UAV-Based Remote Sensing for Early Crop Disease Detection using Machine Learning

1. Engineering Challenge & Problem Statement

Paddy field diseases spread rapidly across large agricultural lands, leading to severe yield reductions and heavy financial losses for smallholder farmers. Conventional diagnostics rely on manual foot surveys (labor-intensive and slow) or multispectral satellite/drone imagery (extremely expensive and requiring specialized post-processing). The primary objective was to build a cost-effective, locally manufactured UAV platform equipped with a standard high-definition RGB camera capable of early crop pathology detection.

2. Hardware & Avionics Architecture

• Custom In-House Flight Controller: Designed a modular multi-layer PCB flight controller integrating an IMU (gyroscope/accelerometer), digital barometer, magnetometer, and GPS receiver.
• Flight Control Algorithms: Implemented tuned PID stabilization loops for roll, pitch, yaw, and barometric altitude holding.
• Airframe & Structural Components: Utilized carbon-fiber frame spars and custom 3D-printed vibration-damped camera mounts and avionics enclosures.

3. Image Processing & Machine Learning Pipeline

Aerial RGB image tiles were captured under calibrated daylight conditions and transformed into visible-spectrum vegetative indices:

A Random Forest classification model was trained on extracted spectral index signatures, demonstrating superior generalization over Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Logistic Regression, achieving 68% accuracy and 73% precision in field trials.

4. Academic Publications & Verification