Intelligent Water Management in Precision Agriculture Using Machine Learning

Abstract

India's economy heavily relies on agriculture, with a significant portion of the population engaged in agriculture-based businesses. Automation in agriculture holds the potential to enhance crop production quality and quantity while reducing resource consumption. However, the high investment costs associated with agricultural automation present a major challenge. To address this, a Machine Learning (ML) technique is proposed to optimize the costs associated with automating agricultural irrigation systems.The proposed ML model aims to reduce the implementation and operational costs of the remote sensor network used for irrigation. A dataset comprising soil moisture and temperature data was utilized, with the irrigation treatment type serving as the target variable. The dataset underwent preprocessing to ensure suitability for learning, followed by the application of k-means clustering for behavioral data analysis. This clustering technique grouped similar sensor readings, improving learning performance in terms of accuracy and training time.Two machine learning algorithms were then implemented to train the model and predict irrigation treatments, effectively minimizing the cost of deploying and maintaining sensors in the field. While the current system demonstrates significant cost efficiency, its overall performance remains reliant on the accuracy of the prediction model. Future work will focus on further enhancing the prediction accuracy to ensure optimal performance.This study highlights the potential of intelligent water management systems in precision agriculture, demonstrating how machine learning can contribute to sustainable and cost-effective agricultural practices.