AgriSense IoT Crop Analytics & Microclimate Engine

A four-member engineering capstone team was tasked with implementing an end-to-end hardware-to-cloud precision agriculture prototype. They faced significant hurdles bridging ESP32 microcontrollers with cloud broker software, suffered from packet loss over unstable Wi-Fi, and lacked the mathematical and software rigor to present ML regression models for evapotranspiration. Moreover, their university evaluation committee mandated a 120-page IEEE 830 format project report, complete UML data flow diagrams, and a rigorous Viva Voce oral defense that the students felt unprepared to deliver.

ARdevlopers architected a complete IoT telemetry and analytics pipeline. We configured ESP32 microcontrollers flashed with MicroPython to capture soil moisture, ambient humidity, temperature, and UV radiation, transmitting telemetry packets via lightweight MQTT to a high-performance Python FastAPI broker. We built an interactive, responsive React web dashboard with real-time Chart.js visual telemetry, historical sensor anomaly heatmaps, and a scikit-learn regression model that forecasts soil drying rates 48 hours in advance. Alongside the code, we delivered an exhaustive, university-formatted IEEE thesis report, viva cheat-sheets, and conducted mock defense coaching sessions.