Development of a Web Platform for Precision Agriculture Optimization

Abstract

This study presents the development of a low-cost web-based platform designed to improve precision agriculture in the sugarcane sector through real-time monitoring of key environmental variables. The proposed system aims to address the technological gap in rural agricultural settings by integrating accessible and open-source technologies. The methodology involved the use of temperature, humidity, and rainfall sensors connected to an Arduino microcontroller, which transmits the collected data via HTTP to a MySQL database. A web interface, developed using HTML, CSS, and PHP, enables users to remotely visualize environmental conditions through graphical and numerical formats. The platform was implemented and tested in a sugarcane cultivation environment to evaluate its performance in terms of responsiveness, stability, and usability. Results from field testing indicated a latency of less than three seconds in data transmission, with an automatic update frequency of approximately one minute. Additionally, a significant reduction in data retrieval time was observed, along with improved accuracy in decision-making due to the real-time visualization of critical parameters. The entire solution was developed with cost efficiency in mind, reaching an estimated implementation cost of approximately $500, significantly lower than traditional commercial systems. The proposed platform not only automates data acquisition and visualization but also lays the foundation for future integration with artificial intelligence algorithms to enable predictive analysis and advanced decision support. This system offers a scalable, adaptable, and affordable tool to support small and medium-sized producers in rural areas.