Data-Driven Soil Fertility Classification Using IoT Sensing and Logistic Regression Model

Abstract

The evaluation of soil fertility is crucial for maximizing agricultural output and promoting sustainable crop management.  Traditional approaches are laborious, expensive, and inadequate for real-time surveillance.  This research introduces a data-driven classification method for soil fertility that integrates Internet of Things (IoT) sensors with a Logistic Regression (LR) model.  IoT-enabled sensors gather essential soil factors, including moisture, temperature, pH, and nutrient levels, relaying the information to a cloud-based processing unit. The dataset is preprocessed, and features are extracted before training the LR classifier to classify soil fertility levels. The model is selected for its efficacy, clarity, and appropriateness for both binary and multiclass classification.  Experimental validation using real-time field data shows significant accuracy and little processing delay, facilitating prompt and informed decision-making.  The proposed approach enhances precision agriculture by delivering actionable information, minimizing resource waste, and fostering sustainable agricultural practices.