Agriculture 5.0 and Explainable AI for Smart Agriculture: A Scoping Review

Siti Fatimah Abdul Razak, Sumendra Yogarayan, Md Shohel Sayeed, Muhammad Izzat Faiz Mohd Derafi


The visionary paradigm of Agriculture 5.0 integrates Industry 4.0 principles into agricultural practices. Our scoping review explores the landscape of Agriculture 5.0, emphasizing the pivotal role of Explainable AI (XAI) in shaping this domain. Guided by the Preferred Reporting Items for Systematic Review and Meta-Analysis Scoping Review, we rigorously analyzed 84 articles published from 2018 to September 2023. Our findings highlight XAI’s potential within Agriculture 5.0, recognizing its influence on intelligent farming. We propose a conceptual framework for integrating XAI, emphasizing its impact on model transparency and user trust. Despite transformative applications, existing literature often lacks XAI discussions. Our objective is to bridge this gap and provide a reference for academics, practitioners, policymakers, and educators in the field of smart agriculture that is both environmentally friendly and technologically advanced.


Doi: 10.28991/ESJ-2024-08-02-024

Full Text: PDF


Explainable AI (xAI); Agriculture 5.0; Data-Driven Agriculture; Smart Agriculture.


Mazzetto, F., Gallo, R., & Sacco, P. (2020). Reflections and methodological proposals to treat the concept of “information precision” in smart agriculture practices. Sensors, 20(10), 2847. doi:10.3390/s20102847.

Goel, R. K., Yadav, C. S., Vishnoi, S., & Rastogi, R. (2021). Smart agriculture – Urgent need of the day in developing countries. Sustainable Computing: Informatics and Systems, 30. doi:10.1016/j.suscom.2021.100512.

Khanna, A., & Kaur, S. (2019). Evolution of Internet of Things (IoT) and its significant impact in the field of Precision Agriculture. Computers and Electronics in Agriculture, 157, 218–231. doi:10.1016/j.compag.2018.12.039.

Fraser, E. D. G., & Campbell, M. (2019). Agriculture 5.0: Reconciling Production with Planetary Health. One Earth, 1(3), 278–280. doi:10.1016/j.oneear.2019.10.022.

Saiz-Rubio, V., & Rovira-Más, F. (2020). From smart farming towards agriculture 5.0: A review on crop data management. Agronomy, 10(2), 207. doi:10.3390/agronomy10020207.

Mesías-Ruiz, G. A., Pérez-Ortiz, M., Dorado, J., de Castro, A. I., & Peña, J. M. (2023). Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review. Frontiers in Plant Science, 14. doi:10.3389/fpls.2023.1143326.

Kovács, I., & Husti, I. (2018). The role of digitalization in the agricultural 4.0 – how to connect the industry 4.0 to agriculture? Hungarian Agricultural Engineering, 33(33), 38–42. doi:10.17676/hae.2018.33.38.

Jha, K., Doshi, A., Patel, P., & Shah, M. (2019). A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2, 1–12. doi:10.1016/j.aiia.2019.05.004.

Barkunan, S. R., Bhanumathi, V., & Sethuram, J. (2019). Smart sensor for automatic drip irrigation system for paddy cultivation. Computers and Electrical Engineering, 73, 180–193. doi:10.1016/j.compeleceng.2018.11.013.

Mottaleb, K. A. (2018). Perception and adoption of a new agricultural technology: Evidence from a developing country. Technology in Society, 55, 126–135. doi:10.1016/j.techsoc.2018.07.007.

Sharma, R., Kamble, S. S., Gunasekaran, A., Kumar, V., & Kumar, A. (2020). A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Computers and Operations Research, 119. doi:10.1016/j.cor.2020.104926.

Kenny, E. M., Ruelle, E., Geoghegan, A., Shalloo, L., O’Leary, M., O’Donovan, M., Temraz, M., & Keane, M. T. (2021). Bayesian Case-Exclusion and Explainable AI (XAI) for Sustainable Farming. The 29th International Joint Conference on Artificial Intelligence - 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI-20), 7-17 January, 2021, Yokohama, Japan.

