Xinjie Wei

Weifang University, Shandong University of Science and Technology

Papers

Total Citations

H-Index

About

Xinjie Wei is a leading researcher in agricultural robotics and precision horticulture, specializing in deep learning for fruit detection and automated harvesting. Their work addresses critical challenges in developing intelligent picking robots, particularly in distinguishing target fruits from complex backgrounds such as branch shading and cluster adhesion. Wei’s major contributions include the Shine-Muscat Grape Detection Model (S), which achieves high-accuracy identification of green grapes under challenging conditions, and TDPPL-Net, a lightweight real-time tomato detection and picking point localization model designed for deployment on low-cost, GPU-free industrial PCs. These innovations have garnered significant attention, with their most-cited papers accumulating 16 and 11 citations respectively within a short period. Notably, TDPPL-Net represents a breakthrough in balancing detection speed and accuracy for resource-constrained harvesting robots, directly addressing the practical limitations of existing large-parameter models. Wei’s work is instrumental in advancing the feasibility of automated fruit harvesting, offering scalable solutions that can be integrated into real-world agricultural systems. Their research continues to shape the future of smart farming by enabling more efficient, cost-effective robotic harvesters.