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Assessing the impact of emerging technologies on sustainable fruit production: A systematic review of the literature
Evaluación del impacto de las tecnologías emergentes en la producción frutícola sostenible: una revisión sistemática de literatura
DOI:
https://doi.org/10.15446/agron.colomb.v41n3.107255Keywords:
productivity, machine learning, artificial Intelligence, fruits, yield, bibliometrics (en)productividad, aprendizaje automático, inteligencia artificial, frutos, rendimiento, bibliometría (es)
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Agriculture 4.0 refers to innovations in technological tools used in agriculture to achieve different objectives, such as adapting the supply chain to avoid waste, increasing productivity and collecting mass data through ICT (Information and Communication Technologies) to meet the growing food demand of the population. The objective of this study is to conduct a systematic literature review to evaluate the impact of emerging technologies on sustainable fruit production. Initially, a bibliographic search was conducted on the technologies currently implemented in agriculture; the Bibliometrix library of the R Studio software was used, and then an analysis of relevant scientific publications published in the last ten years was carried out through the VOSviewer® software, which allowed the construction and visualization of bibliometric networks. The results show Europe and China as the leading regions in technological development, while developing countries face economic and research limitations; in Colombia, the use of Agriculture 4.0 is focused on the implementation of satellite images for monitoring agro-climatic conditions. In summary, Agriculture 4.0 aims to achieve economic, social, and environmental sustainability in the agri-food sector through data-generating technologies to improve production, reduce costs, and ensure food safety and quality. However, there is a technology gap between developed and developing countries that affects the adoption of these innovations. More support is therefore needed from governments, academia, and the private sector to drive innovation, training, and adoption of these technologies, which can contribute to the economic, social, and environmental development of the country.
La Agricultura 4.0 se refiere a las innovaciones en las herramientas tecnológicas utilizadas en la agricultura para lograr diferentes objetivos, como la adaptación de la cadena de suministro para evitar el desperdicio, el aumento de la productividad y la recolección masiva de datos a través de las TIC (Tecnologías de la Información y la Comunicación), para satisfacer la creciente demanda de alimentos de la población. El objetivo de este trabajo es realizar una revisión bibliográfica sistemática para evaluar el impacto de las tecnologías emergentes en la producción frutícola sostenible. Inicialmente, se realizó una búsqueda bibliográfica sobre las tecnologías actualmente implementadas en la agricultura; se utilizó la biblioteca Bibliometrix del software R Studio, y luego se llevó a cabo un análisis de las publicaciones científicas relevantes publicadas en los últimos diez años, a través del software VOSviewer® que permitió la construcción y visualización de redes bibliométricas. Los resultados muestran a Europa y China como las regiones líderes en desarrollo tecnológico, mientras que los países en desarrollo enfrentan limitaciones económicas y de investigación; en Colombia, el uso de la Agricultura 4.0 se centra en la implementación de imágenes satelitales para el monitoreo de las condiciones agroclimáticas. En resumen, la Agricultura 4.0 pretende lograr la sostenibilidad económica, social y ambiental del sector agroalimentario a través de tecnologías generadoras de datos para mejorar la producción, reducir costes y garantizar la seguridad y calidad de los alimentos. Sin embargo, existe una brecha tecnológica entre los países desarrollados y los países en desarrollo que afecta a la adopción de estas innovaciones. Por lo tanto, se necesita más apoyo de los gobiernos, el mundo académico y el sector privado para impulsar la innovación, la formación y la adopción de estas tecnologías, que pueden contribuir al desarrollo económico, social y medioambiental del país.
