INTEGRATING DIGITAL TECHNOLOGIES IN AGRICULTURE LEVERAGING DRONES, SENSORS, AND AI FOR PRECISION CROP MANAGEMENT
Denmark Denmark 
Indonesia 
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Artificial Intelligence, Digital Agriculture, Drones, Precision Crop Management, Wireless Sensors
Abstract
Agricultural production increasingly faces climate variability, resource scarcity, environmental degradation, and rising demands for sustainable food systems, creating an urgent need for precise and adaptive crop management. This study examines how integrated drones, wireless field sensors, and artificial intelligence can enhance precision crop management by transforming heterogeneous agricultural data into actionable decision intelligence. A mixed-methods field-based design combined quasi-experimental comparisons, drone-based remote sensing, continuous sensor monitoring, multimodal data integration, artificial intelligence modeling, agronomic measurements, and stakeholder interviews to evaluate technological and operational performance. The findings indicate that integrated digital management improved crop-stress detection, accelerated management responses, increased productivity, and enhanced water, fertilizer, and pesticide-use efficiency compared with conventional practices. Multimodal integration generated stronger predictive performance than isolated technologies by combining spatial, temporal, and environmental information, while human validation remained essential for adapting algorithmic recommendations to field conditions. The study concludes that effective precision agriculture depends not on technology quantity but on integration quality and the capacity to translate data into timely, interpretable, and contextually relevant interventions. The proposed Integrated Agricultural Digital Intelligence Framework establishes a continuous Sense–Integrate–Analyze–Decide–Act–Learn cycle, providing a foundation for adaptive, resource-efficient, and sustainable crop management across diverse agricultural contexts worldwide.
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References
Apollon, W., Jean-Baptiste, Y., Maheshwari, S., Ghosh, S., Ghosh, S., Ariste, A., Joseph, R., Kamaraj, S.-K., & Bedair, H. (2026). Smart agriculture for greenhouse gas mitigation: Integrating digital tools, engineering metrics, and policy instruments—A review. Next Research, 7, 101445. https://doi.org/10.1016/j.nexres.2026.101445
Bhardwaj, M., Kumar, P., & Singh, A. (2026). Bibliometric review of digital transformation in agriculture: Innovations, trends and sustainable futures. Journal of Agribusiness in Developing and Emerging Economies, 16(4), 826–850. https://doi.org/10.1108/JADEE-02-2025-0083
Biswas, P., Kim, T. M., & Kim, W. S. (2026). Integration of digital twins and physical AI in cyber-physical systems. Intelligent Systems with Applications, 30, 200649. https://doi.org/10.1016/j.iswa.2026.200649
Bitakou, E., Kotzabasaki, M., Psiroukis, V., Nychas, K., Demestichas, K., Bartzanas, T., Magazin, N., & Vuji?, S. (2026). AI-driven approaches in precision agriculture for strawberry production: A systematic literature review. Smart Agricultural Technology, 14, 101944. https://doi.org/10.1016/j.atech.2026.101944
Boulealam, C., Filali, H., Mahraz, A. M., Tairi, H., & Riffi, J. (2026). Sugar beet detection using drone imagery. Array, 30, 100952. https://doi.org/10.1016/j.array.2026.100952
Byrareddy, V. M., Baillie, J., & Scobie, M. (2026). A systematic review of digital twin technologies in broadacre agriculture: Challenges, opportunities, and sustainability. Smart Agricultural Technology, 14, 102394. https://doi.org/10.1016/j.atech.2026.102394
Cheikhrouhou, S., Turki, M., & Dammak, B. (2026). X-FedAgriTwin: Privacy-preserving crop recommendation via Digital Twins and explainable federated learning. Information and Software Technology, 196, 108173. https://doi.org/10.1016/j.infsof.2026.108173
