Plant Recommendation System Using Smart Irrigation Integrated with IoT and Machine/Deep Learning

Abstract

ABSTRACT Agriculture plays a pivotal role in the economy of most countries, serving as a primary source of livelihood and sustenance. In the case of India, it occupies a substantial portion of the nation’s land. This article proposes the integration of IoT (Internet of Things) and an automated irrigation system with ML/DL (Machine Learning and Deep Learning) to revolutionize agriculture. The implementation of crop monitoring through sensors not only eases the burden on farmers but also enhances crop productivity. The system, at its core, monitors crucial field parameters such as soil moisture, temperature, and humidity. Given the increasing importance of efficient water management in agriculture, this study outlines an automated irrigation system that leverages cloud computing and IoT to curtail water consumption. Its primary objective is to gather and consolidate data from diverse sources, including data generated by sensors and IoT devices. This centralized data storage approach facilitates seamless data integration from various locations and devices. Through the application of algorithms and dataset analysis, the study determines that the cultivation of “Spider” plants is more favorable when compared to other plant species. Notably, the Random Forest classifier emerged as the most accurate, achieving an impressive 94.77% accuracy rate in this project. In essence, this research endeavors to propel agriculture into a technology-driven and sustainable future, optimizing water usage and improving crop yield.