References
Abbasi, R., Martinez, P., & Ahmad, R. (2022). The digitization of agricultural industry – a systematic literature review on agriculture 4.0. Smart Agricultural Technology, 2, Article 100042. https://doi.org/10.1016/j.atech.2022.100042 DOI: https://doi.org/10.1016/j.atech.2022.100042
Agudelo Cano, M. J., Callejas Marulanda, E. E., Henao-Céspedes, V., Cardona-Morales, O., & Garcés Gómez, Y. A. (2023). Quantification of flowering in coffee growing with low-cost RGB sensor UAV-mounted. Scientia Horticulturae, 309, Article 111649. https://doi.org/10.1016/j.scienta.2022.111649 DOI: https://doi.org/10.1016/j.scienta.2022.111649
Aker, J. C. (2011). Dial “A” for agriculture: A review of information and communication technologies for agricultural extension in developing countries. Agricultural Economics, 42(6), 631–647. https://doi.org/10.1111/j.1574-0862.2011.00545.x DOI: https://doi.org/10.1111/j.1574-0862.2011.00545.x
Akhter, R., & Ahmad, S. (2022). Precision agriculture using IoT data analytics and machine learning. Journal of King Saud University - Computer and Information Sciences, 34(8), 5602–5618. https://doi.org/10.1016/j.jksuci.2021.05.013 DOI: https://doi.org/10.1016/j.jksuci.2021.05.013
Arinta, R. R., & Andi W. R. E. (2019, November 20-21). Natural disaster application on big data and machine learning: A review. [2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE] Yogyakarta, Indonesia, (pp. 249–254). https://doi.org/10.1109/ICITISEE48480.2019.9003984 DOI: https://doi.org/10.1109/ICITISEE48480.2019.9003984
Arrubla-Hoyos, W., Ojeda-Beltrán, A., Solano-Barliza, A., Rambauth-Ibarra, G., Barrios-Ulloa, A., Cama-Pinto, D., Arrabal-Campos, F. M., Martínez-Lao, J. A., Cama-Pinto, A., & Manzano-Agugliaro, F. (2022). Precision agriculture and sensor systems applications in Colombia through 5G networks. Sensors, 22(19), Article 7295. https://doi.org/10.3390/s22197295 DOI: https://doi.org/10.3390/s22197295
Bantayehu, M., Alemayehu, W. M., Abera, M., & Bizuayehu, W. S. (2017). Postharvest losses assessment of tropical fruits in the market chain of North Western Ethiopia. Food Science and Quality Management, 66. https://www.iiste.org/Journals/index.php/FSQM/article/view/38249/39327
Benos, L., Tagarakis, A. C., Dolias, G., Berruto, R., Kateris, D., & Bochtis, D. (2021). Machine learning in agriculture: A comprehensive updated review. Sensors, 21(11), Article 3758. https://doi.org/10.3390/s21113758 DOI: https://doi.org/10.3390/s21113758
Bouguettaya, A., Zarzour, H., Kechida, A., & Taberkit, A. M. (2022). Deep learning techniques to classify agricultural crops through UAV imagery: A review. Neural Computing and Applications, 34(12), 9511–9536. https://doi.org/10.1007/s00521-022-07104-9 DOI: https://doi.org/10.1007/s00521-022-07104-9
Cadenas, J. M., Garrido, M. C., & Martínez-España, R. (2023). A methodology based on machine learning and soft computing to design more sustainable agriculture systems. Sensors, 23(6), Article 3038. https://doi.org/10.3390/s23063038 DOI: https://doi.org/10.3390/s23063038
Candelaria Martínez, B., Ruiz Rosado, O., Gallardo López, F., Pérez Hernández, P., Martínez Becerra, A., & Vargas Villamil, L. (2011). Aplicación de modelos de simulación en el estudio y planificación de la agricultura, una revisión. Tropical and Subtropical Agroecosystems, 14(3), 999–1010. https://www.scielo.org.mx/pdf/tsa/v14n3/v14n3a4.pdf
