Technology Investment in Smart and Sustainable Agriculture towards Food Security
Abstract
Internet of Things (IoT) has emerged as a transformative force across multiple sectors of the agriculture industry, enhancing both quality and quantity of agricultural yield. Artificial intelligence, integrated with IoT, encompasses soil preparation, cultivation, harvesting, and research-related activities, leading to sustainable productivity improvements. Agriculture automation has enhanced precision in farming operations including irrigation control, pesticide/weedicide/fertilizer management, crop growth monitoring, and environmental control in greenhouse and hydroponics systems. This chapter reviews IoT applications in crop farming, animal farming, farm monitoring and tracking, disease detection in plants and livestock, classification processes of agricultural foods, quality assessment of vegetables and fruits, and rearing activities. Climate-smart agriculture is examined and compared with traditional forms (Agriculture 1.0) regarding efficiency and waste reduction. The chapter discusses benefits, limitations, future directions, and potential development of intelligent farming technologies and IoT (AI-enhanced tools) to make farming more accessible, convenient, and precise, with reference to different countries and their agricultural advancements. Finally, the chapter acknowledges technological limitations and other factors affecting the growth of healthy farming systems. This chapter contributes to understanding AI-enabled IoT in transforming contemporary agriculture through data-driven insights and automation capabilities.
Keywords
Download Options
Introduction
Zero hunger and good health and well-being are Sustainable Development Goals (SDGs) that can be secured through continuous food production and accessibility. Agriculture is the primary and oldest industry in the world. In India, 18% of gross domestic product comes from agriculture, of which approximately 57% originates from rural areas (Reddy and Dutta, 2018). Advancement in agricultural technology with socio-ecological sustainability is depicted across distinct eras: Agriculture 1.0 (pre-industrial era; 1784-1870), Agriculture 2.0 (era of industrial revolution; use of mechanized tools; 1950- 1992), Agriculture 3.0 (automation; 1992-2017), Agriculture 4.0 (smart farming; current decade), and Agriculture 5.0 (era of Internet of Things) (Holzinger et al., 2024; Thilakarathne et al., 2025).
The advent of Internet of Things (IoT) based technologies has transformed every industry including agriculture, automating control and monitoring of most aspects of traditional farming. Climate change and greenhouse gas (GHG) emissions are significant challenges in agriculture. Climate-smart agriculture offers a way to address both challenges simultaneously, ensuring adequate food production while protecting the environment. In essence, climate-smart farming aims to make agriculture more sustainable and successfully adaptable to changing climate and other stressors. Public and private authorities are increasingly investing in AI-based initiatives to incentivize traditional agriculture practices through smart technologies and tools.
Conclusion
Internet of Things (IoT) and artificial intelligence are transforming agriculture across the globe, offering unprecedented opportunities for precision farming, resource optimization, and sustainable intensification. From soil preparation to harvest, from crop farming to livestock management, from pest detection to carbon sequestration, smart technologies are reshaping every aspect of agricultural production.
The evolution from Agriculture 1.0 to Agriculture 5.0 represents a paradigm shift in how food is produced. Traditional farming methods, while still prevalent in many regions, are increasingly being complemented or replaced by data-driven, automated systems that enhance efficiency, reduce waste, and improve resilience to climate change. Climate-smart agriculture, enabled by IoT and AI, offers a pathway to address the dual challenges of food security and environmental sustainability.
Key applications reviewed in this chapter demonstrate the breadth and depth of technological integration in modern agriculture. Smart irrigation systems optimize water use, reducing both consumption and costs. Remote sensing and UAVs provide real-time crop health monitoring, enabling early intervention against pests and diseases. IoT-enabled livestock tracking improves animal welfare and productivity. AI-powered predictive analytics help farmers make informed decisions about planting, fertilization, and harvesting. Carbon sequestration monitoring contributes to climate change mitigation efforts.
However, significant challenges remain. Data quality and availability, high implementation costs, infrastructure limitations, ethical concerns, and digital divides between developed and developing regions must be addressed to ensure equitable access to these technologies. Policy initiatives like those in India demonstrate governmental commitment to promoting digital agriculture, but more comprehensive and inclusive approaches are needed.
The future of smart farming lies in continued innovation, cross-sector collaboration, and inclusive technology design. As AI algorithms become more sophisticated, sensors more affordable, and connectivity more widespread, the potential for transforming agriculture will only grow. Realizing this potential requires concerted efforts from policymakers, researchers, technology developers, and farming communities worldwide.
In conclusion, investment in smart and sustainable agricultural technologies is not merely an option but a necessity for achieving global food security in the face of climate change, population growth, and resource constraints. IoT and AI, when deployed thoughtfully and equitably, can help create a more resilient, productive, and sustainable agricultural system for future generations.
