Integrated Smart Agriculture System for Crop Health, Nutrient Balance, and Harvest Efficiency

Abstract

Agriculture plays a crucial role in ensuring global food security and economic stability. However, traditional farming practices often face challenges such as inefficient water usage, delayed disease detection, and improper nutrient management. These issues reduce crop productivity and increase farming costs. To address these problems, this research proposes an Integrated Smart Agriculture System that combines Internet of Things (IoT) technology and machine learning techniques. The system collects real-time environmental and soil data using sensors deployed in agricultural fields. Parameters such as soil moisture, temperature, humidity, and nutrient levels are continuously monitored. The collected data is analyzed using the Random Forest machine learning algorithm to predict crop health conditions and provide appropriate farming recommendations. The proposed system helps farmers make data-driven decisions related to irrigation, fertilization, and harvesting. Experimental results demonstrate that the system improves crop yield, optimizes resource usage, and supports sustainable agriculture. The proposed system provides a scalable and cost-effective solution for modern precision agriculture.