Smart Fertilization System with IoT using Machine Learning Algorithm

Abstract

Given its status as an agricultural powerhouse, India's economy heavily relies on agricultural yield growth and related agro-industrial products. However, the sector faces significant challenges due to unpredictable rainfall and varied soil conditions affecting parameters like nitrogen, phosphorous, potassium, crop rotation, soil moisture, and surface temperature. This project proposes a Smart Fertilization System (SFS) integrating Internet of Things (IoT) technology and machine learning algorithms to optimise crop yield while conserving resources. The SFS utilizes IoT to gather real-time data from field sensors monitoring soil and weather conditions. These data are processed centrally using regression and classification models to derive actionable insights. The system accurately predicts optimal fertilization schedules tailored to specific crops and soil types by continuously analysing historical data. This predictive capability boosts crop productivity and promotes sustainable agriculture by minimizing fertilizer overuse and reducing environmental impact. Implementing the SFS has the potential to revolutionize traditional farming practices by providing farmers with data-driven decision support. This system represents a pivotal step toward achieving efficient and sustainable agricultural practices in India and beyond by harnessing IoT and machine learning.