A TinyML-based System For Smart Agriculture
Vasileios Tsoukas, Anargyros Gkogkidis, Athanasios Kakarountas
Abstract
Agriculture is a high-priority sector since it creates economic opportunities and generates the majority of the world’s food. In 2050, agricultural products will be in exceptionally high demand due to a 30% increase in the world’s population. Human resources for agriculture development are disappearing as young people migrate to major cities, while agricultural land is being abused for rapid expansion. To satisfy the food demand, the major portion of agricultural tasks must be automated. Agricultural research has revealed that the Internet of Things (IoT) technology might be the future of modern and automated agriculture research. The aforementioned technology faces a number of common obstacles, including internet requirements, data transfer, and privacy concerns. TinyML is an emerging technology that delivers low-cost and highly efficient devices capable of locally running complex machine learning models and neural networks, while overcoming the majority of the aforementioned IoT issues. As a comprehensive solution for autonomously watering plants, a TinyML-based system capable of monitoring ambient conditions and plants’ soil moisture is presented in this work.