Banana Leaves Imagery Dataset.

Abstract

In this work, we present a dataset of banana leaf imagery, both with and without diseases. The dataset consists of 11,767 images, categorized as follows: 3,339 healthy images, 3,496 images of leaves affected by Black Sigatoka and 4,932 images of leaves affected by Fusarium Wilt Race 1. This data was collected to support machine learning diagnostics for disease detection. The data collection process involved farmers, researchers, agricultural experts and plant pathologists from the northern and southern highland regions of Tanzania. To ensure unbiased representation, farms were randomly selected from the Rungwe, Mbeya, Arumeru, and Arusha districts, based on the presence of banana crops and the targeted diseases. The dataset offers a comprehensive collection of images captured from November 2022 to January 2023, using a high-resolution smartphone camera across a wide geographical area. Researchers and developers can use this dataset to build machine learning solutions that automatically detect diseases in images, potentially enabling agricultural stakeholders, including farmers, to diagnose Fusarium Wilt Race 1 and Black Sigatoka early and take timely action.

Plant phenotyping relevance

バナナ葉の病徴画像を収録した再利用可能なデータセットで、植物の病害状態を画像から推定するフェノタイピング基盤として中心的です。

Code and data availability

The paper describes a public banana leaf imagery dataset deposited in Zenodo (10.5281/zenodo.7670326), which is a paper-specific, publicly available phenotyping asset. However, no Zenodo URL appears in the allowed_urls list, so no actionable asset with a permitted URL can be recorded. The only code-related links in the

No evidence-backed public reproduction asset is currently recorded.