A Human-Centered Machine Learning Dashboard for Real-Time Smart Farming Decision Support
DOI:
https://doi.org/10.68012/shem.v2i2.302Keywords:
Artificial Intelligence, Human-Centered Design, Internet of Things (IoT), Machine Learning, Precision AgricultureAbstract
The integration of machine learning (ML) into precision agriculture has advanced predictive accuracy for tasks such as crop monitoring, irrigation scheduling, and yield estimation. However, many existing IoT-based smart farming systems remain technically driven, offering limited attention to how farmers and field operators actually interact with and interpret model outputs. This gap often results in low adoption rates, as complex predictions fail to translate into actionable, trustworthy decisions for end users. This study proposes a human-centered machine learning dashboard designed to bridge predictive modeling with real-time, farmer-friendly decision support. The system integrates IoT sensor data streams with ML-based prediction modules, presented through an interactive, usability-tested interface that emphasizes clarity, accessibility, and interpretability over raw technical complexity. A working prototype was developed and evaluated using both quantitative usability metrics (e.g., task completion time, System Usability Scale scores) and qualitative feedback from target users to assess the dashboard's effectiveness in supporting field-level decisions. The proposed system architecture, dashboard interface, and underlying ML pipeline are implemented as open and reproducible source code, enabling replication and adaptation across similar smart farming contexts. Results indicate that the human-centered design significantly improves users' comprehension of model outputs and their confidence in acting on system recommendations, without compromising predictive performance. This research contributes a practical framework for designing machine intelligence systems that prioritize end-user experience alongside technical accuracy, offering implications for the broader development of human-centered AI in agricultural and IoT-driven decision support environments.
Downloads
References
[1] S. Brugler, M. Gardezi, A. Dadkhah, D. M. Rizzo, A. Zia, and S. A. Clay, “Improving decision support systems with machine learning: Identifying barriers to adoption,” Agronomy Journal, vol. 116, no. 3, pp. 1229–1236, 2024.
[2] S.-C. Chen, R. S. Pamungkas, and D. Schmidt, “The role of machine learning in improving robotic perception and decision making,” International Transactions on Artificial Intelligence, vol. 3, no. 1, pp. 32–43, 2024.
[3] N. K. Khilji, M. Safaei, S. Alamassi, N. U. Q. Mamani, A. Al-Shaybani, and I. Israilova, “Next-generation decision support systems: Integrating artificial intelligence, machine learning, big data analytics, and intelligent information systems,” Computer and Decision Making: An International Journal, vol. 3, no. 1S, pp. 275–286, 2026.
[4] M. Hatta, W. N. Wahid, F. Yusuf, F. Hidayat, N. A. Santoso, and Q. Aini, “Enhancing predictive models in system development using machine learning algorithms,” International Journal of Cyber and IT Service Management, vol. 4, no. 2, pp. 80–87, 2024.
[5] S. Wijono, U. Rahardja, H. D. Purnomo, N. Lutfiani, and N. A. Yusuf, “Leveraging machine learning models to enhance startup collaboration and drive technopreneurship,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 3, pp. 432–442, 2024.
[6] R. Royani, S. D. Maulina, S. Sugiyono, R. W. Anugrah, and B. Callula, “Recent developments in healthcare through machine learning and artificial intelligence,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 6, no. 1, pp. 86–94, 2024.
[7] N. P. L. Santoso, B. Rawat, S. R. Ratri, D. Danang, D. F. C. Kumoro, R. Supriati, and E. A. Natalia, “Transformation of indonesian language in social media using ai expert systems and machine learning,” International Transactions on Artificial Intelligence, vol. 3, no. 2, pp. 130–139, 2025.
[8] M. Hardini, R. A. Sunarjo, M. Asfi, M. H. R. Chakim, and Y. P. A. Sanjaya, “Predicting air quality index using ensemble machine learning,” ADI Journal on Recent Innovation, vol. 5, no. 1Sp, pp. 78–86, 2023.
[9] D. R. Saputra, H. Nugroho, D. Julianingsih, and Z. Queen, “Understanding air pollution through machine learning: Predictive analytics for urban management,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 6, no. 1, pp. 75–85, 2024.
[10] U. Rahardja, Q. Aini, D. Manongga, I. Sembiring, and Y. P. A. Sanjaya, “Enhancing machine learning with low-cost p m2. 5 air quality sensor calibration using image processing,” APTISI Transactions on Management, vol. 7, no. 3, pp. 201–209, 2023.
[11] U. Rahardja, P. A. Sunarya, Q. Aini, S. Millah, and S. Maulana, “Technopreneurship in healthcare: Evaluating user satisfaction and trust in ai-driven safe entry stations,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 3, pp. 404–417, 2024.
[12] A. Wilson and M. R. Anwar, “The future of adaptive machine learning algorithms in high-dimensional data processing,” International Transactions on Artificial Intelligence, vol. 3, no. 1, pp. 97–107, 2024.
[13] D. Robert, F. P. Oganda, A. Sutarman, W. Hidayat, and A. Fitriani, “Machine learning techniques for predicting the success of ai-enabled startups in the digital economy,” CORISINTA, vol. 1, no. 1, pp. 61– 69, 2024.
[14] B. Rawat, J. Nathalie, D. Manongga, G. Fransiso, F. P. O. Moana, and P. A. Sunarya, “Machine learningbased risk assessment and default prediction in p2p lending platforms,” Smart Human-centered Emerging Research in Machine Intelligence, vol. 2, no. 1, pp. 48–59, 2026.
[15] H. Fakhrurroja, S. Suakanto, H. Bangkit et al., “Design of an interactive iot dashboard for intelligent cattleya orchid cultivation using the design thinking method,” in 2026 2nd International Conference on Smart Mechatronics (ICSMech). IEEE, 2026, pp. 1–6.
