Deep Learning for Super Resolution of Sugarcane Crop Line Imagery from Unmanned Aerial Vehicles | SpringerLink
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Deep Learning for Super Resolution of Sugarcane Crop Line Imagery from Unmanned Aerial Vehicles

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Advances in Visual Computing (ISVC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14361))

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Abstract

Improving resolution of sugarcane crop images is crucial for extracting valuable information related to productivity, diseases, and water stress. With the rise of remote sensing technologies like Unmanned Aerial Vehicles (UAVs), the number of images available has grown exponentially. In this study, we aim to enhance image resolution using deep learning techniques, namely MuLUT, LeRF, and Real-ESRGAN, to optimize extraction of sugarcane agronomic characteristics. Although these models were initially designed for landscapes, people, cars, and anime images, our experiments with agricultural images show promising results, outperforming classic upsampling algorithms by an impressive 482.81%. Visually, the image quality improvement is significant, making our approach an attractive alternative for extracting crucial information about the crop. This research has the potential to revolutionize the analysis of sugarcane crops, opening new possibilities for precision agriculture and improved agricultural decision-making.

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Notes

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    Interpolation is the process of estimating pixel values in an image when reconstructing or resizing it.

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  5. 5.

    Engineering Village: www.engineeringvillage.com.

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    http://nuvemuav.com/batmap.

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Acknowledgments

The authors of this study would like to acknowledge the support of the Fundação de Amparo á Pesquisa do Estado de Goiás (FAPEG) - 18/2020, Process no. 202110267000772, and for the support of the Coordenação de Aperfeiçoamento de Pessoal de NÍvel Superior (CAPES) - Financing Code #001.

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Authors and Affiliations

  1. Institute of Computing, Federal University of Goias, Goiânia, GO, Brazil

    Emília A. Nogueira, Juliana Paula Felix, Afonso Ueslei Fonseca, Gabriel Vieira, Deborah S. A. Fernandes & Fabrizzio Soares

  2. Agronomy School, Federal University of Goias, Goiânia, GO, Brazil

    Bruna M. Oliveira

  3. Federal Institute of Education, Science and Technology of Goiás, Urutaí, GO, Brazil

    Julio Cesar Ferreira

Authors
  1. Emília A. Nogueira
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Correspondence to Fabrizzio Soares .

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Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Google Research, Mountain View, CA, USA

    Golnaz Ghiasi

  3. New York University, New York, USA

    Yi Fang

  4. Ben-Gurion University, Be'er Sheva, Israel

    Andrei Sharf

  5. Microsoft Research, Beijing, China

    Yue Dong

  6. The University of Oklahoma, Norman, OK, USA

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  7. University of Maryland, Collage Park, MD, USA

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  8. University of Central Florida, Orlando, FL, USA

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  9. InnerOptic Technology, Hillsborough, NC, USA

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Nogueira, E.A. et al. (2023). Deep Learning for Super Resolution of Sugarcane Crop Line Imagery from Unmanned Aerial Vehicles. 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_46

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  • DOI: https://doi.org/10.1007/978-3-031-47969-4_46

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