Publications (5)
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…
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…
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…
Development of a Boston-area 50-km fiber quantum network testbed
Eric Bersin, Matthew Grein, Madison Sutula +22
Distributing quantum information between remote systems will necessitate the integration of emerging quantum components with existing communication infrastructure. This requires un…