Final Year Major Project
Autonomous Drone Disease Classifier - Final Year B.Tech Project
Developed an autonomous drone payload that detects leaf spot diseases in rubber plantations with 98.4% validation accuracy, earning 1st prize in the University Project Expo.
Organization: Government Engineering College
Rating: 5 / 5.0
Status: Verified Production Delivery
The Requirement & Challenge
The student team needed high-accuracy edge AI inference on a lightweight single-board computer with real-time video streaming to a ground mobile application.
The FairS IT Solutions Engineering Architecture
FairS IT Solutions mentored the students in training a quantized MobileNetV3 model, building the drone gimbal mount, and writing the Flutter telemetry dashboard.
Key Highlights & Metrics
98.4%
Accuracy Score
32 FPS
Inference Speed
A+ (University 1st Prize)
Project Grade
Technology Stack
Python
TensorFlow Lite
Raspberry Pi 4
Flutter
ROS