~ 33 ~ ISSN Print: 2664-9926 ISSN Online: 2664-9934 NAAS Rating: 4.82 IJBS 2025; 7(4): 33-37 www.biologyjournal.net Received: 17-02-2025 Accepted: 23-03-2025 BT Suresh Kumar Post Graduate Researcher, Department of Agronomy, SRM College of Agricultural Sciences, SRM Institute of Science and Technology, Chengalpattu, Tamil Nadu, India Dr. Sivakumar K Assistant Professor, Department of Agronomy, SRM College of Agricultural Sciences, SRM Institute of Science and Technology, Chengalpattu, Tamil Nadu, India Sidharth M Nair Post Graduate Researcher, Department of Agronomy, SRM College of Agricultural Sciences, SRM Institute of Science and Technology, Chengalpattu, Tamil Nadu, India Corresponding Author: BT Suresh Kumar Post Graduate Researcher, Department of Agronomy, SRM College of Agricultural Sciences, SRM Institute of Science and Technology, Chengalpattu, Tamil Nadu, India Optimizing micro irrigation efficiency in water-scarce agroecosystems using AI and remote sensing technologies BT Suresh Kumar, Sivakumar K and Sidharth M Nair DOI: https://www.doi.org/10.33545/26649926.2025.v7.i4a.324 Abstract In dryland farming, water shortages require creative irrigation solutions. In dry and semi-arid areas, drip, sprinkler, and subsurface irrigation are becoming more popular to maximize water use. These systems still struggle to distribute water efficiently, use a lot of energy, and make real-time decisions. Using AI and remote sensing to overcome these restrictions is revolutionary. AI-driven irrigation systems use machine learning algorithms, predictive analytics, and IoT-enabled smart sensors to maximize water use, minimize waste, and increase crop yields. UAVs, satellites, and multispectral imaging are remote sensing technologies. These technologies enable real-time soil moisture, evapotranspiration, and plant health monitoring. For site-by-site water management, GIS, AI, and remote sensing improve precise irrigation. This review examines the pros, cons, and future uses of artificial intelligence (AI) for remote sensing and automated irrigation in dryland farming using recent studies. Comparing current studies shows that wide adoption requires better policy frameworks, sensor networks, and AI algorithms. It also identifies key research gaps. The results show that we need better water management technology and approaches from different fields to sustain farming. Future research should expand remote sensing in small-scale farming, incorporate blockchain for data security, and develop affordable AI-driven irrigation models. To implement policies and infrastructure widely, policymakers and stakeholders must collaborate. Researchers, agronomists, and lawmakers who want to improve irrigation efficiency with AI and remote sensing will benefit from this study. Keywords: Micro-irrigation, remote sensing, smart agriculture, precision irrigation, dryland agriculture Introduction Dryland agriculture faces water scarcity due to high evaporation and unpredictable precipitation, making water efficiency essential for long-term crop production. Micro- irrigation using drip and sprinkler systems has become an important water-saving tool. Plant roots receive specific amounts of water directly. In traditional irrigation methods, water is often not distributed efficiently and resources are wasted because of set schedules and monitoring that has to be done by hand. AI and remote sensing have made it possible for dryland farming solutions that are based on data. This has completely changed how irrigation is managed. Irrigation systems that are powered by AI use machine learning algorithms, IoT sensors, and predictive analytics to keep real-time track of the weather, crop water needs, and soil moisture. By making decisions automatically, these smart systems make sure that crops get the right amount of water at the right time and reduce water loss. UAV hyperspectral images, satellite images, and Geographic Information Systems (GIS) all help with precise irrigation by showing how water is spread out in the soil, how healthy plants are, and how quickly plants lose water through evaporation. With AI and remote sensing, you can always keep an eye on things and find ways to save water by changing how you water based on the weather. A new study says that AI and remote sensing can make micro-irrigation work better in dry and semi-dry areas. To save water and make crops last longer, (Wei et al., 2024) [17] weigh the pros and cons of AI-driven irrigation. (Wang et al., 2024) [10] talk about how machine learning and remote sensing can help farmers use data to decide when to water their crops, which makes precision agriculture better. AI can figure out how much water and energy sensor-based micro-irrigation systems should use, as shown by more research like that by (Mohammed et al., 2023) [13] . International Journal of Biology Sciences 2025; 7(4): 33-37