Kunfeng Lv

Guangxi University of Science and Technology

Papers

Total Citations

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About

Kunfeng Lv is a leading researcher in agricultural robotics and computer vision, whose work is transforming precision agriculture through intelligent detection and automation. His primary research areas encompass deep learning-based object detection, semantic segmentation, and their application to robotic harvesting and precision spraying in complex orchard and field environments. Lv’s major contributions include developing lightweight, real-time detection models tailored for challenging agricultural conditions. His highly cited work, "MYOLO: A Lightweight Fresh Shiitake Mushroom Detection Model Based on YOLOv3" (2023, 35 citations), addresses the critical need for rapid and accurate mushroom detection amidst dense shading and diverse morphology, directly enabling efficient robotic harvesting. Further demonstrating his impact, his research on "Citrus Tree Crown Segmentation of Orchard Spraying Robot Based on RGB-D Image and Improved Mask R-CNN" (2022, 20 citations) provides a robust solution for variable-rate spraying by accurately segmenting tree crowns despite complex backgrounds. By integrating RGB-D data with advanced convolutional neural networks, Lv’s work overcomes key visual challenges in unstructured agricultural settings, offering practical, high-performance tools that significantly advance the capabilities of autonomous farming systems.