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Algorithm for Determination of Pepper Maturity Classes by Combination of Color and Spectral Indices Cover

Algorithm for Determination of Pepper Maturity Classes by Combination of Color and Spectral Indices

Open Access
|Sep 2024

References

  1. Atanassova, S., Nikolov, P., Valchev, N., Masheva, S., & Yorgov, D. (2019). Early detection of powdery mildew (Podosphaera xanthii) on cucumber leaves based on visible and near-infrared spectroscopy. AIP Conference Proceedings, 2075, 160014, 1-5.
  2. Bell Pepper [online]. (n.d.). Retrieved March 11, 2024, from https://postharvest.ucdavis.edu
  3. Cermakova, I., Komarkova, J., & Sedlak, P. (2019). Calculation of visible spectral indices from UAV-based data: Small water bodies monitoring. In 2019 14th Iberian Conference on Information Systems and Technologies (CISTI) (pp. 1-5).
  4. Cong, P., Li, S., Zhou, J., Lv, K., & Feng, H. (2023). Research on instance segmentation algorithm of greenhouse sweet pepper detection based on improved Mask RCNN. Agronomy, 13, 196, 1-24.
  5. Description of undersized pepper varieties [online]. (n.d.). Retrieved March 10, 2024, from https://tomathouse.com/4/nizkoroslye-sorta-perca.html (in Bulgarian)
  6. Farrell, A., Wan, G., Rush, S., Martin, J., Belant, J., Butler, A., & Godwin, D. (2019). Machine learning of large-scale spatial distributions of wild turkeys with high-dimensional environmental data. Ecology and Evolution, 1-12.
  7. Georgieva, Ts., Mihaylova, A., & Daskalov, P. (2020). Research of the possibilities for determination of some basic soil properties using image processing. In Proceedings of the 7th International Conference on Energy Efficiency and Agricultural Engineering (EE&AE) (pp. 1-4).
  8. Harel, B., Kurtser, P., Parmet, Y., & Edan, Y. (2017). Sweet pepper maturity evaluation. Advances in Animal Biosciences: Precision Agriculture, 8(2), 167-171.
  9. Harel, B., Parmet, Y., & Edan, Y. (2020a). Maturity classification of sweet peppers using image datasets acquired in different times. Computers in Industry, 121, 103274, 1-10.
  10. Harel, B., van Essen, R., Parmet, Y., & Edan, Y. (2020b). Viewpoint analysis for maturity classification of sweet peppers. Sensors, 20, 3783, 1-22.
  11. Kirilova, E. (2012). Classifier design for identification of corn kernels, damaged by Fusarium moniliforme, using color features. In Proceedings of University of Ruse, Ruse, Bulgaria, 2012, 51(3.1), 168-175. (in Bulgarian)
  12. Kootstra, G., Wang, X., Blok, P. M., Hemming, J., & van Henten, E. (2021). Selective harvesting robotics: Current research, trends, and future directions. Current Robotics Reports, 2(2), 95–104.
  13. Lapegna, M., Balzano, W., Meyer, N., & Romano, D. (2021). Clustering algorithms on low-power and high-performance devices for edge computing environments. Sensors, 21(16), 5395.
  14. Lehnert, C., McCool, C., Sa, I., & Perez, T. (2020). Performance improvements of a sweet pepper harvesting robot in protected cropping environments. Journal of Field Robotics, 37(7), 1197–1223.
  15. Liu, C., & Niu, S. (2024). Automated fruit sorting in smart agriculture system: Analysis of deep learning-based algorithms. International Journal of Advanced Computer Science and Applications (IJACSA), 15(1).
  16. Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., García-Navarrete, O., & Blasco, J. (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food Bioprocess Technology, 5, 1121-1142.
  17. Pathare, P., Opara, U., & Al-Said, F. (2013). Colour measurement and analysis in fresh and processed foods: A review. Food Bioprocess Technology, 6, 36-60.
  18. Rizzo, M., Marcuzzo, M., Zangari, A., Gasparetto, A., & Albarelli, A. (2023). Fruit ripeness classification: A survey. Artificial Intelligence in Agriculture, 7, 44-57.
  19. Smits, B. (2000). An RGB to spectrum conversion for reflectances. University of Utah, 21 January 2000, 1-10.
  20. van Essen, R., Harel, B., Kootstra, G., & Edan, Y. (2022). Dynamic viewpoint selection for sweet pepper maturity classification using online economic decisions. Applied Sciences, 12, 4414, 1-15.
  21. Villaseñor-Aguilar, M.-J., Bravo-Sánchez, M.-G., Padilla-Medina, J.-A., Vázquez-Vera, J. L., Guevara-González, R.-G., García-Rodríguez, F.-J., & Barranco-Gutiérrez, A.-I. (2020). A maturity estimation of bell pepper (Capsicum annuum L.) by artificial vision system for quality control. Applied Sciences, 10(15), 5097.
  22. Wyman, C., Sloan, P.-P., & Shirley, P. (2013). Simple analytic approximations to the CIE XYZ color matching functions. Journal of Computer Graphics Techniques (JCGT), 2(2), 1-11.
  23. Yotova, G., Tsitouridou, R., Tsakovski, S., & Simeonov, V. (2016). Urban air quality assessment using monitoring data of fractionized aerosol samples, chemometrics and meteorological conditions. Journal of Environmental Science and Health, Part A, 51(7), 544-552.
Language: English
Page range: 103 - 116
Submitted on: Jun 8, 2024
Accepted on: Jul 31, 2024
Published on: Sep 5, 2024
Published by: Latvia University of Life Sciences and Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2024 Miroslav Vasilev, Galya Shivacheva, Vanya Stoykova, Zlatin Zlatev, published by Latvia University of Life Sciences and Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.