Caruso, A. G., Bertacca, S., Parrella, G., Rizzo, R., Davino, S., & Panno, S. (2022). Tomato brown rugose fruit virus: A pathogen that is changing the tomato production worldwide. Annals of Applied Biology, 181(3), 258–274. https://doi.org/10.1111/aab.12788 DOI: https://doi.org/10.1111/aab.12788
Centro Internacional de Agricultura Tropical. (2013). Agricultura colombiana: adaptación al cambio climático. CIAT Políticas en Síntesis No. 1. https://cgspace.cgiar.org/handle/10568/57475
De la Peña, N., & Granados, O. M. (2023). Artificial intelligence solutions to reduce information asymmetry for Colombian cocoa small-scale farmers. Information Processing in Agriculture. https://doi.org/10.1016/J.INPA.2023.03.001 DOI: https://doi.org/10.1016/j.inpa.2023.03.001
Delgado-Delgado, R., Valera-Calero, J. A., Gowie, A. E., Calvo-Moreno, S. O., Centenera-Centenera, M. B., & Gallego-Sendarrubias, G. M. (2021). Is any feature of the stabilometric evaluation clinically relevant in patients with temporomandibular disorders? A cross-sectional study. Applied Sciences, 11(10), Article 4473. https://doi.org/10.3390/app11104473 DOI: https://doi.org/10.3390/app11104473
Dercas, N., Dalezios, N. R., Stamatiadis, S., Evangelou, E., Glampedakis, A., Mantonanakis, G., & Tserlikakis, N. (2022). AquaCrop simulation of winter wheat under different N management practices. Hydrology, 9(4),1-20 Article 56. https://doi.org/10.3390/hydrology9040056 DOI: https://doi.org/10.3390/hydrology9040056
Ding, Z., & Xie, Q. (2023). Provably secure dynamic anonymous authentication protocol for wireless sensor networks in internet of things. Sustainability, 15(7), Article 5734. https://doi.org/10.3390/su15075734 DOI: https://doi.org/10.3390/su15075734
Disraelly, D. S., Walsh, T. J., & Curling, C. A. (2011). A new methodology for estimating contagious biological agent casualties as a function of time. Mathematical and Computer Modelling, 54(1-2), 649–658. https://doi.org/10.1016/j.mcm.2011.03.008 DOI: https://doi.org/10.1016/j.mcm.2011.03.008
Dokic, K., Blaskovic, L., & Mandusic, D. (2020). From machine learning to deep learning in agriculture – the quantitative review of trends. IOP Conference Series: Earth and Environmental Science, 614, Article 012138. https://doi.org/10.1088/1755-1315/614/1/012138 DOI: https://doi.org/10.1088/1755-1315/614/1/012138
Ebrahimi, M. A., Khoshtaghaza, M. H., Minaei, S., & Jamshidi, B. (2017). Vision-based pest detection based on SVM classification method. Computers and Electronics in Agriculture, 137, 52–58. https://doi.org/10.1016/j.compag.2017.03.016 DOI: https://doi.org/10.1016/j.compag.2017.03.016
FAO. (2021). World Food and Agriculture. Statistical Yearbook 2021. Food and Agriculture Organization of the United Nations. https://doi.org/10.4060/cb4477en DOI: https://doi.org/10.4060/cb4477en
Ferrández-Pastor, F. J., García-Chamizo, J. M., Nieto-Hidalgo, M., Mora-Pascual, J., & Mora-Martínez, J. (2016). Developing ubiquitous sensor network platform using Internet of Things: Application in precision agriculture. Sensors, 16(7), Article 1141. https://doi.org/10.3390/s16071141 DOI: https://doi.org/10.3390/s16071141
Frelat, R., Lopez-Ridaura, S., Giller, K. E., Herrero, M., Douxchamps, S., Djurfeldt, A. A., Erenstein, O., Henderson, B., Kassie, M., Paul, B. K., Rigolot, C., Ritzema, R. S., Rodriguez, D., van Asten, P. J. A., & Van Wijk, M. T. (2016). Drivers of household food availability in sub-Saharan Africa based on big data from small farms. Proceedings of the National Academy of Sciences of the United States of America, 113(2), 458–463. https://doi.org/10.1073/pnas.1518384112 DOI: https://doi.org/10.1073/pnas.1518384112
