Revista UIS Ingenierías

Vol. 23 No. 4 (2024): Revista UIS Ingenierías
Articles

Integration of Artificial Intelligence and Precision Agriculture in Coffee Crops

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  • Cristian Andrés Hernández-Salazar,
  • Octavio Andrés González-Estrada,
  • Germán González-Silva
Cristian Andrés Hernández-Salazar
Universidad Industrial de Santander
Octavio Andrés González-Estrada
Universidad Industrial de Santander
Germán González-Silva
Universidad Industrial de Santander
DOI: https://doi.org/10.18273/revuin.v23n4-2024012

Published 2024-12-01

Keywords

  • Deep Learning,
  • coffea arabica,
  • convolutional neural networks,
  • precision agriculture

How to Cite

Hernández-Salazar, C. A. ., González-Estrada, O. A., & González-Silva, G. (2024). Integration of Artificial Intelligence and Precision Agriculture in Coffee Crops. Revista UIS Ingenierías, 23(4), 145–158. https://doi.org/10.18273/revuin.v23n4-2024012

Copyright (c) 2024 Revista UIS Ingenierías

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This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

Abstract

Artificial intelligence (AI) is transforming the agribusiness sector by facilitating tasks such as weather prediction, pest and disease detection, and optimization of water and fertilizer use. These applications not only increase the efficiency and sustainability of agricultural systems, but also improve productivity and resilience to climate change. This paper conducts a systematic review on the use of AI in coffee production, analyzing studies found in recognized databases such as Scopus, Web of Science, IEEE Xplore and Google Scholar. The search included combinations of keywords tailored to capture relevant studies, such as “coffee” AND “machine learning”, “artificial intelligence” AND “precision agriculture”, “pest detection” OR “neural networks”, and “sustainability”. Initially 452 articles were identified, of which 85 met the inclusion criteria after a rigorous screening and exclusion process. The review identified that AI applications in coffee production are mainly focused on the early detection of diseases such as coffee rust using computer vision and convolutional neural networks, the optimization of intelligent irrigation systems that integrate sensors and algorithms to reduce water consumption by up to 20%, and the use of agricultural robotics to improve operational efficiency and decrease labor dependence. The technologies also promote more sustainable practices and improve traceability in the coffee supply chain. The results of this review shed light on how AI techniques can optimize coffee production and contribute to the development of more efficient and sustainable agricultural systems. This work provides a framework that can be useful for guiding future research and guiding producers towards resilient practices in the face of climate change and increasing sustainability demands.

