Dandan Kong

Kunming University of Science and Technology

Papers

Total Citations

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About

Dandan Kong is a researcher at the forefront of agricultural robotics and computer vision, with a focused expertise in intelligent harvesting systems for high-value crops. Her most impactful work centers on developing advanced deep learning models for fruit detection and ripeness classification, directly addressing the critical need for selective harvesting automation. In her landmark 2024 study, Kong introduced the YOLOv8+ model, a novel enhancement to the YOLOv8 architecture, coupled with sophisticated image processing methods to achieve precise strawberry detection and ripeness identification. This work, which has already garnered 48 citations, is pivotal for enabling fruit-picking robots to distinguish between ripe and unripe strawberries, a key step toward fully autonomous, efficient harvesting. By tackling the challenge of ripeness classification in complex field environments, Kong’s contributions are laying the essential groundwork for the next generation of intelligent agricultural systems, promising to significantly reduce labor costs and improve crop yield quality. Her research bridges the gap between cutting-edge AI and practical agricultural engineering, making her a notable emerging voice in precision agriculture.