Giang, N. H., Wang, Y. R., Hieu, T. D., Ngu, N. H., & Dang, T. T. (2022). Estimating land-use change using machine learning: A case study on five central coastal provinces of Vietnam. Sustainability, 14(9), Article 5194. https://doi.org/10.3390/SU14095194 DOI: https://doi.org/10.3390/su14095194
Hemming, S., De Zwart, F., Elings, A., Righini, I., & Petropoulou, A. (2019). Remote control of greenhouse vegetable production with artificial intelligence – greenhouse climate, irrigation, and crop production. Sensors, 19(8), Article 1807. https://doi.org/10.3390/S19081807 DOI: https://doi.org/10.3390/s19081807
Hernández Leal, E. J. (2016). Aplicación de técnicas de análisis de datos y administración de Big Data ambientales [Master thesis, Universidad Nacional de Colombia, Medellín]. https://repositorio.unal.edu.co/handle/unal/57998
Instituto Interamericano de Cooperación para la Agricultura. (2016). El fenómeno de El Niño en la agricultura de las Américas. (Boletín Técnico 2016). https://repositorio.iica.int/handle/11324/3041
Jato-Espino, D., & Mayor-Vitoria, F. (2023). A statistical and machine learning methodology to model rural depopulation risk and explore its attenuation through agricultural land use management. Applied Geography, 152, Article 102870. https://doi.org/10.1016/J.APGEOG.2023.102870 DOI: https://doi.org/10.1016/j.apgeog.2023.102870
Johnson, M. D., Hsieh, W. W., Cannon, A. J., Davidson, A., & Bédard, F. (2016). Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods. Agricultural and Forest Meteorology, 218–219, 74–84. https://doi.org/10.1016/j.agrformet.2015.11.003 DOI: https://doi.org/10.1016/j.agrformet.2015.11.003
Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. https://doi.org/10.1016/j.compag.2017.09.037 DOI: https://doi.org/10.1016/j.compag.2017.09.037
Khan, F. A. (2018). A review on hydroponic greenhouse cultivation for sustainable agriculture. International Journal of Agriculture, Environment and Food Sciences, 2(2), 59–66. https://doi.org/10.31015/JAEFS.18010 DOI: https://doi.org/10.31015/jaefs.18010
Knott, M., Perez-Cruz, F., & Defraeye, T. (2023). Facilitated machine learning for image-based fruit quality assessment. Journal of Food Engineering, 345, Article 111401. https://doi.org/10.1016/J.JFOODENG.2022.111401 DOI: https://doi.org/10.1016/j.jfoodeng.2022.111401
Kusek, M. (2018, May 21-25). Internet of Things: Today and tomorrow. [Conference presentation]. 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO),0335–0338. https://doi.org/10.23919/MIPRO.2018.8400064 DOI: https://doi.org/10.23919/MIPRO.2018.8400064
Malaver, A., Motta, N., Corke, P., & González, F. (2015). Development and integration of a solar powered unmanned aerial vehicle and a wireless sensor network to monitor greenhouse gases. Sensors, 15(2), 4072–4096. https://doi.org/10.3390/s150204072 DOI: https://doi.org/10.3390/s150204072
Martínez-Álvarez, F., & Morales-Esteban, A. (2019). Big data and natural disasters: New approaches for spatial and temporal massive data analysis. Computers & Geosciences, 129, 38–39. https://doi.org/10.1016/J.CAGEO.2019.04.012 DOI: https://doi.org/10.1016/j.cageo.2019.04.012