Schwab, K. (2017). The Global Competitiveness Report 2017-2018. World Economic Forum, Cologny, Switzerland.

Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022). Explainable artificial intelligence: a comprehensive review. Artificial Intelligence Review, 55(5), 3503–3568. doi:10.1007/s10462-021-10088-y.

Islam, S. R., Eberle, W., Ghafoor, S. K., & Ahmed, M. (2021). Explainable artificial intelligence approaches: A survey. arXiv preprint arXiv:2101.09429. doi:10.48550/arXiv.2101.09429.

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. doi:10.1016/j.inffus.2019.12.012.

Fernandez, A., Herrera, F., Cordon, O., Jose Del Jesus, M., & Marcelloni, F. (2019). Evolutionary fuzzy systems for explainable artificial intelligence: Why, when, what for, and where to? IEEE Computational Intelligence Magazine, 14(1), 69–81. doi:10.1109/MCI.2018.2881645.

Samek, W., Wiegand, T., & Müller, K. R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296. doi:10.48550/arXiv.1708.08296.

Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change, 162. doi:10.1016/j.techfore.2020.120392.

Montavon, G., Samek, W., & Müller, K. R. (2018). Methods for interpreting and understanding deep neural networks. Digital Signal Processing: A Review Journal, 73, 1–15. doi:10.1016/j.dsp.2017.10.011.

Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5). doi:10.1145/3236009.

Adadi, A., & Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138–52160. doi:10.1109/ACCESS.2018.2870052.

Ehsan, U., Wintersberger, P., Liao, Q. V., Mara, M., Streit, M., Wachter, S., Riener, A., & Riedl, M. O. (2021). Operationalizing Human-Centered Perspectives in Explainable AI. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. doi:10.1145/3411763.3441342.

Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. doi:10.1038/s42256-019-0048-x.

Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, … Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473. doi:10.7326/M18-0850.

Otieno, M. (2023). An extensive survey of smart agriculture technologies: Current security posture. World Journal of Advanced Research and Reviews, 18(3), 1207–1231. doi:10.30574/wjarr.2023.18.3.1241.

Race, D., Gentle, P., & Mathew, S. (2023). Living on the margins: Climate change impacts and adaptation by remote communities living in the Pacific Islands, the Himalaya and desert Australia. Climate Risk Management, 40. doi:10.1016/j.crm.2023.100503.

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. doi:10.1016/j.compag.2021.106442.

Bwambale, E., Abagale, F. K., & Anornu, G. K. (2022). Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review. Agricultural Water Management, 260. doi:10.1016/j.agwat.2021.107324.

Manlove, J. L., Shew, A. M., & Obembe, O. S. (2021). Arkansas producers value upload speed more than download speed for precision agriculture applications. Computers and Electronics in Agriculture, 190. doi:10.1016/j.compag.2021.106432.

Carrer, M. J., Filho, H. M. de S., Vinholis, M. de M. B., & Mozambani, C. I. (2022). Precision agriculture adoption and technical efficiency: An analysis of sugarcane farms in Brazil. Technological Forecasting and Social Change, 177. doi:10.1016/j.techfore.2022.121510.

Thakur, N., Nigam, M., Mann, N. A., Gupta, S., Hussain, C. M., Shukla, S. K., Shah, A. A., Casini, R., Elansary, H. O., & Khan, S. A. (2023). Host-mediated gene engineering and microbiome-based technology optimization for sustainable agriculture and environment. Functional and Integrative Genomics, 23(1). doi:10.1007/s10142-023-00982-9.

Said Mohamed, E., Belal, A. A., Kotb Abd-Elmabod, S., El-Shirbeny, M. A., Gad, A., & Zahran, M. B. (2021). Smart farming for improving agricultural management. Egyptian Journal of Remote Sensing and Space Science, 24(3), 971–981. doi:10.1016/j.ejrs.2021.08.007.

Adamides, G., Kalatzis, N., Stylianou, A., Marianos, N., Chatzipapadopoulos, F., Giannakopoulou, M., Papadavid, G., Vassiliou, V., & Neocleous, D. (2020). Smart farming techniques for climate change adaptation in Cyprus. Atmosphere, 11(6). doi:10.3390/ATMOS11060557.

