SMART IRRIGATION SYSTEM FOR CLIMATE RESILIENT AGRICULTURE: A SYTEMATIC LITERATURE REVIEW

Abstract

The water shortage has resulted in climate change and higher levels of climatic variability becoming a key issue in the sustainability and food security of the world. Smart irrigation tools have become a new form of combining the Internet of Things (IoT) technologies, wireless sensor-based networks, remote sensing and machine learning algorithms and streamlining the utilization of water resources and the climate resilience. The systematic review is a mixed-method synthesis of 156 peer-reviewed papers published between 2010 and 2025, which is based upon both bibliometric analysis and qualitative thematic synthesis in estimating the technological development, agronomic performance and obstacles to adoption. The indication of the bibliometric evidence is the geometric increase in the size of the research with the conceptual change of threshold-based automation to the artificial intelligence-based adaptive irrigation systems. Some machine learning models that are highly predictive in nature are the Random Forest, XGBoost, convolutional neural networks (CNNs) and long short-term memory (LSTM) networks whereby the predictive accuracy of the soil moisture is 97 percent in controlled environments. Multipurpose monitoring, which implies the use of ground sensors and satellite-based NDVI analysis, and UAV images, is more efficient in regard to identifying uniformity of irrigation ([?]91% accurateness). It is asserted in Agronomic data as 18- 35 per cent more water-efficient compared to traditional irrigation with little expense to the yield in stress-resistant crops and noticeable decrease in greenhouse gas emissions. Despite the better technology, cost, as regards to the economy, and the unavailability of the infrastructure system and inadequacy of the digital literacy also play a major role in causing large scale adoption, especially among the small holder farmers in the developing regions. The results lead to the urgency of the integrated, AI enhanced irrigation framework in the realization of water management sustainability and climate adaptive farming.