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Soybean seed vigor classification through an effective image learning-based approach

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Abstract

To obtain better productivity in soybean crops, it is essential to use high-quality seeds. The quality impacts the growing process of the plant. Currently, human specialists perform this quality control process through seed vigor analysis.It is not only a labor-intensive approach highly susceptible to failure, but also the analysts must understand seed anatomy. This paper proposes a learning-based expert approach and pipeline capable of automatically defining the seeds’ vigor. Our proposed approach simplifies, in an intuitive manner, the entire process of the seed vigor analysis. To corroborate our learning-based approach, we applied it in a real environment to capture, process, and use two real-world datasets composed of hundreds of soybean images with different damages from a seed classification laboratory. From the experiments, we can testify to a strong efficacy and efficiency in these real-world cases, reaching up to 80.17%±2.37 of accuracy. Our approach provides technological enhancements and opens new ways to solve the seed vigor definition regarding in-place image analysis, aggregating an entire on-the-fly seed analysis pipeline.

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References

  1. Ali H, Lali MI, Nawaz MZ, Sharif M, Saleem BA (2017) Symptom based automated detection of citrus diseases using color histogram and textural descriptors. Comput Electron Agric 138:92–104

    Article  Google Scholar 

  2. Al-Sharadqah AA, Ho DKC (2016) Solutions and evaluations for fitting of concentric circles. Signal Proc 120:468–479

    Article  Google Scholar 

  3. Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp 1800–1807

  4. del Egido L, Navarro-Miró D, Martinez Heredia V, Toorop P, Iannetta P (2017) A spectrophotometric assay for robust viability testing of seed batches using 2,3,5-triphenyl tetrazolium chloride: Using hordeum vulgare l. as a model. Front Plant Sci 8(747):1–9

    Google Scholar 

  5. Deng J, Dong W, Socher R, Li L-J, Kai Li Li Fei-Fei (2009) ImageNet: A large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, USA, pp 248–255

  6. Franca-Neto JB, Krzyzanowski FC, da Costa NP (1998) The Tetrazolium Test for Soybean Seeds, pp. 1–72. Embrapa-CNPSo - Brazilian Corporation for Agricultural Research, Londrina

  7. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, USA, pp 770–778

  8. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) MobileNets: Efficient convolutional neural networks for mobile vision applications

  9. Inacio Patricio D, Rieder R (2018) Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Comput Electron Agric 153:69–81

    Article  Google Scholar 

  10. Jha D, Riegler MA, Johansen D, Halvorsen P, Johansen HD (2020) DoubleU-Net: A deep convolutional neural network for medical image segmentation

  11. Kamalalochana S, Guptha N (2019) Optimizing random forest to detect disease in apple leaf. Int J Eng Adv Technol 8(5S):244–249

    Article  Google Scholar 

  12. Kuhn M, Johnson K (2013) Applied Predictive Modeling. Springer, Berlin, Germany

    Book  Google Scholar 

  13. Leist N, Kramer S (2011) ISTA Working Sheets on Tetrazolium Testing - Volume I and II. International Seed Testing Association, Wallisellen, Zurich

  14. Liu W, Li W, Huang Y, Peng J (2017) Image retrieval by subspaceprojected color and texture features. In: Proceedings of the IEEE International Conference on Image Processing, pp 2891–2895

  15. Meyer GE, Neto JC (2008) Verification of color vegetation indices for automated crop imaging applications. Comput Electron Agric 63(2):282–293

    Article  Google Scholar 

  16. Nanni L, Paci M, Caetano dos Santos FL, Skottman H, Juuti-Uusitalo K, Hyttinen J (2016) Texture descriptors ensembles enable imagebased classification of maturation of human stem cell-derived retinal pigmented epithelium. Plos One 11(2):1–29

    Article  Google Scholar 

  17. Narwade J, Kumar B (2016) Local and global color histogram feature for color content-based image retrieval system. In: Proceedings of the International Congress on Information and Communication Technology, Springer, Berlin, Germany, pp 293–300

  18. Penatti OAB, Valle E, da S Torres R, (2012) Comparative study of global color and texture descriptors for web image retrieval. J Vis Commun Image Represent 23(2):359–380

  19. Pereira DF, Bugatti PH, Lopes FM, Souza ALSM, Saito PTM (2019) Contributing to agriculture by using soybean seed data from the tetrazolium test. Data in Brief 23:103652

