3 papers
cs.RO2026
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…
cs.CV2025
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…
cs.CV2025
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…