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References

  1. J. F. Maldonado-Moreno, J. S. Martínez-Castañeda, Y. D. Beltrán-Malaver, I. C. Riveros-Pineda, G. D. Tovar-Hernández, “Metodología de producción de prótesis de miembro inferior: una revisión exhaustiva,” Revista UIS Ingenierías, vol. 23, no. 2, 2024, doi: https://doi.org/10.18273/revuin.v23n2-2024011
  2. P. Rajpurkar, E. Chen, O. Banerjee, and E. J. Topol, “AI in health and medicine,” Nat Med, vol. 28, no. 1, pp. 31–38, 2022, doi: https://doi.org/10.1038/s41591-021-01614-0
  3. L. F. Ortiz-Torres, E. Gómez-Luna, and E. Marlés-Sáenz, “Estudio del uso y contribución de la inteligencia artificial para la operación en redes eléctricas,” Revista UIS Ingenierías, vol. 23, no. 2, 2024, doi: https://doi.org/10.18273/revuin.v23n2-2024003
  4. C. M. Ruiz-Díaz, B. Quispe-Suarez, O. A. González-Estrada, “Two-phase oil and water flow pattern identification in vertical pipes applying long short-term memory networks,” Emergent Mater, vol. 7, no. 5, pp. 1983–1995, 2024, doi: https://doi.org/10.1007/s42247-024-00631-2
  5. C. A. Hernández-Salazar, A. Carreño-Verdugo, O. A. González-Estrada, “Prediction of the volume fraction of liquid- liquid two-phase flow in horizontal pipes using Long-Short Term Memory Networks,” Revista UIS Ingenierías, vol. 23, no. 3, pp. 19–32, 2024, doi: https://doi.org/10.18273/revuin.v23n3-2024002
  6. T. van Klompenburg, A. Kassahun, and C. Catal, “Crop yield prediction using machine learning: A systematic literature review,” Comput Electron Agric, vol. 177, p. 105709, 2020, doi: https://doi.org/10.1016/j.compag.2020.105709
  7. J. Silva, N. Varela, and O. B. P. Lezama, “Multispectral image analysis for the detection of diseases in coffee production,” in Advances in Intelligent Systems and Computing, vol. 1237, pp. 198–205, 2021.
  8. K. Lagos-Ortiz, J. Medina-Moreira, A. Alarcón-Salvatierra, M. F. Morán, J. del Cioppo-Morstadt, and R. Valencia-García, “Decision Support System for the Control and Monitoring of Crops,” in Advances in Intelligent Systems and Computing, vol. 901, pp. 20–28, 2019.
  9. E. Lasso, T. T. Thamada, C. A. A. Meira, and J. C. Corrales, “Graph Patterns as Representation of Rules Extracted from Decision Trees for Coffee Rust Detection,” in Communications in Computer and Information Science, vol. 544, pp. 405–414, 2015.
  10. A. Sharma, A. Jain, P. Gupta, and V. Chowdary, “Machine Learning Applications for Precision Agriculture: A Comprehensive Review,” IEEE Access, vol. 9, 2021, doi: https://doi.org/10.1109/ACCESS.2020.3048415
  11. A. R. Pereira, M. B. P. de Camargo, and N. A. V. Nova, “Coeficiente de cultivo de cafezais com base no índice de área foliar para irrigação de precisão,” Bragantia, vol. 70, no. 4, pp. 946–951, 2011, doi: https://doi.org/10.1590/S0006-87052011000400030
  12. J. Jung, M. Maeda, A. Chang, M. Bhandari, A. Ashapure, and J. Landivar-Bowles, “The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems,” Curr Opin Biotechnol, vol. 70, pp. 15–22, 2021, doi: https://doi.org/10.1016/j.copbio.2020.09.003
  13. L. T. Wright, R. Robin, M. Stone, and D. E. Aravopoulou, “Adoption of Big Data Technology for Innovation in B2B Marketing,” Journal of Business-to-Business Marketing, vol. 26, no. 3–4, pp. 281–293, Oct. 2019, doi: https://doi.org/10.1080/1051712X.2019.1611082
  14. R. Gebbers, V. I. Adamchuk, “Precision Agriculture and Food Security,” Science, vol. 327, no. 5967, pp. 828–831, 2010, doi: https://doi.org/10.1126/science.1183899
  15. J. C. O. Koh, G. Spangenberg, S. Kant, “Automated Machine Learning for High-Throughput Image-Based Plant Phenotyping,” Remote Sens (Basel), vol. 13, no. 5, p. 858, 2021, doi: https://doi.org/10.3390/rs13050858
  16. K. Y. Li et al., “An automated machine learning framework in unmanned aircraft systems: New insights into agricultural management practices recognition approaches,” Remote Sens, vol. 13, no. 16, 2021, doi: https://doi.org/10.3390/rs13163190
  17. Y. Son, X. Zhang, Y. Yoon, J. Cho, and S. Choi, “LSTM–GAN based cloud movement prediction in satellite images for PV forecast,” J Ambient Intell Humaniz Comput, vol. 14, no. 9, pp. 12373–12386, 2023, doi: https://doi.org/10.1007/S12652-022-04333-7/TABLES/8
  18. L. Zhang et al., “CA-U2-Net: Contour Detection and Attention in U2-Net for Infrared Dim and Small Target Detection,” IEEE Access, vol. 11, pp. 88245–88257, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3305942
