A DATA-DRIVEN APPROACH TO VERTICAL HARVESTING: ARTIFICIAL INTELLIGENCE AND INTERNET OF THINGS-BASED AUTOMATION IN AGRICULTURE

Abstract

Smallholder farmers cultivating limited landholdings often experience low yields and reduced returns, as restricted plot sizes limit crop rotation and expansion. Vertical farming provides a practical alternative by enabling intensive production in compact and densely populated areas, supporting year-round cultivation and increased food output. Artificial Intelligence (AI) and the Internet of Things (IoT) play a central role in modern agriculture by enabling precision farming and data-driven decision-making. The proposed system integrated soil-based, hydroponic, and aeroponic techniques within a unified vertical framework. Advanced irrigation and monitoring technologies, including moisture sensors and image-based crop analysis, optimized water usage, nutrient delivery, and crop health management. The approach automated irrigation and nutrient control and achieved a disease detection accuracy of 96 %, demonstrating improved performance compared to conventional machine learning models. Real-time monitoring through sensors and imaging devices reduced manual intervention, improved resource utilization, and supported sustainable agricultural practices. Overall, the system enhanced productivity across diverse farming conditions while promoting resource efficiency, sustainability, and climate resilience.