References
[1] Ahmed, S., & Taylor, R. (2023). Ethical considerations in AI surveillance for conservation. Environmental Ethics, 45(2), 189–204
[2] Anderson, M., Carter, B., & Nguyen, T. (2023). Predicting climate change impacts on biodiversity using AI. Climate Science, 12(4),315–329.
[3] Brown, L., et al. (2023). AI in wildlife monitoring: A bioacoustic approach. Conservation Technology, 18(1), 67–81.
[4] Carter, B., & Nguyen, T. (2023). The role of AI in species conservation under climate change. Global Ecology, 21(3), 287–301.
[5] Chakraborty, A., & Joshi, R. (2021). AI and environmental monitoring: Challenges and opportunities. Ecological Informatics, 56(1), 45–63.
[6] Shivaprakash, K. N., Swami, N., Mysorekar, S., Arora, R., Gangadharan, A., Vohra, K., Jadeyegowda, M., & Kiesecker, J. M. (2022). Potential for Artificial Intelligence (AI) and Machine Learning (ML) applications in biodiversity conservation, managing forests, and related services in India. Sustainability, 14(12), 7154. https://doi.org/10.3390/su14127154
[7] Olawade, D. B., Wada, O. Z., Ige, A. O., Egbewole, B. I., Olojo, A., & Oladapo, B. I. (2024). Artificial intelligence in environmental monitoring: Advancements, challenges, and future directions. Hygiene and Environmental Health Advances, 12, 100114. https://doi.org/10.1016/j.heha.2024.100114
[8] Martinez, R., & Singh, A. (2023). Blockchain applications in forest conservation. Environmental Technology, 15(2), 289–304.
[9] Miller, R., et al. (2021). AI-powered decision-making in conservation. Decision Analytics, 6(2), 134–148.
[10] Nguyen, T., et al. (2024). AI in combating illegal logging. Conservation Enforcement, 12(3), 210–225.
[11] Brock, P., et al. (2022). Ethical implications of AI in conservation decision-making. Environmental Ethics, 44(3), 301–318.
[12] Kattenborn, T., et al. (2021). Deep learning in remote sensing for forest monitoring. ISPRS Journal of Photogrammetry and Remote Sensing, 176, 174–187.
[13] Kumar, S., et al. (2022). CNN-based detection of forest pests. Ecological Informatics, 65, 101485.
[14] Li, H., et al. (2021). AI-enhanced GIS applications for forest management. International Journal of Geographic Information Science,35(7), 1348–1372.
[15] Singh, A., et al. (2021). AI for early detection of tree diseases. Forest Pathology, 51(4), e12793.
[16] Tang, Y., et al. (2022). AI-driven carbon sequestration models for forest ecosystems. Global Change Biology, 28(3), 2547–2561.
[17] Zhang, W., et al. (2023). Integrating AI, GIS, and IoT for real-time forest monitoring. Sustainable Forestry, 45(2), 189–206.
[18] Brock, P., et al. (2022). Ethical implications of AI in conservation decision-making. Environmental Ethics, 44(3), 301–318.
[19] Gonzalez, R., et al. (2023). AI-driven approaches for optimizing sustainable logging. Forest Science, 69(1), 50–67.
[20] Heinzel, J., & Koch, B. (2022). AI-based tree species classification using multispectral data. Remote Sensing, 14(5), 1103.
[21] Jain, P., et al. (2020). Machine learning applications for wildfire prediction. Fire Ecology, 16(2), 12–27.
[22] Thilakarathne, N. N., Abu Bakar, M. S., Abas, P. E., & Yassin, H. (2025). Internet of things enabled smart agriculture: Current status, latest advancements, challenges and countermeasures. Heliyon, 11(3), e42136. https://doi.org/10.1016/j.heliyon.2025.e42136
[23] Rajak, P., Ganguly, A., Adhikary, S., & Bhattacharya, S. (2023). Internet of Things and smart sensors in agriculture: Scopes and challenges. Journal of Agriculture and Food Research, 14, 100776. https://doi.org/10.1016/j.jafr.2023.100776
[24] Tyagi, A. K., & Tiwari, S. (2025). Blockchain and digital twin applications in smart agriculture. CRC Press.
[25] Pandey, D. K., & Mishra, R. (2024). Towards sustainable agriculture: Harnessing AI for global food security. Artificial Intelligence in Agriculture, 12, 72–84.
[26] Taseer, A., & Han, X. (2024). Advancements in variable rate spraying for precise spray requirements in precision agriculture using unmanned aerial spraying systems: A review. Computers and Electronics in Agriculture, 219, 108841.
[27] Boateng, S. L., & Boateng, R. (2025). AI and society - Navigating policy, ethics, and innovation in a transforming world. Routledge.
[28] Rizan, N., Balasundram, S. K., Shahbazi, A. B., Balachandran, U., Redmond, R., & Shamshiri, R. (2024). Internet-of-Things for smart agriculture: Current applications, future perspectives, and limitations. Agricultural Sciences.
