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Technical Case Study

From CNN to YOLO: Improving Crop Disease Detection with Object Detection

The Core Objective

Baseline CNN model struggled to capture contextual image features, limiting accuracy.

01.Engineering Approach

Transitioned to YOLO-based object detection to better localize and classify crop diseases.

02.System Architecture

YOLO deep learning model deployed via Flask backend, integrated with a React frontend for real-time predictions.

03.Lessons Learned

Object detection models outperform traditional CNNs for context-heavy visual tasks.
Model architecture selection can impact accuracy more than hyperparameter tuning.