Agri-vision Bangladesh: A multi-crop augmented image dataset for automated disease diagnosis in Bottle Gourd, Zucchini, Papaya, and Tomato

TL;DR

Agri-vision Bangladesh is a new dataset of augmented images for diagnosing diseases in four crops, helping improve precision agriculture in subtropical regions.

Contribution

The paper introduces a region-specific, expert-validated multi-crop image dataset with augmentation for automated disease diagnosis in subtropical agriculture.

Findings

The dataset includes 28,000 images covering four crops and 28 disease classes.

Augmentation techniques increased the dataset size from 5266 original to 28,000 images.

The dataset is validated by agronomists and standardized for deep learning applications.

Abstract

This article introduces Agri-Vision Bangladesh, a comprehensive, augmented image dataset designed to advance automated disease diagnosis in four economically vital agricultural crops: Bottle Gourd (Lagenaria siceraria), Zucchini (Cucurbita pepo), Papaya (Carica papaya), and Tomato (Solanum lycopersicum). Addressing the scarcity of region-specific agricultural data, a total of 5266 original images were acquired directly from diverse agricultural fields in Bangladesh using a SONY ALPHA 7 II full-frame camera under natural lighting conditions. The dataset encompasses 28 distinct classes, covering a wide spectrum of biotic stressors including viral (Mosaic Virus, Leaf Curl), fungal (Downy Mildew, Anthracnose, Alternaria Blight), bacterial (Bacterial Blight, Xanthomonas), and pest-induced damage (Insect Hole, White Spot), alongside Healthy samples. To ensure scientific reliability, each image underwent a rigorous two-stage validation process by senior agronomists. To tackle class imbalance and facilitate the training of data-intensive Deep Learning models, the dataset was expanded using a Python-based augmentation pipeline incorporating geometric transformations (rotation, flipping) and photometric adjustments (noise, brightness) resulting in a final repository of 28,000 images (5266 original and 22,734 augmented). All files are standardized to 512×512 pixels in JPG format. This expert-validated resource serves as a critical benchmark for developing robust computer vision algorithms (e.g., CNNs, Vision Transformers) for precision agriculture, enabling research into fine-grained classification, object detection, and cross-crop transfer learning in subtropical farming environments.

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