AI-Driven Agricultural Monitoring Application

The client needed a solution to help farmers monitor large areas of crops efficiently. Traditional manual inspection methods were time-consuming and often missed early signs of problems. They wanted to leverage satellite imagery and weather data to provide actionable insights, but lacked the technical infrastructure to process this data at scale. The solution needed to work in rural areas with limited connectivity and be accessible to users with varying levels of technical proficiency.

We created a comprehensive monitoring system that includes: - Integration with satellite imagery APIs to regularly capture field data - Custom machine learning models trained to detect crop stress, disease patterns, and growth anomalies - A progressive web application that works offline and syncs when connectivity is available - Automated alert system for critical conditions like drought stress or potential pest outbreaks - Integration with weather forecast APIs to provide preventative recommendations The solution uses Google Cloud Platform for the ML infrastructure and data processing, with a React Native mobile application that farmers can use in the field.