# PriBeL-Net: Extending betel leaf dataset with CNN-based image classification

## TL;DR

This paper compares deep learning models for classifying betel leaves in agriculture, finding DenseNet121 most reliable in real-world conditions.

## Contribution

The novel contribution is evaluating CNN models on a custom betel leaf dataset under controlled and field conditions to identify the most dependable model.

## Key findings

- DenseNet121 outperformed others in field environments with good accuracy and F1-score.
- MobileNetV2 and ResNet50V2 performed best under controlled conditions.
- EfficientNetB0 showed limitations in handling noisy, real-world datasets.

## Abstract

Deep learning is core to precision agriculture. In this research, the authors compare four deep-learning frameworks: MobileNetV2, EfficientNetB0, ResNet50V2, and DenseNet121, on a custom dataset under controlled and on-field environments.

Whereas MobileNetV2 and ResNet50V2 gave the best results on the controlled setup, robust to light variations, backgrounds, and different leaf orientations, DenseNet121 gave better results in field environments with good accuracy and F1-score.

However, EfficientNetB0 was not up to the mark, implying the restrictions of light-weight models while working with noisy, real-world datasets.

With such implications, DenseNet121 is reported to be the most dependable candidate for application in agriculture.

In the next phase, adaptation and rearrangement of DenseNet121’s architecture and parameters will be taken up to better its performance and its ability for adaptation under diverse agricultural conditions.

Image, graphical abstract