This repository is the official implementation of the paper published in IEEE Access Titled S. Ahmed, M. B. Hasan, T. Ahmed, M. R. K. Sony and M. H. Kabir, "Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification," in IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3187203..

This branch contains the inference code.

Steps To Run the Code

Step 1: Install Anaconda

Go to the Anaconda Website and choose a Python 3.x graphical installer (A) or a Python 2.x graphical installer (B). If you aren't sure which Python version you want to install, choose Python 3. Do not choose both.

Step 2: Clone the Repository

In order to clone the repository, use the following git command in your command line.

git clone https://github.com/redwankarimsony/project-tomato.git

and then move into the project directory with

cd project-tomato

Step 3: Create a Python Environment

The anaconda virtual environment used to run the code is already exported in the .yml files here. If you are using Linux, use the following code to create the environment. This will create a new environment named tomato

conda env create -f environment_linux.yml

Even you are using the other operating system, use the following command to create the anaconda environment.

conda env create -f environment_all_os.yml

Activate the environment with the following command

conda activate tomato

Step 4: Download the Dataset

In order to download the dataset, run the following script with

python download_dataset.py

To run the inference code, run the follwing python file with the given command.

python inference.py

Repository Structure:

├── config.json
├── dataset_preparation.py
├── dataset.py
├── download_dataset.py
├── evaluate.py
├── inference-config.json
├── inference.py
├── model.py
├── PlantVillage-Tomato
│   ├── All-Tomato
│   ├── Test
│   ├── test.csv
│   ├── test.txt
│   ├── Train
│   ├── train.csv
│   ├── train.txt
│   ├── trash.py
│   ├── Val
│   ├── valid.csv
│   └── valid.txt
├── README.md
├── saved_models
│   ├── MobileNetV1_WithoutCLAHE_NoAug_WithoutDense_ValBest.h5
│   └── MobileNetV2_WithCLAHE_NoAug_WithoutDense_ValBest.h5
├── splits
│   ├── test.txt
│   ├── train.txt
│   └── valid.txt
├── train.py
└── utils.py

Citation Instructions

If you use part of the paper or code, please cite this paper with the following bibtex:

@article{ahmed2022less,  
        author={Ahmed, Sabbir and Hasan, Md. Bakhtiar and Ahmed, Tasnim and Sony, Md. Redwan Karim and Kabir, Md. Hasanul},  
        journal={{IEEE Access}},   
        title={{Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification}},   
        year={2022},  
        volume={},  
        number={},  
        pages={1-1},  
        doi={10.1109/ACCESS.2022.3187203},
        url={https://ieeexplore.ieee.org/document/9810234},
        publisher={{IEEE}}
        }