References
- Abasi, A. K., Makhadmeh, S. N., Alomari, O. A., Tubishat, M., & Mohammed, H. J. (2023). Enhancing Rice Leaf Disease Classification: A Customized Convolutional Neural Network Approach. Sustainability, 15(20), 15039. https://doi.org/10.3390/SU152015039
- Bazame, H. C., Molin, J. P., Althoff, D., Martello, M., Bazame, H. C., Molin, J. P., Althoff, D., & Martello, M. (2021). Detection, classification, and mapping of coffee fruits during harvest with computer vision. CEAgr, 183, 106066. https://doi.org/10.1016/J.COMPAG.2021.106066
- Buonocore, D., Carratù, M., & Lamberti, M. (2022). Classification of coffee bean varieties based on a deep learning approach. 18th IMEKO TC10 Conference on Measurement for Diagnostic, Optimisation and Control to Saupport Sustainability and Resilience 2022, 14–19. https://doi.org/10.21014/TC10-2022.002
- Chang, S. J., & Liu, K. H. (2024). Multiscale Defect Extraction Neural Network for Green Coffee Bean Defects Detection. IEEE Access, 12, 15856–15866. https://doi.org/10.1109/ACCESS.2024.3356596
- Coffee: World Markets and Trade. (2024). https://apps.fas.usda.gov/psdonline/circulars/coffee.pdf
- Çorbacıoğlu, Ş. K., & Aksel, G. (2023). Receiver operating characteristic curve analysis in diagnostic accuracy studies: A guide to interpreting the area under the curve value. Turkish Journal of Emergency Medicine, 23(4), 195–198. https://doi.org/10.4103/TJEM.TJEM_182_23
- Crespo, A., Moncada, C., Crespo, F., & Morocho-Cayamcela, M. E. (2025). An efficient strawberry segmentation model based on Mask R-CNN and TensorRT. Artificial Intelligence in Agriculture, 15(2), 327–337. https://doi.org/10.1016/J.AIIA.2025.01.008
- Fu, H., Lu, J., Li, J., Zou, W., Tang, X., Ning, X., & Sun, Y. (2025). Winter Wheat Yield Prediction Using Satellite Remote Sensing Data and Deep Learning Models. Agronomy 15(1), 205. https://doi.org/10.3390/AGRONOMY15010205
- Gope, H. L., & Fukai, H. (2020). Normal and Peaberry Coffee Beans Classification from Green Coffee Bean Images Using Convolutional Neural Networks and Support Vector Machine. International Journal of Computer and Information Engineering, 14(6), 189–196. https://doi.org/10.5281/ZENODO.7085081
- He, J., Ren, Y., Li, W., & Fu, W. (2025). YOLOv11-RCDWD: A New Efficient Model for Detecting Maize Leaf Diseases Based on the Improved YOLOv11. Applied Sciences, 15(8), 4535. https://doi.org/10.3390/APP15084535
- He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. Proc. IEEE Comp. Society Conference on Computer Vision and Pattern Recognition, 2016-December, 770–778. https://doi.org/10.1109/CVPR.2016.90
- Hendrawan, Y., Rohmatulloh, B., Ilmi, F. I., Fauzy, M. R., Damayanti, R., Al Riza, D. F., Hermanto, M. B., & Sandra. (2021). AlexNet convolutional neural network to classify the types of Indonesian Zimka and Pentoś, Evaluation of the feasibility of coffee variety classification from bean images coffee beans. IOP Conference Series: Earth and Environmental Science, 905(1), 012059. https://doi.org/10.1088/1755-1315/905/1/012059
- Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. https://doi.org/10.1016/J.COMPAG.2018.02.016
- Konieczka, P. P., Aliaño-González, M. J., Ferreiro-González, M., Barbero, G. F., & Palma, M. (2020). Characterization of Arabica and Robusta Coffees by Ion Mobility Sum Spectrum. Sensors, 20(11), 3123. https://doi.org/10.3390/S20113123
- Korkmaz, A., Talan, T., Koşunalp, S., & Iliev, T. (2025). Comparison of deep learning models in automatic classification of coffee bean species. PeerJ Computer Science, 11, e2759. https://doi.org/10.7717/PEERJ-CS.2759/SUPP-2
