Abstract
Honey bees are critical in pollination worldwide and are essential for crop productivity and ecological management. Knowing more about honeybees and their interaction with plants is urgently needed given the current global pollination crisis. For monitoring pollination, non-invasive approaches are recommended because they diminish the possibility of interfering with pollinator behaviour. Traditional techniques for manually recording pollinator activity in the field can be expensive and time-consuming. In this paper, we have developed a system for pollination monitoring by honeybees using computer vision technique. However, monitoring honeybees is challenging because of their tiny size, swift speed and complex outdoor environments. To detect honeybees in a frame, we have used YOLOv7 as our deep learning model. We have fine-tuned the model on a custom-created dataset for better detection and accuracy. The dataset contains snapshots of YouTube videos in which honeybees were pollinating flowers in different environments. We examined a specific setting where the honeybee pollinated the flowers at several intervals during the video using the detector. We have generated a heatmap and pollination activity graph based on the data from the detector. This information will help with better pollination management, which will increase crop production quality and yield.
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Acknowledgements
This work is supported by the grant received from DST, Govt. of India for the Technology Innovation Hub at the IIT Ropar in the framework of the National Mission on Interdisciplinary Cyber-Physical Systems.
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Kujur, V., Bedi, A.K., Saini, M. (2023). Monitoring Pollination by Honeybee Using Computer Vision. In: Zaynidinov, H., Singh, M., Tiwary, U.S., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2022. Lecture Notes in Computer Science, vol 13741. Springer, Cham. https://doi.org/10.1007/978-3-031-27199-1_40
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