Analysis of the Implementation of Smart Agriculture Technology in Enhancing Crop Management Efficiency
Keywords:
Smart Agriculture, Internet of Things, Sensor Pertanian, Artificial Intelligence, Efisiensi Pengelolaan TanamanAbstract
Smart Agriculture is an approach that utilizes digital technology for crop management through the integration of sensors, the Internet of Things (IoT), data processing, and artificial intelligence to support data-driven decision-making. This study aims to analyze the application of Smart Agriculture technologies in enhancing crop management efficiency and to identify the specific roles of each technology in the processes of monitoring, collecting, processing, analyzing, and presenting agricultural information. A qualitative-descriptive approach was employed, utilizing a literature review of relevant studies covering Smart Agriculture, IoT, agricultural sensors, Wireless Sensor Networks (WSN), cloud computing, big data, artificial intelligence (AI), and information systems. The analysis involved the identification, classification, synthesis, and thematic analysis of the technologies and functions found in the literature. The results indicate that crop management efficiency is supported by the integration of various technologies: sensors for data generation; IoT and WSN for connectivity; cloud computing and big data for data management support; AI for analysis and prediction; and information systems for information presentation. This study contributes a conceptual synthesis regarding the interrelationships among Smart Agriculture technologies in forming an integrated, data-driven crop management system. Furthermore, it establishes a foundation for future empirical research involving direct field testing of resource-use efficiency and system performance.
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[1] D. N. Halawa, “Peran Teknologi Pertanian Cerdas (Smart Farming) untuk Generasi Pertanian Indonesia,” JURNAL KRIDATAMA SAINS DAN TEKNOLOGI, vol. 6, no. 02, pp. 502–512, Aug. 2024, doi: 10.53863/kst.v6i02.1226.
[2] H. Ciptaningtyas et al., “Internet of Things (IoT) Smartfarming untuk Pertanian Bawang Merah di Desa Kare, Kabupaten Madiun,” Sewagati, vol. 9, no. 2, pp. 2873–2890, Mar. 2025, doi: 10.12962/j26139960.v9i2.2339.
[3] R. Zhang, H. Zhu, Q. Chang, and Q. Mao, “A Comprehensive Review of Digital Twins Technology in Agriculture,” Agriculture, vol. 15, no. 9, p. 903, Apr. 2025, doi: 10.3390/agriculture15090903.
[4] M. Awais, X. Wang, S. Hussain, F. Aziz, and M. Q. Mahmood, “Advancing Precision Agriculture Through Digital Twins and Smart Farming Technologies: A Review,” AgriEngineering, vol. 7, no. 5, p. 137, May 2025, doi: 10.3390/agriengineering7050137.
[5] T. Y. Melesse, “Digital twin-based applications in crop monitoring,” Heliyon, vol. 11, no. 2, p. e42137, Jan. 2025, doi: 10.1016/j.heliyon.2025.e42137.
[6] W. Du, P. Jin, and W. Jin, “Smart farm digital twin model based on edge-cloud architecture for tomato monitoring and detection,” Smart Agricultural Technology, vol. 12, p. 101254, Dec. 2025, doi: 10.1016/j.atech.2025.101254.
[7] C. L. Nkwocha and A. K. Chandel, “Towards an End-to-End Digital Framework for Precision Crop Disease Diagnosis and Management Based on Emerging Sensing and Computing Technologies: State over Past Decade and Prospects,” Computers, vol. 14, no. 10, p. 443, Oct. 2025, doi: 10.3390/computers14100443.
[8] A. T. Khan, S. M. Jensen, A. R. Khan, and S. Li, “Plant disease detection model for edge computing devices,” Front. Plant Sci., vol. 14, Dec. 2023, doi: 10.3389/fpls.2023.1308528.
[9] K. R and S. T, “Development of plant disease detection for smart agriculture,” Multimed. Tools Appl., vol. 83, no. 18, pp. 54391–54410, Dec. 2023, doi: 10.1007/s11042-023-17687-7.
[10] A. Upadhyay et al., “Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture,” Artif. Intell. Rev., vol. 58, no. 3, p. 92, Jan. 2025, doi: 10.1007/s10462-024-11100-x.
[11] A. Jafar, N. Bibi, R. A. Naqvi, A. Sadeghi-Niaraki, and D. Jeong, “Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations,” Front. Plant Sci., vol. 15, Mar. 2024, doi: 10.3389/fpls.2024.1356260.
