Deep Learning-Based Automated Identification of Bean and Gourd Crop Diseases.

Description

This dataset contains raw RGB images of bean and gourd plant leaves prepared for automated plant disease classification research using deep learning methods. The dataset was developed to support the study: “Deep Learning-Based Classification of Bean and Gourd Plant Diseases Using Transfer Learning” The dataset includes images representing disease and healthy categories of bean and gourd crops organized into class-specific folders. Most images were collected directly from field environments by the authors. To improve disease coverage and class representation, a limited portion of selected classes was incorporated from publicly available agricultural datasets. Referenced external dataset: Mojumdar, Mayen Uddin; Dip, Supta Das; Alahi, Tabib E.; Tonmoy, Sheikh (2025), Bean Health and Disease Dataset: A Resource for Agricultural Research, Mendeley Data, V3, DOI: 10.17632/3km9d246z2.3 The final dataset was manually verified, cleaned, organized, and curated by the authors. Dataset Information: • Total Images: 4,155 • Number of Classes: 10 • Image Type: RGB • Format: Raw images (no train/validation/test split) Intended applications include plant disease classification, transfer learning, agricultural computer vision, and deep learning research.

Steps to reproduce

1. Download and extract the dataset. 2. Organize images using the provided class-wise folder structure. 3. Load images using image processing libraries (TensorFlow, Keras, PyTorch, OpenCV, etc.). 4. Apply preprocessing according to experimental requirements (resizing, normalization, augmentation if needed). 5. Prepare train, validation, and test subsets as required. 6. Train classification models using selected deep learning architectures. 7. Evaluate model performance using standard classification metrics.

Institutions

• Daffodil International University

  Dhaka Division, Dhaka

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