A Resource-Constrained AI Factory Framework with Data Schema and Validation for Satellite-Based Agricultural Monitoring in Pakistan
Keywords:
AI Factory, GIS & Remote Sensing, QVS, Agriculture, QDSAbstract
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References
Ahmad Jamal, Hassan Raza, “AI and Data Analytics for Precision Agriculture: Current Progress and Future Directions,” JATAED J. Appropr. Technol. Agric. Environ. Dev., vol. 2, no. 2, pp. 36–46, 2025, doi: 10.62671/jataed.v2i2.88.
E. Navarro, N. Costa, and A. Pereira, “A Systematic Review of IoT Solutions for Smart Farming,” Sensors 2020, Vol. 20, Page 4231, vol. 20, no. 15, p. 4231, Jul. 2020, doi: 10.3390/S20154231.
Sjaak Wolfert, Lan Ge, “Big Data in Smart Farming – A review,” Agric. Syst., vol. 153, pp. 69–80, 2017, doi: https://doi.org/10.1016/j.agsy.2017.01.023.
Ania Cravero, Samuel Sepúlveda, “From Precision Agriculture to Intelligent Agricultural Ecosystems: A Systematic Review of Machine Learning and Big Data Applications,” Agronomy, vol. 16, no. 5, p. 516, 2026, doi: https://doi.org/10.3390/agronomy16050516.
Yasir Mehmood, Nosheen Sabahat, “MLOps critical success factors - A systematic literature review,” VFAST Trans. Softw. Eng., vol. 12, no. 1, 2024, doi: 10.21015/vtse.v12i1.1747.
Sushant Kumar, Sumit Datta, “Opportunities and Challenges in Data-Centric AI,” IEEE Access, 2024, doi: 10.1109/ACCESS.2024.3369417.
Ronivaldo Ferreira, Guilherme da Silva, “From Pre-labeling to Production: Engineering Lessons from a Machine Learning Pipeline in the Public Sector,” Proc. 5th Int. Conf. AI Eng. â•fi Softw. Eng. AI (CAIN ’26), 2025, [Online]. Available: https://arxiv.org/html/2511.01545v1
P. P. Ray, “A Review of TRiSM Frameworks in Artificial Intelligence Systems: Fundamentals, Taxonomy, Use Cases, Key Challenges and Future Directions,” Expert Syst., vol. 43, no. 3, p. e70213, Mar. 2026, doi: 10.1111/exsy.70213.
Hyun-Ho Choi, Petros Amanatidis, “Intelligent Water Management Through Edge-Enabled IoT, AI, and Big Data Technologies,” IOT, vol. 7, no. 1, p. 5, 2026, doi: https://doi.org/10.3390/iot7010005.
Zhaojie Chen, Guangyu Zhang, Fan Zhang, “Multimodal AI for Real-Time Food Safety and Quality: From Sensors to Foundation Models, Edge Deployment, and Regulation,” Food Sci. Nutr., 2026, doi: https://doi.org/10.1002/fsn3.71534Digital Object Identifier (DOI).
Nithya Sambasivan Shivani Kapania Hannah Highfll, Diana Akrong Praveen Paritosh Lora Aroyo, “‘Everyone wants to do the model work, not the data work’: Data Cascades in High-Stakes AI,” Conf. Hum. Factors Comput. Syst. - Proc., 2021, [Online]. Available: https://dl.acm.org/doi/10.1145/3411764.3445518
“(PDF) Data Validation Process in Machine Learning Pipeline.” Accessed: Apr. 13, 2026. [Online]. Available: https://www.researchgate.net/publication/351022721_Data_Validation_Process_in_Machine_Learning_Pipeline
Irene Iele, Giulia Romoli, Daniele Molino, Elena Mulero Ayllón, Filippo Ruffini, Paolo Soda, Matteo Tortora, “Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates,” arXiv:2602.17683, 2026, [Online]. Available: https://arxiv.org/abs/2602.17683
T. Kaur, J. Singh, P. Singh, and G. Singh, “A stacking-based machine learning framework for crop production prediction,” Proc. Indian Natl. Sci. Acad. 2025, pp. 1–18, Dec. 2025, doi: 10.1007/s43538-025-00579-9.
Jiaqi Zhou, Paul Brereton, “Building an intelligent food assurance system based on DevOps: A review,” Futur. Foods, vol. 12, p. 100847, 2025, doi: https://doi.org/10.1016/j.fufo.2025.100847.