[29] Laktionov, I., Diachenko, G., Kashtan, V., Vizniuk, A., Gorev, V., Khabarlak, K., & Shedlovska, Y. (2024). A comprehensive review of recent approaches and hardware-software technologies for digitalisation and intellectualisation of open field crop production: Ukrainian case study in the global context. Computers and Electronics in Agriculture, 218, 108712.
[30] Kurz, H. D., Schütz, M., Strohmaier, R., & Zilian, S. S. (Eds.). (2022). The Routledge handbook of smart technologies - An economicand social perspective. Routledge.
[31] Walter de Gruyter. (2021). Internet of Things and machine learning in agriculture. GmbH.
[32] Brasioli, D., Guercio, L., Landini, G. G., & Giorgio de, A. (Eds.). (2025). The Routledge handbook of artificial intelligence and international relations. Routledge.
[33] Gupta, D. K., & Singh, A. K. (Eds.). (2025). GNSS applications in Earth and space observations - Challenges and prospective approaches. CRC Press.
[34] Ahirwar, A., Shukla, P. K., & Bhatt, R. (Eds.). (2023). Intelligent sensor node-based systems applications in engineering and science. Apple Academic Press.
[35] Boutagayout, A., Hamdani, A., & Adiba, A. (2025). Advancing agroecology for sustainable water management: A comprehensive review and future directions in North African countries. Water Conservation Science and Engineering.
[36] Bamhdi, A. M., Manocha, A., Teli, T. A., Masoodi, Z. S., & Reegu, F. A. (Eds.). (2024). Internet of Things applications and technology. CRC Press.
[37] Stephan, T., Punitha, S., Turjman, F. A., & Perumal, T. (Eds.). (2025). AIoT and smart sensing - A comprehensive guide to the next generation of smart devices. CRC Press.
[38] Khang, A., Abdullayev, V., Hahanov, V., & Shah, V. (Eds.). (2024). Advanced IoT technologies and applications in the Industry 4.0 digital economy. CRC Press.
[39] Valencia, J. B., Pineda, V. G., Arias, A. V., & Valencia, J. (2025). Machine learning in sustainable agriculture: Systematic review and research perspectives. Agriculture, 15(2), 187. https://doi.org/10.3390/agriculture15020187
[40] Adamides, G. (2020). A review of climate-smart agriculture applications in Cyprus. Atmosphere, 11(6), 589. https://doi.org/10.3390/atmos11060589
[41] Bissadu, K. D., Sonko, S., & Hossain, G. (2025). Society 5.0 enabled agriculture: Drivers, enabling technologies, architectures, opportunities, and challenges. Information Processing in Agriculture, 12(1), 112–124. https://doi.org/10.1016/j.inpa.2024.05.001
[42] Shanwad, U. K., Patil, V. C., Dasog, G. S., Mansur, C. P., & Shashidhar, K. C. (2002). Global Positioning System (GPS) in precision agriculture. Proceedings of the Asian GPS Conference.
[43] Raparelli, E., & Bajocco, S. (2019). A bibliometric analysis on the use of unmanned aerial vehicles in agricultural and forestry studies. International Journal of Remote Sensing, 40(24), 9070–9083.
[44] Atalla, S., Tarapiah, S., Gawanmeh, A., Daradkeh, M., Mukhtar, H., Himeur, Y., Mansoor, W., Hashim, K. F. B., & Daadoo, M.(2023). IoT-enabled precision agriculture: Developing an ecosystem for optimized crop management. Information, 14(4),205. https://doi.org/10.3390/info14040205
[45] Taseer, A., & Han, X. (2024). Advancements in variable rate spraying for precise spray requirements in precision agriculture using unmanned aerial spraying systems: A review. Computers and Electronics in Agriculture, 219, 108841.
[46] Wang, S., Edupulapati, B., Hagel, J. M., Kwok, J. J., Quebedeaux, J. C., Khasbaatar, A., Baek, J. M., Davies, D. W., Elangovan, K. E., Wheeler, R. M., Leakey, A. D. B., Hill, C. W., Varnavas, K. A., & Diao, Y. (2024). Highly stretchable, robust, and resilientwearable electronics for remote, autonomous plant growth monitoring. Device, 2(4), 100322.
[47] Ahmed, N., & Shakoor, N. (2025). Advancing agriculture through IoT, Big Data, and AI: A review of smart technologies enabling sustainability. Smart Agricultural Technology, 10, 100848.
[48] Delwar, T. S., Mukhopadhyay, S., Kumar, A., Singh, M., Lee, Y., Ryu, J., & Hosen, A. S. M. S. (2025). Real-time farm surveillanceusing IoT and YOLOv8 for animal intrusion detection. Future Internet, 17(2), 70.