El Hachimi, C., Belaqziz, S., Khabba, S., Karjoun, H., Kharrou, M. H., Hssaine, B. A., Er-Raki, S., Daccache, A., Chehbouni, A., & Weng, Q. (2025). Towards collective intelligence in agriculture: Deep reinforcement learning and digital twins for efficient management of collective irrigation water distribution systems. Energy Nexus, 20, 100599. https://doi.org/10.1016/j.nexus.2025.100599
Elhoseny, M., Jahid, K., Latif, R., Saddik, A., & Atassi, R. (2026). Recent advances in IoT-driven crop monitoring and precision irrigation: Technologies, models, and future challenges. Scientific African, 31, e03222. https://doi.org/10.1016/j.sciaf.2026.e03222
Eze, G., & Fiume, P. (2026). Chapter 2—Digital innovations in farming practices. In T. Ben Hassen & H. El Bilali (Eds.), Digital Technologies for Sustainable Agriculture and Food Systems (pp. 11–42). Academic Press. https://doi.org/10.1016/B978-0-443-33508-2.00005-0
Gueboudji, Z. (2026). Chapter 5—Nitrogen management using digital agriculture technology: A key for climate change mitigation. In T. A. Kurniawan & A. Anouzla (Eds.), Point Source Nitrogen Pollution (pp. 71–92). Elsevier. https://doi.org/10.1016/B978-0-443-27352-0.00007-0
Hashem, I. A., Zerdoumi, S., Jhanjhi, N., Siddiqa, A., & Foufou, S. (2026). Optimization and performance analysis of Drones and Unmanned Aerial Systems and Their Intelligence Applications. International Journal of Cognitive Computing in Engineering, 7, 128–144. https://doi.org/10.1016/j.ijcce.2025.10.004
Khoshnodifar, Z., Ataei, P., & Karimi, H. (2025). Challenges of drone development in Iran’s agricultural sector: The application of the TOWS analysis. Cleaner Engineering and Technology, 26, 100950. https://doi.org/10.1016/j.clet.2025.100950
Kumar, R., Bagul, V. D., & Kumar, M. (2026). Chapter 3—AI in precision pest management and agricultural inputs. In N. A. Sagar, S. Agarwal, S. Sharma, V. Singh, & A. R. Jambrak (Eds.), Artificial Intelligence Interventions in Agriculture, Food and Health Sectors (pp. 65–90). Academic Press. https://doi.org/10.1016/B978-0-443-40501-3.00019-4
Kumar, S., Wani, A. W., Kaushik, R., Kaur, H., Djajadi, D., Khamidah, A., Saidah, Alasbali, N., Alreshidi, M. A., Alam, M. W., Yadav, K. K., & Wani, A. K. (2024). Navigating the landscape of precision horticulture: Sustainable agriculture in the digital Age. Scientia Horticulturae, 338, 113688. https://doi.org/10.1016/j.scienta.2024.113688
Majdalawieh, M., Martins, C., Radi, M., Alaraj, M., & Khan, S. (2025). Precision agriculture in the age of AI: A systematic review of machine learning methods for crop disease detection. Smart Agricultural Technology, 12, 101491. https://doi.org/10.1016/j.atech.2025.101491
Mamabolo, E., Mashala, M. J., Mugari, E., Mogale, T. E., Mathebula, N., Mabitsela, K., & Ayisi, K. K. (2025). Application of precision agriculture technologies for crop protection and soil health. Smart Agricultural Technology, 12, 101270. https://doi.org/10.1016/j.atech.2025.101270
Manimozhian, A., & Chandel, A. K. (2026). Thermal infrared technologies for precision agriculture: A ROSES-guided systematic evidence synthesis on platforms, calibration, and digital analytics. Agricultural Environment and Sustainability, 1(2), 100014. https://doi.org/10.1016/j.ages.2026.100014
Mehrotra, S., Mishra, S., Srivastava, V., Yadav, D., & Singh, V. (2026). Digital dimension of plant science research: A bird’s eye view of AI integration. Plant Science, 371, 113297. https://doi.org/10.1016/j.plantsci.2026.113297
Morchid, A., Kabbaj, M. N., & Benbrahim, M. (2026). Precision irrigation based on IoT and energy management: Driving future sustainable water management in farming. Renewable and Sustainable Energy Reviews, 240, 117179. https://doi.org/10.1016/j.rser.2026.117179
Morgan, H., Elgendy, A., Tran, B., Ismail, T., Moursy, M. M., Shehadeh, Y. Kh., ElGharib, A., Al-Dughairi, A. A., Mubarak, A. E., Harhash, K. A., & El-Askary, H. (2026). AI-driven multispectral drone monitoring for afforestation in arid environments. Results in Engineering, 30, 110792. https://doi.org/10.1016/j.rineng.2026.110792
Moussaid, A., Mousannif, H., Gamoussi, Y., & Briak, H. (2026). Advanced artificial intelligence and remote sensing for earth observation–based tree crop monitoring: A global review with african perspectives. Neurocomputing, 700, 134165. https://doi.org/10.1016/j.neucom.2026.134165