[12] A. Jafar, N. Bibi, R. A. Naqvi, A. Sadeghi-Niaraki, and D. Jeong, “Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations,” Front. Plant Sci., vol. 15, Mar. 2024, doi: 10.3389/fpls.2024.1356260.
[13] A. Das et al., “Machine learning model ensemble for predicting sugarcane yield through synergy of optical and SAR remote sensing,” Remote Sens. Appl., vol. 30, p. 100962, Apr. 2023, doi: 10.1016/j.rsase.2023.100962.
[14] A. Morales and F. J. Villalobos, “Using machine learning for crop yield prediction in the past or the future,” Front. Plant Sci., vol. 14, Mar. 2023, doi: 10.3389/fpls.2023.1128388.
[15] Md. A. Jabed and M. A. Azmi Murad, “Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability,” Heliyon, vol. 10, no. 24, p. e40836, Dec. 2024, doi: 10.1016/j.heliyon.2024.e40836.
[16] A. S Menon, A. J, R. Sankaran, and K. P, “Deep learning based farm-level crop yield prediction using multi-temporal satellite data for complex engineering application,” Smart Agricultural Technology, vol. 12, p. 101562, Dec. 2025, doi: 10.1016/j.atech.2025.101562.
[17] N. Aierken, B. Yang, Y. Li, P. Jiang, G. Pan, and S. Li, “A review of unmanned aerial vehicle based remote sensing and machine learning for cotton crop growth monitoring,” Comput. Electron. Agric., vol. 227, p. 109601, Dec. 2024, doi: 10.1016/j.compag.2024.109601.
[18] J. Agrawal and M. Y. Arafat, “Transforming Farming: A Review of AI-Powered UAV Technologies in Precision Agriculture,” Drones, vol. 8, no. 11, p. 664, Nov. 2024, doi: 10.3390/drones8110664.
[19] R. Guebsi, S. Mami, and K. Chokmani, “Drones in Precision Agriculture: A Comprehensive Review of Applications, Technologies, and Challenges,” Drones, vol. 8, no. 11, p. 686, Nov. 2024, doi: 10.3390/drones8110686.
[20] A. N. Barve et al., “Advancing Precision Agriculture: The Role of UAVs and Drones in Sustainable Farming,” Asian Research Journal of Agriculture, vol. 17, no. 4, pp. 987–992, Dec. 2024, doi: 10.9734/arja/2024/v17i4611.
[21] K. S. Enock, M. J. Sagali, U. I. Jeannick, and D. Chen, “LoRa-Based Smart Agriculture Monitoring and Automatic Irrigation System,” Journal of Computer and Communications, vol. 13, no. 03, pp. 1–20, 2025, doi: 10.4236/jcc.2025.133001.
[22] Y. K. Kushwaha, A. Joshi, R. K. Panigrahi, and A. Pandey, “Development of a smart irrigation monitoring system employing the wireless sensor network for agricultural water management,” Journal of Hydroinformatics, vol. 26, no. 12, pp. 3224–3243, Dec. 2024, doi: 10.2166/hydro.2024.241.
[23] A. Singha, H. L. Gope, A. K. M. Islam, M. M. Billah, M. M. Hasan, and S. Barman, “Integrating IoT-Based Smart Irrigation Systems to Optimize Crop Yield and Water Management for Sustainable Agriculture,” in Proceedings of the 3rd International Conference on Computing Advancements, New York, NY, USA: ACM, Oct. 2024, pp. 123–130. doi: 10.1145/3723178.3723195.
[24] M. Bashiru, M. Ouedraogo, A. Ouedraogo, and P. Läderach, “Smart Farming Technologies for Sustainable Agriculture: A Review of the Promotion and Adoption Strategies by Smallholders in Sub-Saharan Africa,” Sustainability, vol. 16, no. 11, p. 4817, Jun. 2024, doi: 10.3390/su16114817.
[25] F. P. E. Putra, U. Ubaidi, M. A. Huda, H. Hasbullah, and A. Rohman, “Computer Network Management Optimization Through Big Data Analysis Using Time Series Analysis Method,” Brilliance: Research of Artificial Intelligence, vol. 4, no. 1, pp. 434–442, Aug. 2024, doi: 10.47709/brilliance.v4i1.4373.