Shrivastava, A., Nayak, C. K., Dilip, R., Samal, S. R., Rout, S., & Ashfaque, S. M. (2023). Automatic robotic system design and development for vertical hydroponic farming using IoT and big data analysis. Materials Today: Proceedings, 80, 3546–3553. doi:10.1016/j.matpr.2021.07.294.

Cordeiro, M., Markert, C., Araújo, S. S., Campos, N. G. S., Gondim, R. S., da Silva, T. L. C., & da Rocha, A. R. (2022). Towards Smart Farming: Fog-enabled intelligent irrigation system using deep neural networks. Future Generation Computer Systems, 129, 115–124. doi:10.1016/j.future.2021.11.013.

Fraser, A. (2022). ‘You can’t eat data’?: Moving beyond the misconfigured innovations of smart farming. Journal of Rural Studies, 91, 200–207. doi:10.1016/j.jrurstud.2021.06.010.

Nyangaresi, V. O., El-Omari, N. K. T., & Nyakina, J. N. (2022). Efficient Feature Selection and ML Algorithm for Accurate Diagnostics. Journal of Computer Science Research, 4(1), 10–19. doi:10.30564/jcsr.v4i1.3852.

Zambon, I., Cecchini, M., Egidi, G., Saporito, M. G., & Colantoni, A. (2019). Revolution 4.0: Industry vs. agriculture in a future development for SMEs. Processes, 7(1). doi:10.3390/pr7010036.

Polymeni, S., Plastras, S., Skoutas, D. N., Kormentzas, G., & Skianis, C. (2023). The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review. Electronics, 12(12), 2651. doi:10.3390/electronics12122651.

Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2022). Enhancing smart farming through the applications of Agriculture 4.0 technologies. International Journal of Intelligent Networks, 3, 150–164. doi:10.1016/j.ijin.2022.09.004.

Naikwade, R. R., Patle, B. K., Joshi, V. S., Pagar, N. D., & Hirwe, S. B. (2021). Agriculture 5.0: Future of Smart Farming. National Conference on Innovative Global Technology Trends in Art, Design, Technology, Management, Vedic Science, Education and Architecture, Film & Media, 1-6.

Mesías, F. J., Martín, A., & Hernández, A. (2021). Consumers’ growing appetite for natural foods: Perceptions towards the use of natural preservatives in fresh fruit. Food Research International, 150. doi:10.1016/j.foodres.2021.110749.

Zhang, A., Mankad, A., & Ariyawardana, A. (2020). Establishing confidence in food safety: is traceability a solution in consumers’ eyes? Journal Fur Verbraucherschutz Und Lebensmittelsicherheit, 15(2), 99–107. doi:10.1007/s00003-020-01277-y.

Murugesan, R., Sudarsanam, S. K., Malathi, G., Vijayakumar, V., Neelanarayanan, V., Venugopal, R., Rekha, D., Saha, S., Bajaj, R., Miral, A., & Malolan, V. (2019). Artificial intelligence and agriculture 5. 0. International Journal of Recent Technology and Engineering, 8(2), 1870–1877. doi:10.35940/ijrte.B1510.078219.

Ragazou, K., Garefalakis, A., Zafeiriou, E., & Passas, I. (2022). Agriculture 5.0: A New Strategic Management Mode for a Cut Cost and an Energy Efficient Agriculture Sector. Energies, 15(9). doi:10.3390/en15093113.

Berawi, M. A. (2019). Managing Nature 5.0 in industrial revolution 4.0 and society 5.0 era. International Journal of Technology, 10(2), 222–225. doi:10.14716/ijtech.v10i2.3084.

Kwaghtyo, D. K., & Eke, C. I. (2023). Smart farming prediction models for precision agriculture: a comprehensive survey. Artificial Intelligence Review, 56(6), 5729–5772. doi:10.1007/s10462-022-10266-6.

Van Teeffelen, D. (2023). Plant disease detection with machine and deep learning: A systematic literature review & experimental study. Master Thesis, Wageningen University and Research, Wageningen, Netherlands.

