Fault Detection in Sugarcane Crop Lines Using Hough Transform

  • Alexsandro M. Carneiro UCDB
  • Gabriel T. H. Higa UCDB
  • Alexandre G. de O. Rodrigues UCDB
  • José M. Junior UFMS
  • Hemerson Pistori UCDB / UFMS

Resumo


In this work, we present an algorithm for detecting faults in sugarcane planting rows using the Hough transform, supported by image pre-processing techniques, such as binarization, morphological operators, filters, and skeletonization. Seven preprocessing calibration parameters are investigated. For the calibration and testing of the proposed method, a set of images with 589 samples, captured by UAV in an area located in Nova Andradina, MS, is presented.

Referências

M. H. Saleem, J. Potgieter, and K. M. Arif, “Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments,” vol. 22, no. 6, pp. 2053–2091, 2021. [Online]. Available: https://doi.org/10.1007/s11119-021-09806-x

M. De Clercq, A. Vats, and A. Biel, “Agriculture 4.0: The future of farming technology,” Proceedings of the World Government Summit, Dubai, UAE, pp. 11–13, 2018.

S. Khanal, K. KC, J. P. Fulton, S. Shearer, and E. Ozkan, “Remote sensing in agriculture—accomplishments, limitations, and opportunities,” Remote Sensing, vol. 12, no. 22, 2020. [Online]. Available: [link]

D. M. Bulanon, T. Hestand, C. Nogales, B. Allen, and J. Colwell, Machine Vision System for Orchard Management. Cham: Springer International Publishing, 2020, pp. 197–240. [Online]. Available: https://doi.org/10.1007/978-3-030-22587-2_7

E. Mavridou, E. Vrochidou, G. A. Papakostas, T. Pachidis, and V. G. Kaburlasos, “Machine vision systems in precision agriculture for crop farming,” Journal of Imaging, vol. 5, no. 12, p. 89, 2019.

D. I. Patrício and R. Rieder, “Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review,” Computers and Electronics in Agriculture, vol. 153, pp. 69–81, 2018. [Online]. Available: [link].

A. Wang, W. Zhang, and X. Wei, “A review on weed detection using ground-based machine vision and image processing techniques,” Computers and Electronics in Agriculture, vol. 158, pp. 226–240, 2019. [Online]. Available: [link].

R. O. Duda and P. E. Hart, “Use of the hough transformation to detect lines and curves in pictures,” Communications of the ACM, vol. 15, no. 1, pp. 11–15, 1972.

C. S. de Oliveira, G. D. F. a, J. P. H. Sansão, L. A. Mozelli, and M. C. da Silva Jr, “Determinação da orientação em linhas de cultura:investigando métodos de processamento de imagens para aplicação na agricultura de precisão,” in Workshop of Undergraduate Works (WUW) in SIBGRAPI 2012 (XXV Conference on Graphics, Patterns and Images), J. P. P. A. Paiva, Ed., Ouro Preto, MG, Brazil, august 2012. [Online]. Available: [link].

G. Bayar, “The use of hough transform method and knot-like turning for motion planning and control of an autonomous agricultural vehicle,” Agriculture, vol. 13, no. 1, 2023. [Online]. Available: [link]

K. Zheng, X. Zhao, C. Han, Y. He, C. Zhai, and C. Zhao, “Design and experiment of an automatic row-oriented spraying system based on machine vision for early-stage maize corps,” Agriculture, vol. 13, no. 3, 2023.
Publicado
13/11/2023
CARNEIRO, Alexsandro M.; HIGA, Gabriel T. H.; RODRIGUES, Alexandre G. de O.; M. JUNIOR, José; PISTORI, Hemerson. Fault Detection in Sugarcane Crop Lines Using Hough Transform. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 18. , 2023, São Bernardo do Campo/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 90-95. DOI: https://doi.org/10.5753/wvc.2023.27538.