[26] N. Haidar, F. P. Eka Putra, M. Arifin, M. Yasir Zain, and I. Darmawan, “Desain dan Perancangan Smart Campus berbasis ZigBee Wireless Sensor Network,” Jurnal Inovasi Teknologi dan Edukasi Teknik, vol. 1, no. 11, pp. 842–850, Nov. 2021, doi: 10.17977/um068v1i112021p842-850.
[27] F. P. E. Putra, U. Ubaidi, A. Hamzah, W. A. Pramadi, and A. Nuraini, “Systematic Literature Review: Security Gap Detection On Websites Using Owasp Zap,” Brilliance: Research of Artificial Intelligence, vol. 4, no. 1, pp. 348–355, Jul. 2024, doi: 10.47709/brilliance.v4i1.4227.
[28] A. F. Rachman, F. P. E. Putra, S. Syirofi, and D. Wahid, “Case Study of Computer Network Development for the Internet Of Things (IoT) Industry in an Urban Environment,” Brilliance: Research of Artificial Intelligence, vol. 4, no. 1, pp. 399–407, Aug. 2024, doi: 10.47709/brilliance.v4i1.4302.
[29] F. P. E. Putra, U. Ubaidi, R. N. Saputra, F. M. Haris, and S. N. R. Barokah, “Application of Internet of Things Technology in Monitoring Water Quality in Fishponds,” Brilliance: Research of Artificial Intelligence, vol. 4, no. 1, pp. 356–361, Jul. 2024, doi: 10.47709/brilliance.v4i1.4231.
[30] F. P. E. Putra, M. A. Mahmud, and I. S. Maqom, “Pengembangan Sistem Pemantauan Lingkungan Berbasis Internet of Things (IoT) di Kampus,” Digital Transformation Technology, vol. 3, no. 2, pp. 996–1001, Feb. 2024, doi: 10.47709/digitech.v3i2.3457.
[31] F. P. E. Putra, F. Fauzan, S. Syirofi, M. Mursidi, D. Wahid, and A. Nuraini, “Sistem Pengendali Lingkungan Pertanian Dengan Wireless Sensor Network Untuk Mengoptimalkan Budidaya Hidroponik,” Digital Transformation Technology, vol. 3, no. 2, pp. 931–937, Jan. 2024, doi: 10.47709/digitech.v3i2.3461.
[32] M. N. Arifin, M. U. Mansyur, A. Rahman, N. P. Dewi, and F. P. E. Putra, “Enhanced OCR Recognition for Madurese Text Documents: A Genetic Algorithm Approach with Tesseract 5.5,” JUITA: Jurnal Informatika, pp. 109–118, Aug. 2025, doi: 10.30595/juita.v13i2.25794.
[33] M. Zali, S. Nurlaila, F. P. Eka Putra, and A. Y. Heryadi, “Preventing dan Manajemen Karapan Sapi Tangghe’ Di Desa Langsar Kecamatan Saronggi Sumenep Madura,” Jurnal Pengabdian Masyarakat Indonesia, vol. 5, no. 4, pp. 1108–1117, Aug. 2025, doi: 10.52436/1.jpmi.3768.
[34] F. Prasetyo Eka Putra, S. R. Sutarsih, S. Sofiyulloh, P. Permana, and M. Umar Mansyur, “OPTIMALISASI PERANCANGAN APLIKASI MANAJEMEN DATA KOLOMAN, DI DESA PULAU MANDANGIN SAMPANG – MADURA BERBASIS WEBSITE,” Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, vol. 9, no. 2, pp. 285–294, Jul. 2024, doi: 10.36341/rabit.v9i2.4840.
[35] Fauzan Prasetyo Eka Putra, Dian Tri Agustina, Triana Selvia Khusnul Khotimah, and Tarisha Ramadhanty, “Analisis Kinerja Jaringan 5G dalam Meningkatkan Konektivitas Internet of Things (IoT),” Jurnal Informatika Dan Tekonologi Komputer (JITEK), vol. 5, no. 1, pp. 56–62, Mar. 2025, doi: 10.55606/jitek.v5i1.5836.
[36] Fauzan Prasetyo Eka Putra, Dian Tri Agustina, Triana Selvia Khusnul Khotimah, and Tarisha Ramadhanty, “Analisis Kinerja Jaringan 5G dalam Meningkatkan Konektivitas Internet of Things (IoT),” Jurnal Informatika Dan Tekonologi Komputer (JITEK), vol. 5, no. 1, pp. 56–62, Mar. 2025, doi: 10.55606/jitek.v5i1.5836.