  19. C. Wang et al., “Predicting Plant Growth and Development Using Time-Series Images,” Agronomy 2022, vol. 12, no. 9, p. 2213, 2022, doi: https://doi.org/10.3390/AGRONOMY12092213
  20. D. J. Pérez-Ortega, F. A. Bolaños-Alomia, A. Marco da Silva, “Variables que influyen en la aplicación de la agricultura de precisión en Colombia: revisión de estudios,” Ciencia & Tecnología Agropecuaria, vol. 23, no. 1, p. 2298, 2021, doi: https://doi.org/10.21930/rcta.vol23_num1_art:2298
  21. E. Pino V., “Los drones una herramienta para una agricultura eficiente: un futuro de alta tecnología,” Idesia, vol. 37, 2019, doi: https://doi.org/10.4067/S0718-34292019005000402
  22. L. García, L. Parra, J. M. Jimenez, J. Lloret, P. Lorenz, “IoT-based smart irrigation systems: An overview on the recent trends on sensors and iot systems for irrigation in precision agriculture,” Sensors, vol. 20, no. 4, 2020, doi: https://doi.org/10.3390/s20041042
  23. I. C. Arango-Palacio, “Oportunidades para la transformación digital de la cadena de suministro del sector bananero basado en software con inteligencia artificial,” Revista Politécnica, vol. 17, no. 33, pp. 47–63, 2021, doi: https://doi.org/10.33571/rpolitec.v17n33a4
  24. J. J. O. Rodríguez, J. G. A. Marín, D. C. P. Montiel, “Comparison of Performance in Mechanised Agricultural Tasks Between Precision and Conventional Agriculture on Farms in Tolima (Colombia)?,” Revista de Gestão Social e Ambiental, vol. 16, no. 3, p. e03099, 2022, doi: https://doi.org/10.24857/rgsa.v16n3-014
  25. Y. A. Montoya Alvarez and J. I. Rodríguez Molano, “Colombian agriculture: approaching agriculture 4.0,” Ingeniería Solidaria, vol. 18, no. 2, pp. 1–19, 2022, doi: https://doi.org/10.16925/2357-6014.2022.02.04
  26. J. C. García L., H. Posada-Suárez, and P. Läderach, “Recommendations for the Regionalizing of Coffee Cultivation in Colombia: A Methodological Proposal Based on Agro-Climatic Indices,” PLoS One, vol. 9, no. 12, p. e113510, 2014, doi: https://doi.org/10.1371/journal.pone.0113510
  27. Y. K. Kang, S. H. Im, J. S. Ryu, J. Lee, H. J. Chung, “Simple visualized readout of suppressed coffee ring patterns for rapid and isothermal genetic testing of antibacterial resistance,” Biosens Bioelectron, vol. 168, p. 112566, 2020, doi: https://doi.org/10.1016/j.bios.2020.112566
  28. A. Simon-Gruita, M. D. Pojoga, N. Constantin, and G. Duta-Cornescu, “Genetic Engineering in Coffee,” in Caffeinated and Cocoa Based Beverages, Elsevier, 2019, pp. 447–488.
  29. R. Li et al., “Optimizing drip fertigation at different periods to improve yield, volatile compounds and cup quality of Arabica coffee,” Front Plant Sci, vol. 14, p. 1148616, 2023, doi: https://doi.org/10.3389/fpls.2023.1148616
  30. A. I. Magalhães Júnior et al., “A critical techno-economic analysis of coffee processing utilizing a modern fermentation system: Implications for specialty coffee production,” Food and Bioproducts Processing, vol. 125, pp. 14–21, 2021, doi: https://doi.org/10.1016/j.fbp.2020.10.010
  31. A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture,” Computers and Electronics in Agriculture, vol. 147, pp. 70-90 2018, doi: https://doi.org/10.1016/j.compag.2018.02.016
  32. Y. Kittichotsatsawat, V. Jangkrajarng, K. Y. Tippayawong, “Enhancing Coffee Supply Chain towards Sustainable Growth with Big Data and Modern Agricultural Technologies,” Sustainability, vol. 13, no. 8, p. 4593, Apr. 2021, doi: https://doi.org/10.3390/su13084593
  33. R. Gera and A. Jain, “Predicting Crop Yield in Smart Agriculture Using IoT and Machine Learning for Sustainable Development,” Communications in Computer and Information Science, vol. 1939 CCIS, pp. 64–76, 2023, doi: https://doi.org/10.1007/978-3-031-47055-4_6/COVER
  34. K. K. Rai, “Integrating speed breeding with artificial intelligence for developing climate-smart crops,” Molecular Biology Reports 2022 49:12, vol. 49, no. 12, pp. 11385–11402, 2022, doi: https://doi.org/10.1007/S11033-022-07769-4
  35. Y. Pham, K. Reardon-Smith, S. Mushtaq, G. Cockfield, “The impact of climate change and variability on coffee production: a systematic review,” Clim Change, vol. 156, no. 4, pp. 609–630, Oct. 2019, doi: https://doi.org/10.1007/S10584-019-02538-Y/TABLES/1
  36. K. Gikunda, “Harnessing Artificial Intelligence for Sustainable Agricultural Development in Africa: Opportunities, Challenges, and Impact,” Computers and Society, 2024, doi: https://doi.org/10.48550/arXiv.2401.06171
  37. R. C. de Oliveira, R. D. de S. Silva, “Artificial Intelligence in Agriculture: Benefits, Challenges, and Trends,” Applied Sciences, vol. 13, no. 13, p. 7405, 2023, doi: https://doi.org/10.3390/app13137405