- Lachenmeier, W., & Wee Ting Lee, K. (2023). Liberica Coffee Development and Refinement Project in Sarawak Malaysia. Proceedings, 89(1), 15. https://doi.org/10.3390/ICC2023-14849
- Lahai, P. M., Aikpokpodion, P. O., Bah, A. M., Lahai, M. T., Meinhardt, L. W., Lim, S., Ahn, E., Zhang, D., & Park, S. (2025). Unveiling the Genetic Diversity and Demographic History of Coffea stenophylla in Sierra Leone Using Genotyping-By-Sequencing. Plants, 14(1), 50. https://doi.org/10.3390/PLANTS14010050
- Manikandakumar, M., & Karthikeyan, P. (2022). Weed Classification Using Particle Swarm Optimization and Deep Learning Models. Computer Systems Science and Engineering, 44(1), 913–927. https://doi.org/10.32604/CSSE.2023.025434
- Martins, V. D. C., Godoy, R. L. D. O., Gouveâ, A. C. M. S., Santiago, M. C. P. D. A., Borguini, R. G., Braga, E. C. D. O., Pacheco, S., & Nascimento, L. D. S. D. M. Do. (2018). Fraud investigation in commercial coffee by chromatography. Food Quality and Safety, 2(3), 121–133. https://doi.org/10.1093/FQSAFE/FYY017
- Pai, P., Amutha, S., Basthikodi, M., Ahamed Shafeeq, B. M., Chaitra, K. M., & Gurpur, A. P. (2025). A twin CNN-based framework for optimized rice leaf disease classification with feature fusion. Journal of Big Data, 12(1), 1–27. https://doi.org/10.1186/s40537-025-01148-z
- Robusta or Arabica Object Detection Dataset by aefnattanon. (n.d.). Retrieved May 17, 2025, from https://universe.roboflow.com/aefnattanon/robusta-or-arabica
- Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 4510–4520. https://doi.org/10.1109/CVPR.2018.00474
- Santoso, I., Sholilah, A., Rucitra, A. L., Lu’ayya, N. M., & Choirun, A. (2025). Sustainable Coffee Supply Chain Risk Mitigation Analysis Using the Failure Mode and Effect Analysis. BIO Web of Conferences, 165, 02002. https://doi.org/10.1051/BIOCONF/202516502002
- Shara, P. N., Thomy, Z., Anhar, A., Harnelly, E., Ramlan, R. R., Hasan Kreung Kalee, T., & Banda Aceh, D. (2021). Morphological characterization of some Coffea arabica L. varieties in Gayo Experimental Garden Bener Meriah. Journal of Physics: Conference Series, 1882, 12092. https://doi.org/10.1088/1742-6596/1882/1/012092
- Tamayo-Monsalve, M. A., Ruiz, E. M., Pulgarin, J. P. V., Ortiz, M. A. B., Arteaga, H. B. A., Rubio, A. M., Alzate-Grisales, J. A., Garzon, D. A., Cano, V. R., Arias, S. O., Osorio, G., & Soto, R. T. (2022). Coffee Maturity Classification Using Convolutional Neural Networks and Transfer Learning. IEEE Access, 10, 42971–42982. https://doi.org/10.1109/ACCESS.2022.3166515
- Unal, Y., Taspinar, Y. S., Cinar, I., Kursun, R., & Koklu, M. (2022). Application of Pre-Trained Deep Convolutional Neural Networks for Coffee Beans Species Detection. Food Analytical Methods, 15(12), 3232–3243. https://doi.org/10.1007/s12161-022-02362-8
- Vujovic, Z. (2021). Classification Model Evaluation Metrics. International Journal of Advanced Computer Science and Applications, 13(6). https://doi.org/10.14569/IJACSA.2021.0120670
- Wu, Q., Liu, H., Zhu, H., Wang, C., Wang, H., Han, Z., Zhao, L., & Liu, F. (2025). YOLO_SSP: An Auto-Algorithm to Detect Mature Soybean Stem Nodes Based on Keypoint Detection. Agronomy, 15(5), 1128. https://doi.org/10.3390/AGRONOMY15051128
- Zhang, Q., Ma, D., Yang, Y., Hu, F., Fu, X., Li, G., Zhang, X., Wang, N., Liu, D., Wu, R., Bi, X., Lou, Y., Li, Y., Yu, H., Yan, W., & Li, Y. (2024). Population Genetic Characteristics of the Cultivated Coffea arabica with Whole-Genome Resequencing. Horticulturae, 10(11), 1153. https://doi.org/10.3390/horticulturae10111153