    Article  Google Scholar 

  20. Pereira DF, Bugatti PH, Lopes FM, de Souza ALSM, Saito PTM (2021) Assessing active learning strategies to improve the quality control of the soybean seed vigor. IEEE Trans Ind Electron 68(2):1675–1683

    Article  Google Scholar 

  21. Peters J (2010) Tetrazolium Testing Handbook. Association of Official Seed Analysts and Society of Commercial Seed Technologists, Moline, USA

    Google Scholar 

  22. Rehman T, Mahmud M, Chang Y, Jin J, Shin J (2018) Current and future applications of statistical machine learning algorithms for agricultural machine vision systems. Comput Electron Agric 156:585–605

    Article  Google Scholar 

  23. Rocha DM, Nóbrega LHP, Bernardi D, Conti G, Nakajima EA, Ziech MF, Bazzi CL (2019) Random forests in the supervised classification of multidimensional images of the tetrazolium test. J Agric Sci 11(15):115–124

    Google Scholar 

  24. Russell SJ, Norvig P (2016) Artificial Intelligence: a Modern Approach. Pearson Education Limited, USA

    Google Scholar 

  25. Sandid F, Douik A (2016) Robust color texture descriptor for material recognition. Pattern Recognition Letters 80:15–23

    Article  Google Scholar 

  26. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition

  27. Stanitsas P, Cherian A, Li X, Truskinovsky A, Morellas V, Papanikolopoulos N (2016) Evaluation of feature descriptors for cancerous tissue recognition. In: Proceedings of the IEEE International Conference on Pattern Recognition, pp 1490–1495

  28. Stehling RO, Nascimento MA, Falcao AX (2002) A compact and efficient image retrieval approach based on border/interior pixel classification. In: Proceedings of the International Conference on Information and Knowledge Management, pp 102–109

  29. Strutz T (2016) Data Fitting and Uncertainty (A Practical Introduction to Weighted Least Squares and Beyond), 2nd edn. Springer, Berlin, Germany

    Google Scholar 

  30. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2015) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2818–2826

  31. Valenzuela G, Celebi ME, Schaefer G (2018) Color quantization using coreset sampling. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, IEEE, USA, pp 2096–2101

  32. Wolpert DH (1996) The lack of a priori distinctions between learning algorithms. Neural Comput 8(7):1341–1390

    Article  Google Scholar 

  33. Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? In: Proceedings of the International Conference on Neural Information Processing Systems, pp 3320–3328

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Funding

This work has been supported by National Council for Scientific and Technological Development - CNPq [grant numbers \(\#431668/2016\)-7, \(\#422811/2016\)-5]; Coordination for the Improvement of Higher Education Personnel - CAPES; Fundação Araucária and UTFPR.

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Authors and Affiliations

  1. Department of Computing, Federal University of Technology - UTFPR, 1640 Alberto Carazzai Ave, Cornelio Procopio, 86300-000, PR, Brazil

    Marcelo Souza Jr., William C. Horikoshi, Priscila T. M. Saito & Pedro H. Bugatti

  2. Department of Computing, Federal University of São Carlos - UFSCar, km 235, Rod. Washington Luís, São Carlos, 13565-905, SP, Brazil

    Priscila T. M. Saito & Pedro H. Bugatti

Authors
  1. Marcelo Souza Jr.
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  2. William C. Horikoshi
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  3. Priscila T. M. Saito
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  4. Pedro H. Bugatti
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Contributions

\(\bullet \) Marcelo de Souza Jr.: Conceptualization, Methodology, Validation, Formal analysis, Data Curation, Software, Investigation, Writing - Original Draft

\(\bullet \) William C. Horikoshi: Conceptualization, Methodology, Validation, Formal analysis, Software

\(\bullet \) Priscila T. M. Saito: Conceptualization, Methodology, Writing - Original Draft, Writing - Review & Editing, Supervision, Project administration, Funding acquisition

\(\bullet \) Pedro H. Bugatti: Conceptualization, Methodology, Writing - Original Draft, Writing - Review & Editing, Supervision, Project administration, Funding acquisition

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Correspondence to Pedro H. Bugatti.

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Souza, M., Horikoshi, W.C., Saito, P.T.M. et al. Soybean seed vigor classification through an effective image learning-based approach. Multimed Tools Appl 83, 13113–13136 (2024). https://doi.org/10.1007/s11042-023-15804-0

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