Development of a decision support system for supplementary drip irrigation using soil moisture sensor method

Abstract

The global population, currently at 8.1 billion, is projected to reach 9.7 billion by 2050, intensifying the global demand for food. Africa, particularly Uganda, is experiencing rapid population growth, with Uganda’s population reaching 45.9 million in 2024 and agriculture remaining the cornerstone of its economy. Despite 80% of Uganda's land being arable, only 35% is under cultivation. Agriculture contributes significantly to GDP and employment but remains heavily reliant on rain-fed systems, which are increasingly threatened by climate-induced droughts and floods. Irrigation has proven essential in boosting agricultural productivity, with irrigated lands contributing up to 40% of global food output. However, poor irrigation practices in Uganda have led to waterlogging, inefficient water use, and declining crop yields. Although the government has promoted various irrigation systems through the National Irrigation Policy and Vision 2040, improper application continues to undermine food security and sustainable resource management. This study proposes the development of a precision decision support irrigation system that utilizes real-time data from soil moisture, temperature, and humidity sensors to guide irrigation intervals, depth, and water quantity. Such a system promises improved crop productivity, water-use efficiency, and alignment with Sustainable Development Goals (SDG 2 and SDG 12), thereby supporting Uganda’s long-term agricultural resilience and food security.