Melo León, S. F., Riveros Salcedo, L. C., Romero Otálora, G., Álvarez, A. C., Diaz Giraldo, C., & Calderón Díaz, S. L. (2017). Efectos económicos de futuras sequías en Colombia: Estimación a partir del Fenómeno El Niño 2015. Archivos de Economía, 466, 1–34. https://colaboracion.dnp.gov.co/CDT/Estudios%20Econmicos/466.pdf
Meshram, V., Patil, K., Meshram, V., Hanchate, D., & Ramkteke, S. D. (2021). Machine learning in agriculture domain: A state-of-art survey. Artificial Intelligence in the Life Sciences, 1, Article 100010. https://doi.org/10.1016/J.AILSCI.2021.100010 DOI: https://doi.org/10.1016/j.ailsci.2021.100010
Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), 358–371. https://doi.org/10.1016/j.biosystemseng.2012.08.009 DOI: https://doi.org/10.1016/j.biosystemseng.2012.08.009
Murad, C. A., & Pearse, J. (2018). Landsat study of deforestation in the Amazon region of Colombia: Departments of Caquetá and Putumayo. Remote Sensing Applications: Society and Environment, 11, 161–171. https://doi.org/10.1016/j.rsase.2018.07.003 DOI: https://doi.org/10.1016/j.rsase.2018.07.003
Ojha, T., Misra, S., & Raghuwanshi, N. S. (2015). Wireless sensor networks for agriculture: The state of-the-art in practice and future challenges. Computers and Electronics in Agriculture, 118, 66–84. https://doi.org/10.1016/j.compag.2015.08.011 DOI: https://doi.org/10.1016/j.compag.2015.08.011
Pennisi, G., Orsini, F., Blasioli, S., Cellini, A., Crepaldi, A., Braschi, I., Spinelli, F., Nicola, S., Fernandez, J. A., Stanghellini, C., Gianquinto, G., & Marcelis, L. F. M. (2019). Resource use efficiency of indoor lettuce (Lactuca sativa L.) cultivation as affected by red:blue ratio provided by LED lighting. Scientific Reports, 9(1), Article 14127. https://doi.org/10.1038/s41598-019-50783-z DOI: https://doi.org/10.1038/s41598-019-50783-z
Piedad, E., Larada, J. I., Pojas, G. J., & Ferrer, L. V. V. (2018). Postharvest classification of banana (Musa acuminata) using tier-based machine learning. Postharvest Biology and Technology, 145, 93–100. https://doi.org/10.1016/J.POSTHARVBIO.2018.06.004 DOI: https://doi.org/10.1016/j.postharvbio.2018.06.004
Pineda, D., Pérez, J., Gaviria, D., Ospino-Villalba, K., & Camargo, O. (2022). MEDUSA: An open-source and webcam based multispectral imaging system. HardwareX, 11, Article e00282. https://doi.org/10.1016/j.ohx.2022.e00282 DOI: https://doi.org/10.1016/j.ohx.2022.e00282
Pineda, M. F., Tinoco, H. A., Lopez-Guzman, J., Perdomo-Hurtado, L., Cardona, C. I., Rincon-Jimenez, A., & Betancur-Herrera, N. (2022). Ripening stage classification of Coffea arabica L. var. Castillo using a machine learning approach with the electromechanical impedance measurements of a contact device. Materials Today: Proceedings, 62(P12), 6671–6678. https://doi.org/10.1016/j.matpr.2022.04.669 DOI: https://doi.org/10.1016/j.matpr.2022.04.669
Puntel, L. A., Bolfe, E. L., Melchiori, R. J. M., Ortega, R., Tiscornia, G., Roel, A., Scaramuzza, F., Best, S., Berger, A. G., Hansel, D. S. S., Palacios Durán, D., & Balboa, G. R. (2022). How digital is agriculture in a subset of countries from South America? Adoption and limitations. Crop & Pasture Science, 74(6), 555–572. https://doi.org/10.1071/CP21759 DOI: https://doi.org/10.1071/CP21759
Putra, P. A., & Yuliando, H. (2015). Soilless culture system to support water use efficiency and product quality: A review. Agriculture and Agricultural Science Procedia, 3, 283–288. https://doi.org/10.1016/J.AASPRO.2015.01.054 DOI: https://doi.org/10.1016/j.aaspro.2015.01.054
