Abstract
The detection of individual plants within field images is critical for many applications in precision agriculture and research. Computer vision models for object detection, while often highly accurate, require large amounts of labeled data for training, something that is not readily available for most plants. To address the challenge of creating large datasets with accurate labels, we used indoor images of maize plants to create synthetic field images with automatically derived bounding box labels, enabling the generation of thousands of synthetic images without any manual labeling. Training an object detection model (Faster R-CNN) exclusively on synthetic images led to a mean average precision (mAP) value of 0.533 when the model was evaluated on pre-processed real plot images. When fine-tuned with a small number of real plot images, the model pre-trained on the synthetic images (mAP = 0.884) outperformed the model that was not pre-trained.
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Acknowledgements
This research was supported in part by an appointment to the Agricultural Research Service (ARS) Research Participation Program administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy (DOE) and the U.S. Department of Agriculture (USDA). ORISE is managed by ORAU under DOE contract number DE-SC0014664. Funding was provided by the United States Department of Agriculture, Agricultural Research Service, SCINet Postdoctoral Fellows Program. All opinions expressed in this publication are the author’s and do not necessarily reflect the policies and views of USDA, DOE, or ORAU/ORISE.
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Pandey, P., Best, N.B., Washburn, J.D. (2023). Synthetically Labeled Images for Maize Plant Detection in UAS Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2023. Lecture Notes in Computer Science, vol 14361. Springer, Cham. https://doi.org/10.1007/978-3-031-47969-4_42
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DOI: https://doi.org/10.1007/978-3-031-47969-4_42
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