Towards Sustainable Farming: Leveraging AIoT for Precision Water Management and Crop Yield Optimization

Abstract

Sustainable Development Goals (SDGs) by the United Nations have Zero Hunger and Responsible Consumption and Production as the two significant agriculture-related components. Conventional irrigation control systems, relying heavily on manual labour, exhibit shortcomings in minimizing farmer input dependency. Recognizing the critical interdependence of water resource management, crop yield optimization, and environmental preservation, this work contends that precision in water management is essential. While efforts to reuse treated wastewater offer potential water conservation solutions, they often compromise crop yield. Precisely calculating water requirements can enhance crop yield, reduce human involvement, eliminate potential decision-making errors, and address water conservation issues. Automating tasks like irrigation demands comprehensive knowledge of crop and soil characteristics, climate conditions, time factors, and physical parameters like temperature, soil moisture, and humidity. This research addresses the limitations of current agricultural practices by advocating for a paradigm shift towards sustainable farming through the integration of Artificial Intelligence of Things (AIoT). The proposed framework leverages the fusion of the Artificial Intelligence (AI) and Internet of Things (IoT) to automate irrigation by employing a comprehensive analysis of diverse parameters. Through empirical evaluation, this work demonstrates that AIoT-based precision water management not only improves crop yield but also reduces human intervention, addressing water conservation challenges, and fostering sustainable agriculture practices.