Rajak, P., Ganguly, A., Adhikary, S., & Bhattacharya, S. (2023). Internet of Things and smart sensors in agriculture: Scopes and challenges. Journal of Agriculture and Food Research, 14, Article 100776. https://doi.org/https://doi.org/10.1016/j.jafr.2023.100776 DOI: https://doi.org/10.1016/j.jafr.2023.100776
Ramírez Alberto, L., Cabrera Ardila, C. E., & Prieto Ortiz, F. A. (2023). A computer vision system for early detection of anthracnose in sugar mango (Mangifera indica) based on UV-A illumination. Information Processing in Agriculture, 10(2), 204–215. https://doi.org/10.1016/j.inpa.2022.02.001 DOI: https://doi.org/10.1016/j.inpa.2022.02.001
Rodríguez, J. P., Montoya-Munoz, A. I., Rodriguez-Pabon, C., Hoyos, J., & Corrales, J. C. (2021). IoT-Agro: A smart farming system to Colombian coffee farms. Computers and Electronics in Agriculture, 190, Article 106442. https://doi.org/10.1016/J.COMPAG.2021.106442 DOI: https://doi.org/10.1016/j.compag.2021.106442
Saha, A. K., Saha, J., Ray, R., Sircar, S., Dutta, S., Chattopadhyay, S. P., & Saha, H. N. (2018, June 8-10). IOT-based drone for improvement of crop quality in agricultural field [Conference presentation]. 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA. https://doi.org/10.1109/CCWC.2018.8301662 DOI: https://doi.org/10.1109/CCWC.2018.8301662
Sarkar, R. (2012). Decision support systems for agrotechnology transfer. Organic fertilisation, soil quality and human health, 9, 263–299. https://doi.org/10.1007/978-94-007-4113-3_10 DOI: https://doi.org/10.1007/978-94-007-4113-3_10
Shafi, U., Mumtaz, R., García-Nieto, J., Hassan, S. A., Zaidi, S. A. R., & Iqbal, N. (2019). Precision agriculture techniques and practices: From considerations to applications. Sensors, 19(17), Article 3796. https://doi.org/10.3390/s19173796 DOI: https://doi.org/10.3390/s19173796
Shafi, U., Mumtaz, R., Iqbal, N., Zaidi, S. M. H., Zaidi, S. A. R., Hussain, I., & Mahmood, Z. (2020). A multi-modal approach for crop health mapping using low altitude remote sensing, Internet of Things (IoT) and machine learning. IEEE Access, 8, 112708–112724. https://doi.org/10.1109/ACCESS.2020.3002948 DOI: https://doi.org/10.1109/ACCESS.2020.3002948
Singh, A., Vaidya, G., Jagota, V., Darko, D. A., Agarwal, R. K., Debnath, S., & Potrich, E. (2022). Recent advancement in postharvest loss mitigation and quality management of fruits and vegetables using machine learning frameworks. Journal of Food Quality, 2022, Article 6447282. https://doi.org/10.1155/2022/6447282 DOI: https://doi.org/10.1155/2022/6447282
Sousa, C., Fatoyinbo, L., Neigh, C., Boucka, F., Angoue, V., & Larsen, T. (2020). Cloud-computing and machine learning in support of country-level land cover and ecosystem extent mapping in Liberia and Gabon. PLoS ONE, 15(1), Article e0227438. https://doi.org/10.1371/JOURNAL.PONE.0227438 DOI: https://doi.org/10.1371/journal.pone.0227438
Strong, R., Wynn, J. T., Lindner, J. R., & Palmer, K. (2022). Evaluating Brazilian agriculturalists’ IoT smart agriculture adoption barriers: Understanding stakeholder salience prior to launching an innovation. Sensors, 22(18), Article 6833. https://doi.org/10.3390/s22186833 DOI: https://doi.org/10.3390/s22186833
