AI-Driven Smart Irrigation System Using Edge-Based Embedded Controllers

Abstract

This study brings out a new AI-based smart irrigation device aimed at solving the increasing problem of water shortage and poor agricultural procedures using emulated edge-based controllers. This combination of environmental sensing, lightweight machine learning algorithms and actuation on microcontrollers enables the system to do real time, adaptive irrigation scheduling. Their implementation of important parts like Raspberry Pi 4 as an AI inference module and an STM32 microcontroller as an actuator controller accelerates the responsiveness, increases security, and saves energy consumption since there is no need in cloud computing. Using the local climatic and soil moisture data the machine learning model identifies optimum irrigation time and duration looking at various parameters such as temperature, humidity, rain and crop-specific thresholds. The whole decision-making process is automated and carried out at the edge that makes it functional even without interruption in areas with poor or no internet connection. This was done by conducting extensive field tests within the semi-arid agricultural areas to prove the efficacy of the recommended solution. The findings show that there is a considerable gain in the efficiency of resources, with up to 38 percent of reduction of water use and 20 percent of increase in the yield of the crops contrasted to the conventional time-based irrigation schemes. Also, the system averaged a latency of only 34millisecs and consumed power at less than 2.5W which became very handy in deployment in rural areas. TinyML and its compatibility with cheap edge hardware are scalable and sustainable, which creates an efficient path to modernize the irrigation infrastructure in the developing world. This article does not only highlight the revolutionary nature of embedded AI in precision agriculture, but it precondition the further innovative development of the field of data-driven farm management and autonomous farm management. The one suggested is one of the options that could be implemented to achieve environmental sustainability, enhance the productivity of agriculture, and make smart and local decisions to use water intelligently.