Gardezi, M., Joshi, B., Rizzo, D. M., Ryan, M., Prutzer, E., Brugler, S., & Dadkhah, A. (2023). Artificial intelligence in farming: Challenges and opportunities for building trust. Agronomy Journal, 1-12. doi:10.1002/agj2.21353.

Vikranth, K., & Krishna Prasad, K. (2021). An Implementation of IoT and Data Analytics in Smart Agricultural System – A Systematic Literature Review. International Journal of Management, Technology, and Social Sciences, 41–70. doi:10.47992/ijmts.2581.6012.0129.

Boursianis, A. D., Papadopoulou, M. S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G., Karagiannidis, G., Wan, S., & Goudos, S. K. (2022). Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review. Internet of Things, 18, 100187. doi:10.1016/j.iot.2020.100187.

Dara, R., Hazrati Fard, S. M., & Kaur, J. (2022). Recommendations for ethical and responsible use of artificial intelligence in digital agriculture. Frontiers in Artificial Intelligence, 5. doi:10.3389/frai.2022.884192.

Bacco, M., Barsocchi, P., Ferro, E., Gotta, A., & Ruggeri, M. (2019). The Digitisation of Agriculture: a Survey of Research Activities on Smart Farming. Array, 3–4, 100009. doi:10.1016/j.array.2019.100009.

Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS - Wageningen Journal of Life Sciences, 1-16. doi:10.1016/j.njas.2019.100315.

Javed, A. R., Ahmed, W., Pandya, S., Maddikunta, P. K. R., Alazab, M., & Gadekallu, T. R. (2023). A Survey of Explainable Artificial Intelligence for Smart Cities. Electronics (Switzerland), 12(4), 1020. doi:10.3390/electronics12041020.

Yenduri, G., & Gadekallu, T. R. (2023). XAI for Maintainability Prediction of Software-Defined Networks. Proceedings of the 24th International Conference on Distributed Computing and Networking, 402-406. doi:10.1145/3571306.3571443.

Du, M., Liu, N., & Hu, X. (2020). Techniques for interpretable machine learning. Communications of the ACM, 63(1), 68–77. doi:10.1145/3359786.

Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021). Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: A systematic review. Applied Sciences (Switzerland), 11(11), 5088. doi:10.3390/app11115088.

Slack, D., Hilgard, S., Jia, E., Singh, S., & Lakkaraju, H. (2020). Fooling LIME and SHAP. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 180-186. doi:10.1145/3375627.3375830.

Nauta, M., Trienes, J., Pathak, S., Nguyen, E., Peters, M., Schmitt, Y., Schlötterer, J., Van Keulen, M., & Seifert, C. (2023). From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI. ACM Computing Surveys, 55(13), 1-42. doi:10.1145/3583558.

Huuskonen, J., & Oksanen, T. (2018). Soil sampling with drones and augmented reality in precision agriculture. Computers and Electronics in Agriculture, 154, 25–35. doi:10.1016/j.compag.2018.08.039.

Atitallah, S. Ben, Driss, M., Boulila, W., & Ghezala, H. Ben. (2020). Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions. Computer Science Review, 38. doi:10.1016/j.cosrev.2020.100303.

van Etten, J., de Sousa, K., Cairns, J. E., Dell’Acqua, M., Fadda, C., Guereña, D., Heerwaarden, J. van, Assefa, T., Manners, R., Müller, A., Enrico Pè, M., Polar, V., Ramirez-Villegas, J., Øivind Solberg, S., Teeken, B., & Tufan, H. A. (2023). Data-driven approaches can harness crop diversity to address heterogeneous needs for breeding products. Proceedings of the National Academy of Sciences, 120(14), 1-10. doi:10.1073/pnas.2205771120.

Paul, K., Chatterjee, S. S., Pai, P., Varshney, A., Juikar, S., Prasad, V., Bhadra, B., & Dasgupta, S. (2022). Viable smart sensors and their application in data driven agriculture. Computers and Electronics in Agriculture, 198. doi:10.1016/j.compag.2022.107096.

