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All issues Volume 328 (2025) EPJ Web Conf., 328 (2025) 01038 References
Open Access
Issue
EPJ Web Conf.
Volume 328, 2025
First International Conference on Engineering and Technology for a Sustainable Future (ICETSF-2025)
Article Number 01038
Number of page(s) 12
DOI https://doi.org/10.1051/epjconf/202532801038
Published online 18 June 2025
  1. https://www.tradeimex.in/blogs/top-10-coffee-producing-countries-global-export-data. [Google Scholar]
  2. https://www.leocoffee.co.in/blogs/blogs/challenges-in-coffee-farming?srsltid=AfmBOopJdEhrcFvo0X9MC4jmrIEcDlrgsIUsNDp7BYdLSyb SUIpOs5y. [Google Scholar]
  3. K.P. Lewis and J.D. Espineli, "Classification And Detection Of Nutritional Deficiencies In Coffee Plants Using Image Processing And Convolutional Neural Network (CNN)," 2020, [Online]. Available: www.ijstr.org [Google Scholar]
  4. H.M.O. Otieno, B.A. Alwenge, and O.O. Okumu, "Coffee Production Challenges and Opportunities in Tanzania: The Case Study of Coffee Farmers in Iwindi, Msia and Lwati Villages in Mbeya Region," Asian Journal of Agricultural and Horticultural Research, pp. 1–14, Feb. 2019, DOI: 10.9734/ajahr/2019/v3i229993. [CrossRef] [Google Scholar]
  5. D. Monsalve, M. Trujillo, and D. Chaves, "Automatic Classification of Nutritional Deficiencies in Coffee Plants," in 6th Latin-American Conference on Networked and Electronic Media (LACNEM 2015), Institution of Engineering and Technology, 2015, pp. 11 6 (6). DOI: 10.1049/ic.2015.0317. [Google Scholar]
  6. https://en.vikaspedia.in/viewcontent/agriculture/crop-production/integrated-pest-managment/ipm-for-commercial-crops/ipm-strategies-for-coffee/coffee-nutritional-deficiencies-disorders. [Google Scholar]
  7. Samuel Dave R. Signo, Chloe Lei, G. Tuquero, and Edwin R. Arboleda, "Coffee disease detection and classification using image processing: A Literature review," International Journal of Science and Research Archive, vol. 11, no. 1, pp. 1614–1621, Feb. 2024, DOI: 10.30574/ijsra.2024.11.1.0212. [CrossRef] [Google Scholar]
  8. K.P. Lewis and J.D. Espineli, "Classification And Detection Of Nutritional Deficiencies In Coffee Plants Using Image Processing And Convolutional Neural Network (CNN)," 2016, [Online]. Available: www.ijstr.org [Google Scholar]
  9. S. Timilsina et al., "CNDD-Net: A Lightweight Attention-Based Convolutional Neural Network for Classifying Corn Nutritional Deficiencies and Leaf Diseases," Electronics (Basel), vol. 14, no. 7, p. 1482, Apr. 2025, DOI: 10.3390/electronics14071482. [Google Scholar]
  10. J. Sikati and J.C. Nouaze, "YOLO-NPK: A Lightweight Deep Network for Lettuce Nutrient Deficiency Classification Based on Improved YOLOv8 Nano f," Engineering Proceedings, vol. 58, no. 1, 2023, DOI: 10.3390/ecsa-10-16256. [Google Scholar]
  11. L. Huang, M. Chen, and Z. Peng, "YOLOv8-G: An Improved YOLOv8 Model for Major Disease Detection in Dragon Fruit Stems," Sensors, vol. 24, no. 15, Aug. 2024, DOI: 10.3390/s24155034. [Google Scholar]
  12. H.I.M.-C.J.A.-D. Victor A. Tuesta-Monteza, "CoLeaf-DB: Peruvian coffee leaf images dataset for coffee leaf nutritional deficiencies detection and classification," Elsevier, 2023. [Google Scholar]
  13. P. Abbasian and T.A. Hammond, "A Comprehensive Analysis of Early Alzheimer Disease Detection from 3D sMRI Images Using Deep Learning Frameworks," Information (Switzerland), vol. 15, no. 12, Dec. 2024, DOI: 10.3390/info15120746. [Google Scholar]
  14. M. Hussain and R. Khanam, "In-Depth Review of YOLOv1 to YOLOv10 Variants for Enhanced Photovoltaic Defect Detection," Solar, vol. 4, no. 3, pp. 351–386, Jun. 2024, DOI: 10.3390/solar4030016. [CrossRef] [Google Scholar]
  15. Z.A. Simonyan, "Very Deep Convolutional Networks for Large-Scale Image Recognition.," arXiv 2014, 2015. [Google Scholar]
  16. M.A.R. Alif and M. Hussain, "YOLOvl to YOLOvlO: A comprehensive review of YOLO variants and their application in the agricultural domain," Jun. 2024, [Online]. Available: http://arxiv.org/abs/2406.10139 [Google Scholar]
  17. Q.J. Jocher, https://github.com/ultralytics/ultralytics/blob/main/CITATION.cff, YOLO by Ultralytics. [Google Scholar]
  18. S. Goshika, K. Meksem, K.R. Ahmed, and N. Lakhssassi, "Deep Learning Model for Classifying and Evaluating Soybean Leaf Disease Damage," Int J Mol Sci, vol. 25, no. 1, Jan. 2024, DOI: 10.3390/ijms25010106. [Google Scholar]

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