Nath, M. K., Mookkandi, K., Balakrishnan, K., Mishra, M., Kavitha, M., & Mathumathi, M. (2026). Bottleneck of AI driven techniques for identifying indian agricultural diseases: A systematic review and future directives. Results in Engineering, 31, 111212. https://doi.org/10.1016/j.rineng.2026.111212
Rahimzad, M., Homayouni, S., & McNairn, H. (2026). Active and incremental learning with RAUNet for advancing soil cover monitoring from mobile digital imaging through deep learning. Computers and Electronics in Agriculture, 253, 112161. https://doi.org/10.1016/j.compag.2026.112161
Rai, N., (Dana) Choi, D., Boyd, N. S., & Schumann, A. W. (2026). Advancing site-specific disease and pest management in precision agriculture: From reasoning-driven foundation models to adaptive, feedback-based learning. Computers and Electronics in Agriculture, 250, 111859. https://doi.org/10.1016/j.compag.2026.111859
Rayamajhi, A., Jahanifar, H., & Mahmud, M. S. (2024). Measuring ornamental tree canopy attributes for precision spraying using drone technology and self-supervised segmentation. Computers and Electronics in Agriculture, 225, 109359. https://doi.org/10.1016/j.compag.2024.109359
Rehman, M. U., Eesaar, H., Abbas, Z., Seneviratne, L., Hussain, I., & Chong, K. T. (2024). Advanced drone-based weed detection using feature-enriched deep learning approach. Knowledge-Based Systems, 305, 112655. https://doi.org/10.1016/j.knosys.2024.112655
Storm, H., Seidel, S. J., Klingbeil, L., Ewert, F., Vereecken, H., Amelung, W., Behnke, S., Bennewitz, M., Börner, J., Döring, T., Gall, J., Mahlein, A.-K., McCool, C., Rascher, U., Wrobel, S., Schnepf, A., Stachniss, C., & Kuhlmann, H. (2024). Research priorities to leverage smart digital technologies for sustainable crop production. European Journal of Agronomy, 156, 127178. https://doi.org/10.1016/j.eja.2024.127178
Subeesh, A., & Chauhan, N. (2026). Agricultural digital twin for smart farming: A review. Green Technologies and Sustainability, 4(2), 100299. https://doi.org/10.1016/j.grets.2025.100299
Talaeizadeh, A., Sharifi, I., Alasty, A., & Ghatrehsamani, S. (2025). Agricultural spraying drones: A comprehensive review. Smart Agricultural Technology, 12, 101519. https://doi.org/10.1016/j.atech.2025.101519
Vahidi, M., Shafian, S., & Frame, W. H. (2025). Multi-Modal sensing for soil moisture mapping: Integrating drone-based ground penetrating radar and RGB-thermal imaging with deep learning. Computers and Electronics in Agriculture, 236, 110423. https://doi.org/10.1016/j.compag.2025.110423
Volpato, L., Wright, E. M., & Gomez, F. E. (2024). Drone-Based Digital Phenotyping to Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans (Phaseolus vulgaris L.). Plant Phenomics, 6, 0278. https://doi.org/10.34133/plantphenomics.0278
Xia, X., Sachar, J., Alibhai, K., Labelle, J., Demin, D., Sulle, E., & Kim, W. S. (2025). Accessible drone image processing for sustainable resource management of 3D tree-like crops using unsupervised algorithms. Information Processing in Agriculture. https://doi.org/10.1016/j.inpa.2025.11.009
Xu, C., Zhao, L., Wen, H., Zhang, Y., Wang, L., & Zhang, L. (2026). Intelligent management of crop diseases and pests in multiscale and multimodal complex scenarios: Technologies, applications, and prospects. Computers and Electronics in Agriculture, 244, 111443. https://doi.org/10.1016/j.compag.2026.111443
Yan, Y., Song, F., & Sun, J. (2025). The application of UAV technology in maize crop protection strategies: A review. Computers and Electronics in Agriculture, 237, 110679. https://doi.org/10.1016/j.compag.2025.110679
Yang, Z., & Tang, T. (2026). Closing the loop: AI-driven integration of multi-omics and phenomics for systematic resilient crop engineering. Biotechnology Advances, 90, 108908. https://doi.org/10.1016/j.biotechadv.2026.108908
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Copyright (c) 2026 Lars Jensen, Maria Nielsen, Ismail Ismail
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