A5054099841- Smart Agriculture and AI
- Mosquito-borne diseases and control
- 3D Surveying and Cultural Heritage
- Robotics and Sensor-Based Localization
- Date Palm Research Studies
- Remote Sensing in Agriculture
- Plant Virus Research Studies
- Insect-Plant Interactions and Control
- Indoor and Outdoor Localization Technologies
- Spectroscopy and Chemometric Analyses
Murdoch University
2022-2024
Shanghai Jiao Tong University
2019
Aphids are persistent insect pests that severely impact agricultural productivity. The detection of aphid infestations is critical for mitigating their effects. This paper presents an artificial intelligence approach to detect aphids in crop images captured by consumer-grade RGB imaging cameras. In addition detecting the presence aphids, size important indicator infestation severity. To address these, we present a Bayesian multi-task learning model and estimate simultaneously. Our employs...
Context Insects are a major threat to crop production. They can infect, damage, and reduce agricultural yields. Accurate fast detection of insects will help insect control. From computer algorithm point view, from imagery is tiny object problem. Handling objects in large datasets challenging due small resolution the an image, other nuisances such as occlusion, noise, lack features. Aims Our aim was achieve high-performance detector using enhanced artificial intelligence machine learning...
This In robotics, visual-inertial sensor fusion has become one of the most active research topics; optimization-based approaches have gone beyond filtering in terms robustness and accuracy. For Simultaneous Localization Mapping (SLAM), accurate initialization is essential for this nonlinear system which requires an estimation initial states (Inertial Measurement Unit (IMU) biases, scale, gravity, velocity). Therefore, our goal to propose a more robust method. First, we estimate gyroscope...