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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

  1. 1.

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

  2. 2.

    Scopus: www.scopus.com.

  3. 3.

    IEEE Xplore: ieeexplore.ieee.org/Xplore/home.jsp.

  4. 4.

    ACM Library: dl.acm.org/.

  5. 5.

    Engineering Village: www.engineeringvillage.com.

  6. 6.

    http://nuvemuav.com/batmap.

References

  1. Food, FAO et al.: Faostat statistical database. Rome: Food and Agriculture Organisation of the United Nations (2020)

    Google Scholar 

  2. Mulyono, S., et al.: Identifying sugarcane plantation using LANDSAT-8 images with support vector machines. In: IOP Conference Series: Earth and Environmental Science, vol. 47, p. 012008. IOP Publishing (2016)

    Google Scholar 

  3. Food FAOSTAT. Agriculture organization of the united nations FAO statistical database, p. 40 (2023). https://www.fao.org/. Accessed June 2023

  4. Crossa, J., et al.: The modern plant breeding triangle: optimizing the use of genomics, phenomics, and enviromics data. Front. Plant Sci. 12, 651480 (2021)

    Google Scholar 

  5. Nogueira, E., Oliveira, B., Bulcão-Neto, R., Soares, F.: A systematic review of the literature on machine learning methods applied to high throughput phenotyping in agricultural production. IEEE Lat. Am. Trans. 21(7), 783–796 (2023)

    Article  Google Scholar 

  6. Sun, J., et al.: High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage. Theor. Appl. Genet. 132, 1705–1720 (2019)

    Google Scholar 

  7. Yang, W., et al.: Crop phenomics and high-throughput phenotyping: past decades, current challenges, and future perspectives. Mol. Plant 13(2), 187–214 (2020)

    Google Scholar 

  8. Furbank, R.T., Tester, M.: Phenomics-technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 16(12), 635–644 (2011)

    Google Scholar 

  9. Mota, L.F.M., et al.: Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data. J. Dairy Sci. 104(7), 8107–8121 (2021)

    Google Scholar 

  10. Araus, J.L., Cairns, J.E.: Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci. 19(1), 52–61 (2014)

    Google Scholar 

  11. Gebremedhin, A., Badenhorst, P.E., Wang, J., Spangenberg, G.C., Smith, K.F.: Prospects for measurement of dry matter yield in forage breeding programs using sensor technologies. Agronomy 9(2), 65 (2019). https://doi.org/10.3390/agronomy9020065. https://www.mdpi.com/2073-4395/9/2/65

    Article  Google Scholar 

  12. Araus, J.L., Kefauver, S.C., Zaman-Allah, M., Olsen, M.S., Cairns, J.E.: Translating high-throughput phenotyping into genetic gain. Trends Plant Sci. 23(5), 451–466 (2018)

    Google Scholar 

  13. Rocha, B., et al.: Skew angle detection and correction in text images using RGB gradient. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds.) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. LNCS, vol. 13232, pp. 249–262. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06430-2_21

  14. Demirel, M., Kaya, Y., Polat, N.: Investigation of the effect of UAV flight altitude in map production. Intercont. Geoinf. Days 4, 21–24 (2022)

    Google Scholar 

  15. Chang, Y., Li, D., Gao, Y., Yun, S., Jia, X.: An improved yolo model for UAV fuzzy small target image detection. Appl. Sci. 13(9), 5409 (2023)

    Article  Google Scholar 

  16. Rosenfeld, A.: Digital Picture Processing. Academic Press, Cambridge (1976)

    Google Scholar 

  17. Wang, X., Xie, L., Dong, C., Shan, Y.: Real-ESRGAN: training real-world blind super-resolution with pure synthetic data. In: Proceedings of the CVF International Conference on Computer Vision, vol. 2021-October (2021)

    Google Scholar 

  18. Li, J., Chen, C., Cheng, Z., Xiong, Z.: MuLUT: cooperating multiple look-up tables for efficient image super-resolution. In: Avidan, S., Brostow, G., Cisse, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision – ECCV 2022. ECCV 2022. LNCS, vol. 13678, pp. 238–256. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19797-0_14

  19. Li, J., et al.: Learning steerable function for efficient image resampling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5866–5875 (2023)

    Google Scholar 

  20. Panagiotopoulou, A., et al.: Super-resolution techniques in photogrammetric 3D reconstruction from close-range UAV imagery. Heritage 6(3), 2701–2715 (2023)

    Article  Google Scholar 

  21. Wang, X., et al.: ESRGAN: enhanced super-resolution generative adversarial networks. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)

    Google Scholar 

  22. Zhang, K., Zuo, W., Zhang, L.: Learning a single convolutional super-resolution network for multiple degradations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3262–3271 (2018)

    Google Scholar 

  23. Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2017)

    Google Scholar 

  24. Bilecen, B.B., Ayazoglu, M.: Bicubic++: slim, slimmer, slimmest-designing an industry-grade super-resolution network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1623–1632 (2023)

    Google Scholar 

  25. Singh, A., Singh, J.: Review and comparative analysis of various image interpolation techniques. In: 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), vol. 1, pp. 1214–1218. IEEE (2019)

    Google Scholar 

  26. Keys, R.: Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust. Speech Signal Process. 29(6), 1153–1160 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  27. Rosenfeld, A.: Picture processing by computer. ACM Comput. Surv. (CSUR) 1(3), 147–176 (1969)

    Article  MATH  Google Scholar 

  28. Nogueira, E.A., et al.: Upsampling of unmanned aerial vehicle images of sugarcane crop lines with a Real-ESRGAN. In: 2023 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–4. IEEE (2023)

    Google Scholar 

  29. Zhang, R., Isola, P., Efros, A. A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)

    Google Scholar 

  30. Bishop, C.M.: Pattern recognition and machine learning. In: Jordan, M., Kleinberg, J., Schölkopf, B. (eds.) Pattern Recognition. Information Science and Statistics, vol. 4, no. 4, pp. 738. Springer (2006). https://doi.org/10.1117/1.2819119. http://www.library.wisc.edu/selectedtocs/bg0137.pdf

  31. Das, K., Jiang, J., Rao, J.N.K.: Mean squared error of empirical predictor. Ann. Stat. 32(2), 818–840 (2004). https://doi.org/10.1214/009053604000000201

    Article  MathSciNet  MATH  Google Scholar 

  32. Korhonen, J., You, J.: Peak signal-to-noise ratio revisited: is simple beautiful? In: 2012 Fourth International Workshop on Quality of Multimedia Experience, pp. 37–38. IEEE (2012)

    Google Scholar 

  33. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Google Scholar 

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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

    Chris Weaver

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

    Zhicheng Leo

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

    Joseph J. LaViola Jr.

  9. InnerOptic Technology, Hillsborough, NC, USA

    Luv Kohli

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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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  • Online ISBN: 978-3-031-47969-4

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