The agricultural sector, a vital industry for human survival and a primary source of food and raw materials, faces increasing pressure due to global population growth and environmental strains. Productivity, efficiency, and sustainability constraints are preventing traditional farming methods from adequately meeting the growing demand for food. Precision farming has emerged as a transformative paradigm to address these issues. It integrates advanced technologies to improve decision making, optimize yield, and conserve resources. This approach leverages technologies such as wireless sensor networks, the Internet of Things (IoT), robotics, drones, artificial intelligence (AI), and cloud computing to provide effective and cost-efficient agricultural services. Smart sensor technologies are foundational to precision farming. They offer crucial information regarding soil conditions, plant growth, and environmental factors in real time. This review explores the status, challenges, and prospects of smart sensor technologies in precision farming. The integration of smart sensors with the IoT and AI has significantly transformed how agricultural data is collected, analyzed, and utilized to optimize yield, conserve resources, and enhance overall farm efficiency. The review delves into various types of smart sensors used, their applications, and emerging technologies that promise to further innovate data acquisition and decision making in agriculture. Despite progress, challenges persist. They include sensor calibration, data privacy, interoperability, and adoption barriers. To fully realize the potential of smart sensors in ensuring global food security and promoting sustainable farming, the challenges need to be addressed.

The present study assessed the household-level biophysical vulnerability to climate variability and extreme events of fish farmers in Kitui East and Kitui Central Sub counties of Kitui County, Kenya. The study employed a descriptive survey design and a total of 60 farmers constituted the sample. Carefully selected indicators for exposure and sensitivity were used to operationalize vulnerability. Principal Component Analysis (PCA) was used to assign weights to the indicators. Biophysical vulnerability indices indicated that Kitui East fish farmers were more vulnerable (3.70) as compared to Kitui Central fish farmers (-0.80). In conclusion, the study revealed a high biophysical vulnerability of fish farmers to climate variability and extreme events in arid and semi-arid lands. Therefore, fish farmers’ access to off-farm income and their adaptive capacity should be a primary target as fish farming stakeholders lack control over environmental factors that increase the biophysical vulnerability of fish farmers.