Update README.md
Browse files
README.md
CHANGED
|
@@ -5,6 +5,8 @@ license: apache-2.0
|
|
| 5 |
|
| 6 |
This repository contains the implementation of the lightweight convolutional neural network architecture described in the paper "Advancing Real-Time Crop Disease Detection on Edge Computing Devices using Lightweight Convolutional Neural Networks."
|
| 7 |
|
|
|
|
|
|
|
| 8 |
## Overview
|
| 9 |
|
| 10 |
This project introduces LARS-MobileNetV4, an optimized version of MobileNetV4 specifically designed for real-time crop disease detection on resource-constrained edge devices such as Raspberry Pi. Our implementation achieves 97.84% accuracy on the Paddy Doctor dataset while maintaining fast inference times (88.91ms on Raspberry Pi 5), making it suitable for deployment in agricultural field settings.
|
|
|
|
| 5 |
|
| 6 |
This repository contains the implementation of the lightweight convolutional neural network architecture described in the paper "Advancing Real-Time Crop Disease Detection on Edge Computing Devices using Lightweight Convolutional Neural Networks."
|
| 7 |
|
| 8 |
+
https://github.com/lars-uav/LARS-MobileNet-V4
|
| 9 |
+
|
| 10 |
## Overview
|
| 11 |
|
| 12 |
This project introduces LARS-MobileNetV4, an optimized version of MobileNetV4 specifically designed for real-time crop disease detection on resource-constrained edge devices such as Raspberry Pi. Our implementation achieves 97.84% accuracy on the Paddy Doctor dataset while maintaining fast inference times (88.91ms on Raspberry Pi 5), making it suitable for deployment in agricultural field settings.
|