Max Robotics — Global Service Robot Directory + US Certification

About

Fengjun Chen is a leading researcher in agricultural robotics and computer vision, with a focus on automating fruit maturity detection for precision agriculture. His work centers on developing lightweight, real-time deep learning models that can accurately assess crop readiness directly in orchard environments. Chen’s most impactful contribution is the modified lightweight YOLO network for detecting *Camellia oleifera* fruit maturity, which has garnered 55 citations since 2024, demonstrating its immediate relevance to the field. He further advanced this line of research with Olive-EfficientDet, a specialized model for multi-cultivar olive fruit maturity detection in complex orchard settings, cited 34 times. These innovations address critical challenges in automated harvesting by enabling efficient, on-device inference without sacrificing accuracy. Chen also contributed to the foundational technology of forestry mobile robots, developing calibration methods for vision systems that integrate monocular cameras with 2D LiDAR to improve autonomous navigation safety and efficiency. His work bridges the gap between cutting-edge computer vision and practical agricultural automation, offering scalable solutions for the growing demands of smart farming and forestry operations.