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--- |
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license: cc-by-4.0 |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: label |
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dtype: |
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class_label: |
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names: |
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'0': banana_healthy_leaf |
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'1': black_sigatoka |
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'2': bract_mosaic_virus |
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'3': insect_pest |
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'4': moko_disease |
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'5': panama_disease |
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'6': yellow_sigatoka |
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splits: |
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- name: train |
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num_bytes: 30097455.0 |
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num_examples: 700 |
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- name: test |
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num_bytes: 3085091.0 |
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num_examples: 77 |
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download_size: 33186711 |
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dataset_size: 33182546.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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--- |
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**Banana Disease Recognition Dataset** |
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**Introduction:** |
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The Banana Disease Recognition Dataset is a collection of images aimed at facilitating research and development in the field of banana disease detection and classification. This dataset contains a total of 777 images, with 700 images designated for training and 77 images for testing. The dataset encompasses six classes of banana diseases and one class for healthy banana leaves. Each class consists of 100 training images and 11 testing images, ensuring a balanced distribution across classes. |
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**Dataset Overview:** |
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The dataset covers the following classes of banana diseases: |
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1. Black Sigatoka Disease |
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2. Yellow Sigatoka Disease |
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3. Bract Virus |
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4. Insect Pest |
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5. Healthy Leaf |
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6. Moko Disease |
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7. Panama Disease |
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**Source:** |
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The Banana Disease Recognition Dataset was sourced from Kaggle, a prominent platform for sharing and discovering datasets, under the URL: [Banana Disease Recognition Dataset](https://www.kaggle.com/datasets/sujaykapadnis/banana-disease-recognition-dataset). This dataset provides a valuable resource for researchers, developers, and enthusiasts interested in the detection and classification of banana diseases. |
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**Dataset Usage:** |
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Researchers and developers can utilize this dataset for various tasks, including but not limited to: |
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- Training and evaluating machine learning models for banana disease recognition. |
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- Benchmarking different algorithms and techniques for disease detection and classification. |
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- Conducting experiments to enhance the accuracy and robustness of disease detection systems. |
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- Exploring innovative solutions to address the challenges in banana farming caused by diseases. |
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**Importance of Studying Plant Diseases:** |
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Studying plant diseases, especially those affecting bananas, holds significant importance for several reasons: |
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1. **Economic Impact:** Banana diseases can cause substantial losses in agricultural productivity, affecting the livelihoods of farmers and economies dependent on banana cultivation. |
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2. **Food Security:** Bananas are a staple food for millions of people worldwide, and diseases affecting banana crops can threaten food security and nutrition. |
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3. **Environmental Sustainability:** Effective disease management strategies can contribute to sustainable agriculture by reducing the need for chemical pesticides and promoting eco-friendly farming practices. |
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4. **Biodiversity Preservation:** Preserving banana diversity is crucial for maintaining genetic resources and biodiversity, which are essential for breeding disease-resistant varieties and ensuring long-term crop resilience. |
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In conclusion, the Banana Disease Recognition Dataset offers a valuable resource for advancing research and innovation in the field of plant pathology, with a focus on banana diseases. By studying and addressing the challenges posed by banana diseases, we can work towards enhancing agricultural sustainability, food security, and economic prosperity for communities around the globe. |
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**References** |
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Mafi, Md Mafiul Hasan Matin; Sifat, R.M.; Moazzam, Md. Golam Moazzam; Uddin, Mohammad Shorif (2023), “Banana Disease Recognition Dataset”, Mendeley Data, V1, doi: 10.17632/79w2n6b4kf.1 |
