Shaping the Future of Agriculture with Intelligent Systems
DOI:
https://doi.org/10.70356/josapen.v3i2.72Keywords:
Agriculture, Intelligent Systems, Climate changeAbstract
This study explores the implementation of intelligent systems in agriculture as a solution to longstanding challenges such as inefficient resource use, disease management, and low productivity. By integrating technologies like Artificial Intelligence (AI), the Internet of Things (IoT), computer vision, and robotics, intelligent systems enable precision farming that optimizes water usage, enhances crop monitoring, automates labor-intensive tasks, and improves overall decision-making. Real-world applications such as CropX and NetBeat for smart irrigation, Plantix and Nuru for disease detection, and John Deere’s autonomous tractors for automated fieldwork demonstrate the tangible benefits of these innovations. Additionally, tools like Moocall and Ida offer real-time livestock health monitoring, while platforms such as AgriPredict and aWhere provide data-driven decision support to farmers globally. A sample block diagram of a smart irrigation system, supported by a simplified calculation, illustrates the practical operation and measurable benefits of such systems. The study emphasizes the potential of intelligent agriculture not only to boost productivity and sustainability but also to make advanced tools more accessible to small and medium-scale farmers. Future advancements should aim to enhance integration, affordability, and ease of use, ultimately supporting the transition to more resilient and efficient agricultural practices in the face of growing global food demands.
Downloads
References
G. S. Garcia, C. Craveiro, C. Rocha, and E. Recine, “Concepts, Methods, and Parameters: A scoping review of tools for assessing food system sustainability,” Environ. Sustain. Indic., vol. 27, no. April, p. 100782, 2025, doi: https://doi.org/10.1016/j.indic.2025.100782.
C. X. Yang, L. M. Baker, A. Mattox, D. Diehl, and S. Honeycutt, “The forgotten factor: Exploring consumer perceptions of artificial intelligence in the food and agriculture systems,” Futur. Foods, vol. 11, no. January, p. 100553, 2025, doi: https://doi.org/10.1016/j.fufo.2025.100553.
K. A. Singh, F. Patra, T. Ghosh, N. K. Mahnot, H. Dutta, and R. K. Duary, “Advancing food systems with industry 5.0: A systematic review of smart technologies, sustainability, and resource optimization,” Sustain. Futur., vol. 9, no. August 2024, p. 100694, 2025, doi: https://doi.org/10.1016/j.sftr.2025.100694.
A. Bin Rashid, A. K. Kausik, A. Khandoker, and S. N. Siddque, “Integration of Artificial Intelligence and IoT with UAVs for Precision Agriculture,” Hybrid Adv., vol. 10, no. January, p. 100458, 2025, doi: https://doi.org/10.1016/j.hybadv.2025.100458.
L. Colizzi, G. Dimauro, E. Guerriero, and N. Lomonte, “Artificial intelligence and IoT for water saving in agriculture: A systematic review,” Smart Agric. Technol., vol. 11, no. March, p. 101008, 2025, doi: https://doi.org/10.1016/j.hybadv.2025.100458.
V. Choudhary, P. Guha, G. Pau, and S. Mishra, “An overview of smart agriculture using internet of things (IoT) and web services,” Environ. Sustain. Indic., vol. 26, no. January, 2025, doi: https://doi.org/10.1016/j.indic.2025.100607.
S. M. Zaigham Abbas Naqvi et al., “Climate-resilient water management: Leveraging IoT and AI for sustainable agriculture,” Egypt. Informatics J., vol. 30, no. April, p. 100691, 2025, doi: https://doi.org/10.1016/j.eij.2025.100691.
J. Wanyama et al., “A systematic review of fourth industrial revolution technologies in smart irrigation: Constraints, opportunities, and future prospects for sub-Saharan Africa,” Smart Agric. Technol., vol. 7, no. October 2023, p. 100412, 2024, doi: https://doi.org/10.1016/j.atech.2024.100412.
N. Ahmed and N. Shakoor, “Advancing agriculture through IoT, Big Data, and AI: A review of smart technologies enabling sustainability,” Smart Agric. Technol., vol. 10, no. December 2024, p. 100848, 2025, doi: https://doi.org/10.1016/j.atech.2025.100848.
D. Pang et al., “A mini review on AI-driven thermal treatment of solid Waste: Emission control and process optimization,” Green Energy Resour., vol. 3, no. 2, p. 100132, 2025, doi: https://doi.org/10.1016/j.gerr.2025.100132.
H. Yang, Q. Feng, S. Xia, Z. Wu, and Y. Zhang, “Arti fi cial Intelligence in Agriculture AI-driven aquaculture : A review of technological innovations and their sustainable impacts,” Artif. Intell. Agric., vol. 15, no. 3, pp. 508–525, 2025, doi: https://doi.org/10.1016/j.aiia.2025.01.012.
M. Javaid, A. Haleem, R. P. Singh, and R. Suman, “Enhancing smart farming through the applications of Agriculture 4.0 technologies,” Int. J. Intell. Networks, vol. 3, no. July, pp. 150–164, 2022, doi: https://doi.org/10.1016/j.ijin.2022.09.004.
D. D. Mühl and L. de Oliveira, “A bibliometric and thematic approach to agriculture 4.0,” Heliyon, vol. 8, no. 5, 2022, doi: https://doi.org/10.1016/j.heliyon.2022.e09369.
M. Roy and A. Medhekar, “Transforming smart farming for sustainability through agri-tech Innovations: Insights from the Australian agricultural landscape,” Farming Syst., vol. 3, no. 4, p. 100165, 2025, doi: https://doi.org/10.1016/j.farsys.2025.100165.
M. A. Mushtaq et al., “Securing fruit trees future: AI-driven early warning and predictive systems for abiotic stress in changing climate,” Plant Stress, vol. 17, no. April, p. 100953, 2025, doi: https://doi.org/10.1016/j.stress.2025.100953.
G. A. Senthil, S. U. Suganthi, L. Prinslin, R. Selvi, and R. Prabha, “Generative AI in Agri: Sustainability in Smart Precision Farming Yield Prediction Mapping System Based on GIS Using Deep Learning and GPS,” Procedia Comput. Sci., vol. 252, pp. 365–380, 2025, doi: https://doi.org/10.1016/j.procs.2024.12.038.
R. Matera et al., “Precision livestock farming in buffalo species: a sustainable approach for the future,” Smart Agric. Technol., vol. 11, no. March, p. 101060, 2025, doi: https://doi.org/10.1016/j.atech.2025.101060.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Ican Anwar
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
