5 papers
Automated Pest Counting in Water Traps through Active Robotic Stirring for Occlusion Handling
Xumin Gao, Mark Stevens, Grzegorz Cielniak
Existing image-based pest counting methods rely on single static images and often produce inaccurate results under occlusion. To address this issue, this paper proposes an automate…
Counting with Confidence: Accurate Pest Monitoring in Water Traps
Xumin Gao, Mark Stevens, Grzegorz Cielniak
Accurate pest population monitoring and tracking their dynamic changes are crucial for precision agriculture decision-making. A common limitation in existing vision-based automatic…
Developing a Hybrid Convolutional Neural Network for Automatic Aphid Counting in Sugar Beet Fields
Xumin Gao, Wenxin Xue, Callum Lennox +2
Aphids can cause direct damage and indirect virus transmission to crops. Timely monitoring and control of their populations are thus critical. However, the manual counting of aphid…
Automatic Detection, Positioning and Counting of Grape Bunches Using Robots
Xumin Gao
In order to promote agricultural automatic picking and yield estimation technology, this project designs a set of automatic detection, positioning and counting algorithms for grape…
Interactive Image-Based Aphid Counting in Yellow Water Traps under Stirring Actions
Xumin Gao, Mark Stevens, Grzegorz Cielniak
The current vision-based aphid counting methods in water traps suffer from undercounts caused by occlusions and low visibility arising from dense aggregation of insects and other o…