Agricultural Product Classification Using Machine Learning and Deep Learning

This repository provides implementations of state-of-the-art machine learning and deep learning algorithms for classifying agricultural products. The project employs advanced techniques to achieve high accuracy and robustness in classification tasks. The methods are categorized as follows:

• Pixel-based Machine Learning Models
• Pixel-based Deep Learning Models
• Pixel-block Based Deep Learning Models

This project was carried out at the Telecommunications Research Center of Iran under the supervision of experienced professors and industry experts.

Table of Contents

1. Introduction
2. Features
3. Installation
4. Usage
5. Methods
   • Pixel-based Machine Learning Models
   • Pixel-based Deep Learning Models
   • Pixel-block Based Deep Learning Models
6. Contributors
7. License

Introduction

The accurate classification of agricultural products is vital for improving productivity and automating agricultural processes. This repository explores the latest machine learning and deep learning algorithms to address classification challenges at different levels of granularity.

Features

• Comprehensive implementation of pixel-based and pixel-block-based models.
• Use of cutting-edge machine learning and deep learning techniques.
• High accuracy achieved through advanced optimization and training strategies.
• Modular and extensible design for easy integration and customization.

Installation

To get started with the project, clone the repository and install the required dependencies:

git clone https://github.com/your-repo/agriculture-product-classification.git
cd agriculture-product-classification
pip install -r requirements.txt

Usage

Run the following command to train a model:

python train.py --model [model_type] --data [path_to_data]

For evaluation:

python evaluate.py --model [path_to_model] --data [path_to_data]

Methods

Pixel-based Machine Learning Models

These models use pixel-level information as input features to train traditional machine learning algorithms such as:

• Support Vector Machines (SVM)
• Random Forests
• Gradient Boosting Machines

Pixel-based Deep Learning Models

Deep learning models that operate directly on pixel-level data, leveraging architectures like:

• Convolutional Neural Networks (CNNs)
• Fully Convolutional Networks (FCNs)

Pixel-block Based Deep Learning Models

These models process blocks of pixels, aggregating local spatial information to improve classification. Techniques include:

• Patch-based CNNs
• Transformer-based architectures

Contributors

This project was developed by a team of researchers and practitioners at the Telecommunications Research Center of Iran, guided by academic and industry experts.

License

This repository is licensed under the MIT License. See LICENSE for details.