Crop Recommendation System using Machine Learning and IoT

Abstract

In many regions across the globe, agriculture
re- mains the cornerstone of livelihoods, with a
significant portion of the population relying on it as
their primary occupation. The success of agricultural
endeavors hinges greatly on crop production, making it
a crucial aspect of sustenance and economic stability. To
address the challenge of ensuring optimal crop yields, a
cutting-edge solution integrating IoT (Internet of
Things) and ML (Machine Learning) technologies has
emerged. This innovative system employs sensor-based
soil testing to meticulously assess soil conditions,
thereby mitigating the risk of soil degradation and
fostering healthy crop growth. A variety of sensors are
deployed within this system, each tasked with
monitoring specific soil parameters essential for crop
health. These sensors include those for measuring soil
temperature, moisture levels, pH balance, and nutrient
composition (NPK). By continuously gathering data on
these crucial factors, the system builds a comprehensive
understanding of soil dynamics. The collected data is
then transmitted to a microcontroller, where it is
subjected to rigorous analysis utilizing sophisticated
machine learning algorithms such as random forest.
Through this analytical process, the system generates
actionable insights and recommendations tailored to
optimize crop growth conditions. Ultimately, this
integrated IoT and ML system represents a
groundbreaking approach to agricultural management,
empowering farmers with real- time, data-driven
guidance to enhance crop productivity and
sustainability.