Jianlei Kong

About

Jianlei Kong is a prolific researcher working at the intersection of artificial intelligence, robotics, and intelligent systems, with particular expertise in state estimation, human motion analysis, and smart agriculture. His most influential contribution, "The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods" (2021, 125 citations), charts a transformative shift in how automated systems process sensor data, bridging classical mathematical models with modern machine learning approaches. This work has become a key reference for researchers developing IoT systems, unmanned vehicles, and robotic platforms. Kong has also made significant strides in robotic mobility, with his 2018 survey on AI-enabled robot movement accumulating 53 citations, highlighting the persistent challenges robots face in complex real-world environments. A recurring theme across his portfolio is human gait phase recognition, where he has applied neural networks and deep learning to accelerometer data, supporting advances in rehabilitation robotics and prosthetics. More recently, Kong has turned his attention to precision agriculture, developing lightweight neural networks for crop pest detection and disease monitoring in autonomous farming systems. Collectively, his body of work reflects a researcher deeply committed to making intelligent machines more perceptive, adaptive, and practically deployable across diverse domains.

Research Focus

Key Achievements

Top Papers

1. 1
   The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

   125 citations · 2021
2. 2
   State-of-the-Art Mobile Intelligence: Enabling Robots to Move Like Humans by Estimating Mobility with Artificial Intelligence

   53 citations · 2018
3. 3
   Walking Gait Phase Detection Based on Acceleration Signals Using Voting-Weighted Integrated Neural Network

   33 citations · 2020
4. 4
   Real-Time Vegetables Recognition System based on Deep Learning Network for Agricultural Robots

   20 citations · 2018
5. 5
   Hybrid Deep-Learning Framework Based on Gaussian Fusion of Multiple Spatiotemporal Networks for Walking Gait Phase Recognition

   14 citations · 2020
6. 6
   An Acceleration Based Fusion of Multiple Spatiotemporal Networks for Gait Phase Detection

   13 citations · 2020
7. 7
   LCA-Net: A Lightweight Cross-Stage Aggregated Neural Network for Fine-Grained Recognition of Crop Pests and Diseases

   10 citations · 2023
8. 8
   An effective object detector via diffused graphic large selective kernel with one-to-few labelling strategy for small-scaled crop diseases detection

   6 citations · 2024
9. 9
   Inertial Pose Estimation Method Based on Multi-Genre Cascade Networks

   2 citations · 2024
10. 10
    GDMR-Net: A Novel Graphic Detection Neural Network via Multi-Crossed Attention and Rotation Annotation for Agronomic Applications in Supply Cyber Security

    1 citations · 2023

Key Collaborators

Contact & Links