Precision agriculture predictive modeling and sensor analysis for enhanced crop monitoring

Abstract

This research investigated the application of the Internet of Things (IoT) in precision agriculture and crop monitoring through a two-fold research methodology. A simulated dataset was generated, mirroring real-world IoT sensor readings of soil temperature, salinity level, soil moisture, and conductivity. Employing the Pandas and Matplotlib libraries in Python facilitated exploratory data analysis, including time series analysis through line plots to illustrate temporal variations in these critical parameters. The study then explored the evaluation of predictive models for soil moisture levels, extending the dataset to include simulated predicted values. Performance metrics such as Mean Squared Error (MSE) and R-squared (R2) were computed using the sci-kit-learn library, providing a comprehensive evaluative framework. Visual representations of actual versus predicted soil moisture levels, accompanied by the analysis of residuals, offered nuanced insights into the model’s efficacy. The results highlight dynamic variations in soil temperature, salinity, soil moisture, and conductivity, emphasizing the importance of continuous monitoring in precision agriculture. Fluctuations observed in these parameters are attributed to climatic conditions, agricultural practices, and soil properties. The study contributes valuable insights for stakeholders, emphasizing the significance of IoT technologies in providing actionable data for sustainable and adaptive farming practices. The visual representations offer practical tools for decision-making, while the performance evaluation of predictive models enhances the reliability of data-driven approaches in agriculture. The findings presented herein contribute to the ongoing discourse on precision agriculture, emphasizing the role of accurate predictions for efficient resource utilization and improved crop yield.