  38. A. Yu. Fedosov, A. M. Menshikh, “Precision farming technologies in vegetable growing,” Vegetable crops of Russia, vol. 0, no. 6, pp. 40–45, 2022, doi: https://doi.org/10.18619/2072-9146-2022-6-40-45
  39. M. J. Smith, “Getting value from artificial intelligence in agriculture,” Anim Prod Sci, vol. 60, no. 1, p. 46, 2020, doi: https://doi.org/10.1071/AN18522
  40. H. Tayara, K. Chong, “Object Detection in Very High-Resolution Aerial Images Using One-Stage Densely Connected Feature Pyramid Network,” Sensors, vol. 18, no. 10, p. 3341, 2018, doi: https://doi.org/10.3390/s18103341
  41. H. C. Bazame, J. P. Molin, D. Althoff, M. Martello, L. D. P. Corrêdo, “Mapping coffee yield with computer vision,” Precis Agric, vol. 23, no. 6, pp. 2372–2387, 2022, doi: https://doi.org/10.1007/s11119-022-09924-0
  42. I. H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” SN Comput Sci, vol. 2, no. 3, 2021, doi: https://doi.org/10.1007/S42979-021-00592-X
  43. R. Oliveira, P. F. Lima, M. Cirillo, J. Martensson, and B. Wahlberg, “Trajectory Generation using Sharpness Continuous Dubins-like Paths with Applications in Control of Heavy-Duty Vehicles,” in 2018 European Control Conference (ECC), 2018, pp. 935–940.
  44. A. Lenci, S. Padó, “Editorial: Perspectives for natural language processing between AI, linguistics and cognitive science,” Front Artif Intell, vol. 5, p. 1059998, Nov. 2022, doi: https://doi.org/10.3389/FRAI.2022.1059998/BIBTEX
  45. R. Dara, S. M. Hazrati Fard, J. Kaur, “Recommendations for ethical and responsible use of artificial intelligence in digital agriculture,” Front Artif Intell, vol. 5, 2022, doi: https://doi.org/10.3389/frai.2022.884192
  46. J. P. Garcia Vazquez, R. S. Torres, and D. B. Perez Perez, “Scientometric Analysis of the Application of Artificial Intelligence in Agriculture,” Journal of Scientometric Research, vol. 10, no. 1, pp. 55–62, 2021, doi: https://doi.org/10.5530/jscires.10.1.7
  47. E. Hysa, A. Kruja, N. U. Rehman, and R. Laurenti, “Circular Economy Innovation and Environmental Sustainability Impact on Economic Growth: An Integrated Model for Sustainable Development,” Sustainability, vol. 12, no. 12, p. 4831, 2020, doi: https://doi.org/10.3390/su12124831
  48. S. Huet et al., “Populations of the Parasitic Plant Phelipanche ramosa Influence Their Seed Microbiota,” Front Plant Sci, vol. 11, p. 540645, 2020, doi: https://doi.org/10.3389/FPLS.2020.01075/BIBTEX
  49. H. Tamiminia, S. Homayouni, H. McNairn, and A. Safari, “A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations,” International Journal of Applied Earth Observation and Geoinformation, vol. 58, pp. 201–212, 2017, doi: https://doi.org/10.1016/j.jag.2017.02.010
  50. X. Ge, J. Yu, F. Chen, F. Kong, and H. Wang, “Toward Verifiable Phrase Search Over Encrypted Cloud-Based IoT Data,” IEEE Internet Things J, vol. 8, no. 16, pp. 12902–12918, 2021, doi: https://doi.org/10.1109/JIOT.2021.3063855
  51. J. G. M. Esgario, P. B. C. de Castro, L. M. Tassis, and R. A. Krohling, “An app to assist farmers in the identification of diseases and pests of coffee leaves using deep learning,” Information Processing in Agriculture, vol. 9, no. 1, pp. 38–47, 2022, doi: https://doi.org/10.1016/j.inpa.2021.01.004
  52. D. Velásquez, A. Sánchez, S. Sarmiento, M. Toro, M. Maiza, B. Sierra, “A Method for Detecting Coffee Leaf Rust through Wireless Sensor Networks, Remote Sensing, and Deep Learning: Case Study of the Caturra Variety in Colombia,” Applied Sciences, vol. 10, no. 2, p. 697, 2020, doi: https://doi.org/10.3390/app10020697
  53. R. Selvanarayanan, S. Rajendran, S. Algburi, O. Ibrahim Khalaf, and H. Hamam, “Empowering coffee farming using counterfactual recommendation based RNN driven IoT integrated soil quality command system,” Sci Rep, vol. 14, no. 1, p. 6269, 2024, doi: https://doi.org/10.1038/s41598-024-56954-x
  54. A. Sharma, A. Jain, P. Gupta, and V. Chowdary, “Machine Learning Applications for Precision Agriculture: A Comprehensive Review,” IEEE Access, vol. 9, 2021, doi: https://doi.org/10.1109/ACCESS.2020.3048415
  55. Y. Liu, J. Ruiz-Menjivar, L. Zhang, J. Zhang, M. E. Swisher, “Technical training and rice farmers’ adoption of low-carbon management practices: The case of soil testing and formulated fertilization technologies in Hubei, China,” J Clean Prod, vol. 226, pp. 454–462, 2019, doi: https://doi.org/10.1016/j.jclepro.2019.04.026

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