Ayoub Shaikh, T., Rasool, T., & Rasheed Lone, F. (2022). Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture, 198. doi:10.1016/j.compag.2022.107119.

Nasirahmadi, A., & Hensel, O. (2022). Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm. Sensors, 22(2). doi:10.3390/s22020498.

Chhetri, T. R., Hohenegger, A., Fensel, A., Kasali, M. A., & Adekunle, A. A. (2023). Towards improving prediction accuracy and user-level explainability using deep learning and knowledge graphs: A study on cassava disease. Expert Systems with Applications, 233. doi:10.1016/j.eswa.2023.120955.

Hu, T., Zhang, X., Bohrer, G., Liu, Y., Zhou, Y., Martin, J., Li, Y., & Zhao, K. (2023). Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield. Agricultural and Forest Meteorology, 336. doi:10.1016/j.agrformet.2023.109458.

Bandi, R., Swamy, S., & Arvind, C. S. (2023). Leaf disease severity classification with explainable artificial intelligence using transformer networks. International Journal of Advanced Technology and Engineering Exploration, 10(100), 278–302. doi:10.19101/IJATEE.2022.10100136.

Chandra, H., Pawar, P. M., Elakkiya, R., Tamizharasan, P. S., Muthalagu, R., & Panthakkan, A. (2023). Explainable AI for Soil Fertility Prediction. IEEE Access, 11, 97866–97878. doi:10.1109/ACCESS.2023.3311827.

Sahidullah, M., Nayan, N. M., Morshed, M. S., Hossain, M. M., & Islam, M. U. (2023). Date Fruit Classification with Machine Learning and Explainable Artificial Intelligence. International Journal of Computer Applications, 184(50), 1–5. doi:10.5120/ijca2023922617.

Celik, M. F., Isik, M. S., Taskin, G., Erten, E., & Camps-Valls, G. (2023). Explainable Artificial Intelligence for Cotton Yield Prediction With Multisource Data. IEEE Geoscience and Remote Sensing Letters, 20. doi:10.1109/LGRS.2023.3303643.

Bhat, S. A., Hussain, I., & Huang, N. F. (2023). Soil suitability classification for crop selection in precision agriculture using GBRT-based hybrid DNN surrogate models. Ecological Informatics, 75. doi:10.1016/j.ecoinf.2023.102109.

Ryo, M. (2022). Explainable artificial intelligence and interpretable machine learning for agricultural data analysis. Artificial Intelligence in Agriculture, 6, 257–265. doi:10.1016/j.aiia.2022.11.003.

Kawakura, S., Hirafuji, M., Ninomiya, S., & Shibasaki, R. (2022). Adaptations of Explainable Artificial Intelligence (XAI) to Agricultural Data Models with ELI5, PDPbox, and Skater using Diverse Agricultural Worker Data. European Journal of Artificial Intelligence and Machine Learning, 1(3), 27–34. doi:10.24018/ejai.2022.1.3.14.

Cartolano, A., Cuzzocrea, A., Pilato, G., & Grasso, G. M. (2022). Explainable AI at Work! What Can It Do for Smart Agriculture? 2022 IEEE Eighth International Conference on Multimedia Big Data (BigMM). doi:10.1109/bigmm55396.2022.00020.

Feldkamp, N., Genath, J., & Strassburger, S. (2022). Explainable AI For Data Farming Output Analysis: A Use Case for Knowledge Generation Through Black-Box Classifiers. 2022 Winter Simulation Conference (WSC), Singapore. doi:10.1109/wsc57314.2022.10015304.

Mehedi, M. H. K., Hosain, A. K. M. S., Ahmed, S., Promita, S. T., Muna, R. K., Hasan, M., & Reza, M. T. (2022). Plant Leaf Disease Detection using Transfer Learning and Explainable AI. 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, Canada. doi:10.1109/iemcon56893.2022.9946513.

Kawakura, S., Hirafuji, M., Ninomiya, S., & Shibasaki, R. (2022). Analyses of Diverse Agricultural Worker Data with Explainable Artificial Intelligence: XAI based on SHAP, LIME, and LightGBM. European Journal of Agriculture and Food Sciences, 4(6), 11–19. doi:10.24018/ejfood.2022.4.6.348.

