IoT-Enabled Predictive Modelling Of Atmospheric And Soil Conditions Using AgroTS Dataset

Abstract

The evolution of agricultural practices has seen a significant transformation from traditional methods to data-driven approaches. Historically, agriculture relied heavily on manual observations and experience-based techniques to predict atmospheric and soil conditions. The AgroTS dataset emerged as a critical resource, containing vital information on various atmospheric and soil parameters, paving the way for more precise agricultural practices and resource management. The primary objective of this study is to develop a predictive modeling system that leverages IoT technology to analyse atmospheric and soil conditions, enhancing decision- making in agriculture. By utilizing the AgroTS dataset, the study aims to improve the accuracy of predictions related to crop yield, soil health, and climate impact, ultimately contributing to sustainable agricultural practices. Traditional agricultural systems primarily relied on manual labor and experience, involving physical observation of weather patterns and soil conditions. Farmers depended on almanacs and historical data for decision-making, often leading to inefficiencies and unpredictability in crop yields. Before adopting machine learning approaches, traditional methods of assessing atmospheric and soil conditions were often inaccurate and inefficient. Farmers faced challenges in predicting weather patterns and soil quality, resulting in suboptimal crop management decisions and reduced yields. The lack of real-time data and advanced analytics limited farmers ability to respond promptly to environmental changes, leading to resource wastage and lower productivity. The proposed system will consist of an integrated IoT framework that includes a network of sensors deployed in agricultural fields to monitor atmospheric parameters such as temperature, humidity, and rainfall, as well as soil conditions like moisture and pH levels. This data will be collected in real-time and transmitted to a central database for analysis. A user-friendly interface will allow farmers to visualize data trends and receive predictive insights to guide their farming practices, ultimately optimizing crop management and resource allocation.