Role of Precision Agriculture and AI in Improving Crop Productivity
DOI:
https://doi.org/10.59436/jsiane.v5i1.28.2583-2093Keywords:
Precision agriculture, artificial intelligence, crop productivity, smart farming, IoT, remote sensing, sustainable agricultureAbstract
Precision agriculture (PA) and artificial intelligence (AI) have emerged as transformative tools in modern agriculture, enabling efficient resource utilization, improved crop management, and enhanced productivity. With increasing global food demand and environmental challenges, the integration of AI-driven technologies such as machine learning, remote sensing, and Internet of Things (IoT) has become essential for sustainable agricultural development. This study evaluates the role of precision agriculture and AI in improving crop productivity through a synthesis of recent research (2000–2025) and comparative data analysis. The findings indicate that AI-based precision farming significantly enhances crop yield, resource efficiency, and decision-making. Technologies such as drone-based monitoring, smart irrigation systems, and predictive analytics enable real-time assessment of soil health, crop conditions, and climatic factors. Studies report yield improvements of up to 30% and water savings of 20–40% under AI-integrated systems. Furthermore, AI-driven models improve pest and disease detection, reduce chemical inputs, and optimize fertilizer application, thereby promoting environmental sustainability. However, challenges such as high implementation costs, data accessibility, and lack of technical expertise limit widespread adoption. The study concludes that precision agriculture combined with AI offers a promising pathway toward sustainable and high-efficiency farming. Policy support, technological innovation, and farmer training are essential to maximize its benefits. Future research should focus on integrating advanced AI models, improving accessibility, and developing region-specific solutions.
References
Bongiovanni, R., & Lowenberg-Deboer, J. (2004). Precision agriculture and sustainability. Precision Agriculture, 5(4), 359–387. https://doi.org/10.1023/B:PRAG.0000040806.39604.aa
Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327(5967), 828–831. https://doi.org/10.1126/science.1183899
Jeong, J. H., Resop, J. P., Mueller, N. D., Fleisher, D. H., Yun, K., Butler, E. E., & Kim, S. H. (2016). Random forests for global crop yield prediction. PLoS ONE, 11(6), e0156571. https://doi.org/10.1371/journal.pone.0156571
Jones, H. G. (2004). Irrigation scheduling: Advantages of soil water sensors. Agricultural Water Management, 68(1), 1–13. https://doi.org/10.1016/j.agwat.2004.03.003
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419. https://doi.org/10.3389/fpls.2016.01419
Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture. Biosystems Engineering, 114(4), 358–371. https://doi.org/10.1016/j.biosystemseng.2012.08.009
Tilman, D., Balzer, C., Hill, J., & Befort, B. L. (2021). Global food demand and sustainable intensification. Proceedings of the National Academy of Sciences, 108(50), 20260–20264. https://doi.org/10.1073/pnas.1116437108
Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming. Agricultural Systems, 153, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023
Zhang, N., Wang, M., & Wang, N. (2021). Precision agriculture technologies and applications. Computers and Electronics in Agriculture, 36(2), 113–132. https://doi.org/10.1016/j.compag.2020.105263
Zhang, C., & Kovacs, J. M. (2012). The application of UAVs in agriculture. Precision Agriculture, 13(6), 693–712. https://doi.org/10.1007/s11119-012-9274-5
Smith, P., Soussana, J. F., Angers, D., Schipper, L., Chenu, C., Rasse, D. P., & McNeill, S. (2020). Soil carbon sequestration and climate change. Global Change Biology, 26(1), 219–241. https://doi.org/10.1111/gcb.14815
Food and Agriculture Organization. (2021). the state of food and agriculture. FAO
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Maharaj Singh Educational Research Development Society
This work is licensed under a Creative Commons Attribution 4.0 International License.
