Spectroscopy Sensor and Mobile App and Artificial Intelligence Integration for Real-Time Soil Analysis and Crop Management in Precision Agriculture

This research proposes a major enhancement to a mobile application for agricultural recommendations by adding spectroscopic sensors for accurate detection of soil nutrients. The application, which is based on Firebase and Flutter, effortlessly integrates real-time data from color sensors (or spectroscopic sensors) to give farmers precise insights into the nutrient levels of their soil. The recommendation engine's integration of spectroscopic data requires quick deployment and effective management of real-time updates, which Firebase's server-less architecture provides. An improved recommendation engine powered by TensorFlow and Python and utilizing sensor and historical data is driven by the cross-platform Flutter framework, which offers an easy-to-use user interface. The app's ability to provide thorough soil nutrient assessments is enhanced by the color sensor integration, giving farmers more control over crop selection and nutrient management. Empirical evidence demonstrates enhanced agricultural yields and more efficient use of resources, highlighting the concrete advantages of this improvement. In order to further precision agriculture, this research explores the technical nuances of integrating spectroscopic data into pre-existing machine learning models. This study compares pre-existing machine learning algorithms to identify the most effective model for crop prediction using spectroscopic data. This study offers recommendations for future agrotechnological advancements by emphasizing the complementary roles of mobile applications and sensor technology in empowering farmers and enhancing agricultural sustainability.