cited 2022 study on citrus tree crown segmentation, Feng proposed an innovative approach combining RGB-D imaging with an improved Mask R-CNN architecture, enabling orchard spraying robots to dynamically adapt to variable tree growth stages. This work addresses real-world challenges such as overlapping canopies, inconsistent lighting, and irregular tree morphology — obstacles that have long hindered the deployment of autonomous agricultural robots. Accumulating 20 citations since its publication, this contribution has drawn meaningful attention from the precision agriculture and robotics communities. Feng's research sits at a critical intersection of machine learning and sustainable agriculture, advancing the goal of reducing pesticide waste through intelligent, vision-guided variable-rate spraying. His work is an important reference point for researchers and engineers developing next-generation smart farming systems.
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Top Papers
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