Suarez-Peña, J. A., Lobaton-García, H. F., Rodríguez-Molano, J. I., & Rodríguez-Vázquez, W. C. (2020). Machine learning for cup coffee quality prediction from green and roasted coffee beans features. In J. C. Figueroa-García, F. S. Garay-Rairán, G. J. Hernández-Pérez, & Y. Díaz-Gutierrez (Eds.), Applied computer sciences in engineering. WEA 2020. Communications in Computer and Information Science vol. 1274 (pp. 48–59). Springer, Cham. https://doi.org/10.1007/978-3-030-61834-6_5 DOI: https://doi.org/10.1007/978-3-030-61834-6_5
Sun, G., Jia, X., & Geng, T. (2018). Plant diseases recognition based on image processing technology. Journal of Electrical and Computer Engineering, 2018, Article 6070129. https://doi.org/10.1155/2018/6070129 DOI: https://doi.org/10.1155/2018/6070129
Tan, w, H., Ibrahim, H., & Chan, D. J .C. (2021). Estimation of mass, chlorophylls, and anthocyanins of Spirodela polyrhiza with smartphone acquired images. Computers and Electronics in Agriculture, 190, Article 106449. https://doi.org/10.1016/j.compag.2021.106449 DOI: https://doi.org/10.1016/j.compag.2021.106449
Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of Things in agriculture, recent advances and future challenges. Biosystems Engineering, 164, 31–48. https://doi.org/10.1016/j.biosystemseng.2017.09.007 DOI: https://doi.org/10.1016/j.biosystemseng.2017.09.007
Xie, B., Xu, J., Jung, J., Yun, S. H., Zeng, E., Brooks, E. M., Dolk, M., & Narasimhalu, L. (2020). Machine learning on satellite radar images to estimate damages after natural disasters. GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, 461–464. https://doi.org/10.1145/3397536.3422349 DOI: https://doi.org/10.1145/3397536.3422349
Xu, M., David, J. M., & Kim, S. H. (2018). The fourth industrial revolution: Opportunities and challenges. International Journal of Financial Research, 9(2), 90–95. https://doi.org/10.5430/ijfr.v9n2p90 DOI: https://doi.org/10.5430/ijfr.v9n2p90
Yousaf, A., Kayvanfar, V., Mazzoni, A., & Elomri, A. (2023). Artificial intelligence-based decision support systems in smart agriculture: Bibliometric analysis for operational insights and future directions. Frontiers in Sustainable Food Systems, 6, Article 1053921. https://doi.org/10.3389/fsufs.2022.1053921 DOI: https://doi.org/10.3389/fsufs.2022.1053921
Zhang, B., & Meng, L. (2021). Energy efficiency analysis of wireless sensor networks in precision agriculture economy. Scientific Programming, 2021, Article 8346708. https://doi.org/10.1155/2021/8346708 DOI: https://doi.org/10.1155/2021/8346708
Zhang, Z. K., Cho, M. C. Y., Wang, C. W., Hsu, C. W., Chen, C. K., & Shieh, S. (2014). IoT security: Ongoing challenges and research opportunities. 2014 IEEE 7th International Conference on Service Oriented Computing and Applications, SOCA 2014, 230–234. https://doi.org/10.1109/SOCA.2014.58 DOI: https://doi.org/10.1109/SOCA.2014.58
Ziesche, S., Agarwal, S., Nagaraju, U., Prestes, E., & Singha, N. (2023). Role of artificial intelligence in advancing sustainable development goals in the agriculture sector. In F. Mazzi, & L. Floridi (Eds.), The ethics of artificial intelligence for the sustainable development goals (pp. 379–397). Philosophical Studies Series, 152. Springer, Cham. https://doi.org/10.1007/978-3-031-21147-8_21 DOI: https://doi.org/10.1007/978-3-031-21147-8_21
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1. Nur Muttaqien Zuhri, Nurcahyono Nurcahyono, Nurul Puspita. (2025). THE SUSTAINABLE AGRICULTURE SUPPLY CHAINS: A BIBLIOMETRIC ANALYSIS APPROACH. Agrisocionomics: Jurnal Sosial Ekonomi Pertanian, 9(1), p.250. https://doi.org/10.14710/agrisocionomics.v9i1.22691.
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