Image dataset of ten durian diseases captured in real-field conditions from a family orchard in Vinh Long, Vietnam.

Abstract

This dataset comprises 5452 images of durian plant parts-including leaves, flowers, branches, stems, and roots-affected by ten common disease classes. The images were captured from one family-owned durian orchard and four nearby orchards in Vinh Long Province, Vietnam. Each class contains approximately 405-427 raw images, photographed using an iPhone 14 under natural field conditions. These conditions simulate typical farmer photography practices, featuring varied angles, inconsistent lighting, and complex environmental backgrounds, resulting in significant visual noise. All raw JPEG images were manually reviewed and cropped on macOS systems using MacBook devices equipped with Apple M4 chips to focus on disease-affected regions, reduce file size, and minimize background noise. The processed, cropped images are provided in PNG format with variable dimensions. Images were resized to 224×224 pixels only during model training for machine learning experiments. Disease symptoms were verified in collaboration with plant pathologists to ensure accurate classification. This dataset is publicly available on Mendeley Data and is suitable for developing and evaluating machine learning models in plant disease classification. It is particularly valuable for testing model performance under real-world, noisy conditions and for supporting the creation of mobile or edge-based diagnostic tools in agriculture.

Plant phenotyping relevance

植物病徴を画像で直接捉えた公開データセットで、植物病害状態の分類モデル開発・評価を主目的とするため、表現型計測データセットとして中心的です。

Code and data availability

The paper is a Data in Brief describing a public durian disease image dataset (5452 field images, ten classes) deposited on Mendeley Data with an explicit DOI and direct URL, matching an allowed URL. This is a paper-specific, publicly available image dataset directly reproducing the paper's phenotyping measurements. No