Estimation of Sensor Data Fusion and Evapotranspiration for Enhanced Performance in Smart Drip and Conventional Irrigation Systems
Authors: *Odo, K.O., Abonyi, D.O., Okoro, C.K. And Omosun, Y.
DOI Info: http://doi.org/10.5281/zenodo.18062111
ABSTRACT
Accurate estimation of crop water requirements is essential for improving irrigation efficiency in modern agriculture. This study examined the role of Sensor Data Fusion (SDF) and evapotranspiration (ETo) estimation in enhancing the performance of a Smart Drip Irrigation System (SDIS). Sensor measurements of soil moisture, soil temperature, and relative humidity for five (5) months (April, 2025 – August, 2025) were integrated to compute monthly SDF values, while ETo was estimated to evaluate crop water demand. Results showed that SDIS consistently achieved lower SDF values (56.992 – 77.062) than the Conventional Irrigation System (CIS), which recorded noticeably higher values (70.472 – 78.544) over the same period. Similarly, SDIS produced lower ETo values (504.97- 523.71 mm/month) compared with the higher CIS values (532.91 – 596.72 mm/month), indicating reduced evaporative losses and improved irrigation precision. These outcomes demonstrated that SDIS more accurately captured real-time field conditions and aligned irrigation scheduling with actual evapotranspiration needs. Overall, the integration of sensor data fusion significantly enhanced ET estimation, minimized water wastage, and supported sustainable water management. The findings affirmed SDIS as a more efficient and adaptive alternative to conventional irrigation systems, supporting its adoption in modern precision agriculture.
Affiliations: Department of Electrical and Electronic Engineering, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria.
Keywords: Sensor Data Fusion, Evapotranspiration, Wireless Sensor Networks, Conventional Irrigation System, Smart Drip Irrigation System
Published date: 2025/12/30