Soo Jin Kim, Hyunjoon Kim, “Assessment and correction of Sentinel-2 and Landsat-8/9 NDVI using in-situ measurements across rice growth stages in southern South Korea,” F. Crop. Res., vol. 334, p. 110149, 2025, doi: https://doi.org/10.1016/j.fcr.2025.110149.
Muhammad Shoaib, Wasif Yousaf, “Assessing the potential of Sentinel-2 imagery and NDVI thresholds for the development of crop phenology: A case study of Sahiwal District,” Pakistan J. Agric. Res., vol. 60, no. 3, pp. 419–428, 2023, doi: 10.21162/PAKJAS/23.970.
Antonio Carlos Cob-Parro, Yerhard Lalangui, “Fostering Agricultural Transformation through AI: An Open-Source AI Architecture Exploiting the MLOps Paradigm,” Agronomy, vol. 14, no. 2, p. 259, 2024, doi: https://doi.org/10.3390/agronomy14020259.
Rahat Tufail, Patrizia Tassinari, “Deep Learning Applications for Crop Mapping Using Multi-Temporal Sentinel-2 Data and Red-Edge Vegetation Indices: Integrating Convolutional and Recurrent Neural Networks,” Remote Sens., vol. 17, no. 18, p. 3207, 2025, doi: https://doi.org/10.3390/rs17183207.
Shriyank Somvanshi, Md Monzurul Islam, “From TinyML to TinyDL: A survey for edge AI applications,” ACM Comput. Surv., vol. 58, no. 7, pp. 1–33, 2025, [Online]. Available: https://dl.acm.org/doi/10.1145/3776588
Muhammad Riaz, Shaopeng Zhao, “Using remote sensing in agriculture for sustainable development goals in developing countries,” Util. Earth Obs. Data Reach. Sustain. Dev. Goals, 2026, doi: 10.1016/B978-0-443-30204-6.00004-8.
Alok Kumar Srivastav, Priyanka Das, “Edge Computing and AI in Agricultural IoT,” Biotechnol. IoT Agric. Food Prod. Green Innov., 2025, doi: 10.1007/979-8-8688-1469-3_21.
Yicong Sun, Tingting Zhao, “Crop classification method for multi-temporal remote sensing imagery based on a (3 + 2)D SAFPN,” Front. Plant Sci., vol. 17, 2026, doi: https://doi.org/10.3389/fpls.2026.1765836.
Q.-U.-A. A. Nuzba Shaheen, “CMIP6-Based Climate Projections and Trends for Exploring Adaptations and Policies in Pakistan,” Pakistan J. Eng. Appl. Sci., vol. 33, pp. 50–69, 2025, [Online]. Available: https://journal.uet.edu.pk/ojs_old/
index.php/pjeas/article/view/3700
M. B. Moisa, Z. R. Roba, S. Purohit, K. T. Deribew, and D. O. Gemeda, “Evaluating the impact of land use and land cover change on soil moisture variability using GIS and remote sensing technology in southwestern Ethiopia,” Environ. Monit. Assess. 2025 1977, vol. 197, no. 7, pp. 824-, Jun. 2025, doi: 10.1007/s10661-025-14301-1.
N. A. M. Abdelrahim and S. Jin, “A Novel Agricultural Remote Sensing Drought Index (ARSDI) for high-resolution drought assessment in Africa using Sentinel and Landsat data,” Environ. Monit. Assess. 2025 1973, vol. 197, no. 3, pp. 242-, Feb. 2025, doi: 10.1007/s10661-025-13686-3.
L. Barbieri, J. Wyngaard, S. Swanz, and A. K. Thomer, “Making Drone Data FAIR Through a Community-Developed Information Framework,” Data Sci. J., vol. 22, no. 1, 2023, doi: 10.5334/DSJ-2023-001.
Josepha Schiller, Stefan Stiller & Masahiro Ryo, “Artificial intelligence in environmental and Earth system sciences: explainability and trustworthiness,” Artif. Intell. Rev., vol. 58, no. 316, 2025, [Online]. Available: https://link.springer.com/article/10.1007/s10462-025-11165-2
Tahir Ata-Ul-Karim, Yang Liu, “Integrating Remote Sensing and Autonomous Robotics in Precision Agriculture: Current Applications and Workflow Challenges,” Agronomy, vol. 15, no. 10, p. 2314, 2025, doi: https://doi.org/10.3390/agronomy15102314.
F. Berisha, L. Da Silva, E. Tegolo, P. Mooney, Z. Pourzolfaghar, and M. Helfert, “An AI-Driven Framework for Agricultural Data Interoperability Informed by Literature and Stakeholder Insights,” pp. 308–316, Nov. 2025, doi: 10.1109/cyber-ai66431.2025.11233360.
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