[16] V. Meilinda, L. W. Ming, M. Muhtarom, J. Zanubiya, M. R. Kusuma, and R. Yaputra, “Artificial intelligence and iot integration for intelligent decision-making systems,” Smart Human-centered Emerging Research in Machine Intelligence, vol. 2, no. 1, pp. 1–13, 2026.
[17] E. Erika, B. S. Riza, S. Maulana, and S. Rahagi, “Mapping the nexus of strategic management and sustainable development goals 12 with a focus on innovation: A bibliometric analysis,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 7, no. 1, pp. 71–84, 2025.
[18] P. Prabhakar, “A human-centric technological revolution in agriculture-industry 5.0 for sustainable agripreneurship,” in Industry 5.0’s Impact on Economic Innovation. IGI Global Scientific Publishing, 2026, pp. 71–98.
[19] G. Contini, F. Grandi, and M. Peruzzini, “Human-centric green design for automatic production lines: Using virtual and augmented reality to integrate industrial data and promote sustainability,” Journal of Industrial Information Integration, vol. 44, p. 100801, 2025.
[20] J. G. Alegia and P. D. Cerna, “From prediction to decision: An explainable machine learning–driven framework for precision sugarcane agriculture,” in Proceedings of the 2026 International Conference on AI Decision-Making and Management, 2026, pp. 106–114.
[21] N. Tachaboon, T. Piyatamrong, C. Charoenwong, and N. Taneepanichskul, “Forecast-based decision support system design for small-scale agriculture: A dmadv case study from northern thailand,” in 2025 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD). IEEE, 2025, pp. 1–6.
[22] M. Kannan and P. Seethapathy, “Human and machine synergy in agriculture and food systems,” in HumanAI Cognitive Collaboration for Industrial Evolution. IGI Global Scientific Publishing, 2027, pp. 247– 278.
[23] F. Syafariani, M. S. Lola, S. S. S. Abd Mutalib, W. N. F. W. Nasir, A. A. K. A. Hamid, and N. H. Zainuddin, “Leveraging a hybrid machine learning model for enhanced cyberbullying detection,” Aptisi Transactions on Technopreneurship (ATT), vol. 7, no. 2, pp. 371–386, 2025.
[24] K.-W. Su, C.-H. Wang, O. Y.-H. Lin, H.-J. Chen, W.-L. Wang, P.-C. Chiu, and C.-S. Jiang, “Human centered design of an artificial intelligence-based diagnostic system for tomato diseases in smallholder agriculture,” Journal of Industrial and Production Engineering, vol. 43, no. 5, pp. 746–768, 2026.
[25] S. Sudha and J. S. Loret, “A review on machine learning-based precision agriculture techniques for crop farming monitoring with iot,” Discover Environment, vol. 4, no. 1, p. 10, 2026.
[26] M. Fanfani, L. A. I. Palesi, and P. Nesi, “Human-centered ai for decision support systems: A systematic review of application domains, architecture designs, current trends and future directions,” Big Data and Cognitive Computing, vol. 10, no. 6, p. 186, 2026.
[27] S. Uma, V. Vikram, S. Sachin et al., “Human-centered ai in smart farming: Toward agriculture 5.0,” in 2025 2nd International Conference on Computational Intelligence and Computing Applications (ICCICA). IEEE, 2025, pp. 433–438.
[28] I. H. Saputra, T. Mariyanti, and M. R. Athallah, “Strategy for development of pharmaceutical salt business in improving the welfare of the salt farmers from islamic perspective,” ADI Journal on Recent Innovation, vol. 4, no. 1, pp. 43–55, 2022.
[29] M. Migunani, A. Setiawan, and I. Sembiring, “Optimizing automated machine learning for ensemble performance and overfitting mitigation,” Aptisi Transactions on Technopreneurship (ATT), vol. 7, no. 3, pp. 808–822, 2025.
[30] B. Narin and T. Pinthong, “Human-centered iot system for soil ph monitoring: A design thinking approach toward smart agriculture,” in 2025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP). IEEE, 2025, pp. 1–6.
[31] R. Sriramkumar, J. A. Dhanraj, M. Lakshmanan, A. Vegi Fernando, Mithaguru, S. Saxena, and M. N. Naik, “Farmer-centric artificial intelligence through explainable federated learning for smart agriculture,” Agricultural Supply Chain Using Federated Learning, pp. 299–322, 2026.
[32] M. Ramadona, R. Abzie, M. L. Alamsyah, and Y. T. Putra, “Farming performance can be estimated using blockchain technology,” Blockchain Frontier Technology, vol. 2, no. 1, pp. 58–63, 2022.
[33] P. I. Braileanu, “Human–computer interaction in smart greenhouses: a review of interfaces, technologies,˘ and user-centered approaches,” Computers, vol. 14, no. 12, p. 553, 2025.
[34] A. Siabdelhadi and A. Ouared, “A human-centric cognitive framework to support climate-resilient farming behaviors,” Journal on Multimodal User Interfaces, pp. 1–29, 2026.
[35] P. Wu and Y. Zhong, “Artificial intelligence in sustainable agriculture: Towards a socio-technical roadmap,” Smart Agricultural Technology, p. 101578, 2025.
[36] A. S. Anita, T. Kuusk, G. Nicola, M. Hardini, and U. Rahardja, “Advancements in artificial intelligence and their contributions to sustainable development goals: A multidisciplinary review,” Smart Humancentered Emerging Research in Machine Intelligence, vol. 2, no. 1, pp. 37–47, 2026.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Adam Faturahman, James Williams, Evelyn Anderson, Goh Yao, Muhammad Rifat Syauqy (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