[37] M. D. Y. Pamsi, F. K. Labu, K. S. H. Molan, and E. D. M. Welin, “Sosialisasi Smart Farming sebagai Strategi untuk Pertanian Keberlanjutan di Desa Baobolak, Kecamatan Nagawutung, Kabupaten Lembata,” Jurnal Pendidikan dan Pengabdian Masyarakat, vol. 8, no. 3, pp. 445–452, Aug. 2025, doi: 10.29303/jppm.v8i3.9432.
[38] S. P. Sudha and J. B. 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, Jan. 2026, doi: 10.1007/s44274-025-00305-8.
[39] D. Thotho and P. Macheso, “Comprehensive Survey on Applications of Internet of Things, Machine Learning and Artificial Intelligence in Precision Agriculture,” Tanzania Journal of Engineering and Technology, vol. 42, no. 4, pp. 30–45, Feb. 2024, doi: 10.52339/tjet.v42i4.922.
[40] K. M. Panduranga and R. H. Ranganathasharma, “Sustainability insights on learning-based approaches in precision agriculture in internet-of-things,” International Journal of Electrical and Computer Engineering (IJECE), vol. 14, no. 3, p. 3495, Jun. 2024, doi: 10.11591/ijece.v14i3.pp3495-3511.
[41] A. Pagano, D. Croce, I. Tinnirello, and G. Vitale, “A Survey on LoRa for Smart Agriculture: Current Trends and Future Perspectives,” IEEE Internet Things J., vol. 10, no. 4, pp. 3664–3679, Feb. 2023, doi: 10.1109/JIOT.2022.3230505.
[42] G. Yassin and L. Ramaswamy, “Effective & Efficient Access Control in Smart Farms: Opportunities, Challenges & Potential Approaches,” in Proceedings of the 8th International Conference on Information Systems Security and Privacy, SCITEPRESS - Science and Technology Publications, 2022, pp. 445–452. doi: 10.5220/0010873000003120.
[43] E. Yilmaz, S. C. Bocekci, C. Safak, and K. Yildiz, “Advancements in smart agriculture: A systematic literature review on state‐of‐the‐art plant disease detection with computer vision,” IET Computer Vision, vol. 19, no. 1, Jan. 2025, doi: 10.1049/cvi2.70004.
[44] A. Jafar, N. Bibi, R. A. Naqvi, A. Sadeghi-Niaraki, and D. Jeong, “Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations,” Front. Plant Sci., vol. 15, Mar. 2024, doi: 10.3389/fpls.2024.1356260.
[45] F. P. Eka Putra, I. N. Sudana Degeng, S. Ulfa, and W. Kamdi, “The Evolution of Quality Education: Impacts and Challenges of Using Open Educational Resources (OER) and Open Educational Practices (OEP) in the Conceive - Design - Implement - Operate (CDIO) Framework,” TEM Journal, pp. 386–395, Feb. 2024, doi: 10.18421/TEM131-40.
[46] V. S. Dhaka, N. Kundu, G. Rani, E. Zumpano, and E. Vocaturo, “Role of Internet of Things and Deep Learning Techniques in Plant Disease Detection and Classification: A Focused Review,” Sensors, vol. 23, no. 18, p. 7877, Sep. 2023, doi: 10.3390/s23187877.
[47] “An internet of things adoption framework for the South African farming industry,” Issues In Information Systems, 2025, doi: 10.48009/4_iis_2025_129.
[48] A. A. Zhahir et al., “Smart Farming Adoption – A Scoping Review,” KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, vol. 4, no. 1, pp. 14–23, Jun. 2024, doi: 10.24002/konstelasi.v4i1.9257.
[49] R. J. Thomas, G. O’Hare, and D. Coyle, “Understanding technology acceptance in smart agriculture: A systematic review of empirical research in crop production,” Technol. Forecast. Soc. Change, vol. 189, p. 122374, Apr. 2023, doi: 10.1016/j.techfore.2023.122374.
[50] F. P. Eka Putra, F. Muslim, N. Hasanah, Holipah, R. Paradina, and R. Alim, “Analisis Komparasi Protokol Websocket dan MQTT Dalam Proses Push Notification,” Jurnal Sistim Informasi dan Teknologi, pp. 63–72, Jan. 2024, doi: 10.60083/jsisfotek.v5i4.325.
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