The primary outcomes of this research include the development of a cost-effective Arduino-
based irrigation control system capable of achieving water savings of 25-40% compared to
conventional methods, implementation of real-time soil parameter monitoring with accuracy
levels suitable for precision agriculture applications, and establishment of automated decision-
making algorithms that respond to environmental conditions without human intervention.
Additionally, the study provides comprehensive guidelines for system scalability, maintenance
protocols, and integration with emerging technologies such as IoT and machine learning
frameworks. The research contributes to the growing body of knowledge in agricultural
automation while addressing practical implementation challenges faced by farmers and
agricultural engineers