Abstract
Weeds cost Australian farmers around $1.5 billion a year in weed control activities and a further $2.5 billion a year in lost agricultural production. Weed management requires a good understanding of weed inventories and distribution for effective management. Nowadays, cutting-edge research provides improved options for remote weed detection, facilitating broader adoption of these transformational technologies like airborne, drones, and satellites, to provide tools to improve weed management in complex environmental and agricultural systems. In this paper, we present our recent research work on applying two deep learning approaches to identify tiny weeds from airborne captured RGB images with the goal of determining feasible approaches for weeds managers. High accuracy and low false-positive have been achieved through convolutional network learning. To address the challenges remote sensing images had, such as low image resolution, high similarity, and a large volume of data, the deep learning-based approach shows superior performance to detect weeds in heterogeneous landscapes. Our findings will enhance remote sensing capabilities in the Australian weed community through knowledge and skills transfer and stimulate the development of applications to process.
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Acknowledgement
Authors would like to thank for the grant support from the Australian Department of Agriculture, Water and the Environment (DAWE), would also like to gratefully acknowledge the support of Wendy Menz and Liesl Grant (NPWS) for their assistance in the collection of data at the study site.
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Zheng, L. et al. (2023). Remote Tiny Weeds Detection. In: Wang, H., et al. Image and Video Technology. PSIVT 2022. Lecture Notes in Computer Science, vol 13763. Springer, Cham. https://doi.org/10.1007/978-3-031-26431-3_13
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DOI: https://doi.org/10.1007/978-3-031-26431-3_13
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