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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…
cs.CV2024
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…