AGRICULTURAL ENGINEERING

This study presents the design and fabrication of a Smart Internet of Thing (IoT)-based feul monitoring system for agricultural tractors. The system aims to improve operational efficiency, minimize fuel theft, and enhance real-time decision-making in mechanized farming. It integrates an ultrasonic fuel level sensor, NodeMCU V3microcontroller, GPS, and GSM modules to provide continuous fuel data and location tracking. Using Blynk and Thing Speak IoT platforms, real-time fuel levels, consumption trends, and geographic positions were displayed through web and mobile interfaces. Calibration and testing revealed that the system achieved high measurement accuracy with an error margin of less than ±5%, Wi-Fi data transmission latency between 6–8 seconds, and SM S alert delay of 7–12 seconds. The prototype demonstrated effective performance under field conditions, withstanding vibration, heat, and moisture without data loss. Results confirm that the developed IoT-based system is affordable, reliable, and user-friendly for small- and medium-scale farmers. It enables efficient monitoring of fuel resources, enhances accountability, and supports preventive maintenance through analytics and alert mechanisms. Overall, the system bridges the technological gap in fuel management for agricultural operations in developing regions and contributes to sustainable mechanization practices

Efficient water management is important for sustainable agricultural production, particularly in regions experiencing climatic variability and limited water resources. This study focuses on determining evapotranspiration rates for maize (Zea mays) and rice (Oryza sativa) crops using selected evapotranspiration models under the climatic conditions of Ovia North East LGA, Edo State. Two ET models- the Blaney Morin Nigeria (BMN) and Hagreaves- Samani methods were semployed to estimate reference evapotranspiration (ETo) based on meteorological data obtained from the Nigerian Institute for Oil Palm Research (NIFOR) station. Crop evapotranspiration (ETc) was subsequently derived by applying crop coefficients (Kc) corresponding to the different growth stages. The study compared the performance of both models to evaluate their suitability for local conditions. Results indicated that the BMN model, which uses relative humidity alongside temperature and daylength, produced ET estimates more consistent with humid tropical conditions than the temperature based Hagreaves- Samani model. It was also found that using BMN instead of Hagreaves- Samani model reduces estimated irrigation demand by 85% for both maize and rice, corresponding to water savings of about 8,587 m³/ha and 10,230 m³/ha and approximate energy savings of 390kWh/ha for maize and 456kWh/ha for rice. The findings highlight the importance of using locally calibrated ET models for accurate irrigation scheduling and water resource management. This study provides valuable insights for improving water use efficiency, enhancing crop yield, and promoting climate smart agricultural practices in southern Nigeria.