Delaney, E. (2022). Case-based explanation for black-box time series and image models with applications in smart agriculture. 30th International Conference on Case-Based Reasoning, 11-12 September, 2022, Nancy, France.

Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2021). Explainable AI: A review of machine learning interpretability methods. Entropy, 23(1), 1–45. doi:10.3390/e23010018.

Zhang, Q., & Zhu, S. (2018). Visual interpretability for deep learning: a survey. Frontiers of Information Technology & Electronic Engineering, 19(1), 27–39. doi:10.1631/fitee.1700808.

Taj, I., & Zaman, N. (2022). Towards Industrial Revolution 5.0 and Explainable Artificial Intelligence: Challenges and Opportunities. International Journal of Computing and Digital Systems, 12(1), 285–310. doi:10.12785/ijcds/120124.

Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and Abstraction in Sociotechnical Systems. Proceedings of the Conference on Fairness, Accountability, and Transparency, 56-68. doi:10.1145/3287560.3287598.

Gonçalves, A. R., Pinto, D. C., Rita, P., & Pires, T. (2023). Artificial Intelligence and Its Ethical Implications for Marketing. Emerging Science Journal, 7(2), 313-327. doi:10.28991/ESJ-2023-07-02-01.

Saeed, W., & Omlin, C. (2023). Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities. Knowledge-Based Systems, 263. doi:10.1016/j.knosys.2023.110273.

Velten, S., Jager, N. W., & Newig, J. (2021). Success of collaboration for sustainable agriculture: a case study meta-analysis. Environment, Development and Sustainability, 23(10), 14619–14641. doi:10.1007/s10668-021-01261-y.

Maryono, M., Killoes, A. M., Adhikari, R., & Abdul Aziz, A. (2024). Agriculture development through multi-stakeholder partnerships in developing countries: A systematic literature review. Agricultural Systems, 213. doi:10.1016/j.agsy.2023.103792.

Hermans, F., Sartas, M., Van Schagen, B., Van Asten, P., & Schut, M. (2017). Social network analysis of multi-stakeholder platforms in agricultural research for development: Opportunities and constraints for innovation and scaling. PLoS ONE, 12(2), 1-21. doi:10.1371/journal.pone.0169634.

de Bruijn, H., Warnier, M., & Janssen, M. (2022). The perils and pitfalls of explainable AI: Strategies for explaining algorithmic decision-making. Government Information Quarterly, 39(2), 101666. doi:10.1016/j.giq.2021.101666.

Gardezi, M., Adereti, D. T., Stock, R., & Ogunyiola, A. (2022). In pursuit of responsible innovation for precision agriculture technologies. Journal of Responsible Innovation, 9(2), 224–247. doi:10.1080/23299460.2022.2071668.

Rose, D. C., & Chilvers, J. (2018). Agriculture 4.0: Broadening Responsible Innovation in an Era of Smart Farming. Frontiers in Sustainable Food Systems, 2. doi:10.3389/fsufs.2018.00087.

Du, Y., Antoniadi, A. M., McNestry, C., McAuliffe, F. M., & Mooney, C. (2022). The Role of XAI in Advice-Taking from a Clinical Decision Support System: A Comparative User Study of Feature Contribution-Based and Example-Based Explanations. Applied Sciences, 12(20), 10323. doi:10.3390/app122010323.

Jamil, H., Umer, T., Ceken, C., & Al-Turjman, F. (2021). Decision Based Model for Real-Time IoT Analysis Using Big Data and Machine Learning. Wireless Personal Communications, 121(4), 2947–2959. doi:10.1007/s11277-021-08857-7.

Weber, L., Lapuschkin, S., Binder, A., & Samek, W. (2023). Beyond explaining: Opportunities and challenges of XAI-based model improvement. Information Fusion, 92, 154–176. doi:10.1016/j.inffus.2022.11.013.

Full Text: PDF

DOI: 10.28991/ESJ-2024-08-02-024


  • There are currently no refbacks.

Copyright (c) 2024 Siti Fatimah Abdul Razak