Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review.

This systematic review critically evaluates the current state and future potential of real-time, end-to-end smart, and automated irrigation management systems, focusing on integrating the Internet of Things (IoTs) and machine learning technologies for enhanced agricultural water use efficiency and crop productivity. In this review, the automation of each component is examined in the irrigation management pipeline from data collection to application while analyzing its effectiveness, efficiency, and integration with various precision agriculture technologies. It also investigates the role of the interoperability, standardization, and cybersecurity of IoT-based automated solutions for irrigation applications.

Comparative performance assessment of pilot irrigation schemes in Uganda.

Irrigation schemes across sub-Saharan Africa are constructed with the intention of increasing agricultural production to increase food security, reduce poverty and improve economic growth. However, most of these schemes are not performing as expected. This study therefore, diagnosed performance gaps in the pilot irrigation schemes of Mubuku and Doho in Uganda and analysed sustainable improvement options.

Assessing soil erosion risk in a peri-urban catchment of the Lake Victoria basin.

Soil erosion and sedimentation contribute to deteriorating water quality, adverse alterations in basin hydrology and overall ecosystem biogeochemistry. Thus, understanding soil erosion patterns in catchments is critical for conservation planning. This study was conducted in a peri-urban Inner Murchison Bay (IMB) catchment on the northern shores of Lake Victoria since most soil erosion studies in Sub-Saharan Africa have been focused on rural landscapes.