Dataset of banana leaves and stem images for object detection, classification and segmentation: A case of Tanzania.

Abstract

Banana is among major crops cultivated by most smallholder farmers in Tanzania and other parts of Africa. This crop is very important in the household economy as well as food security since it serves as both food and cash crops. Despite these benefits, the majority of smallholder farmers are experiencing low yields which are attributed to diseases. The most problematic diseases are Black Sigatoka and Fusarium Wilt Race 1. Black Sigatoka is a disease that produces spots on the leaves of bananas and is caused by an air-borne fungus called Pseudocercospora fijiensis , formerly known as Mycosphaerella fijiensis . Fusarium Wilt Race 1 disease is one of the most destructive banana diseases that is caused by a soil-borne fungus called Fusarium oxysporum f.sp. Cubense (Foc). The dataset of curated banana crop image is presented in this article. Images of both healthy and diseased banana leaves and stems were taken in Tanzania and are included in the dataset. Smartphone cameras were used to take pictures of the banana leaves and stems. The dataset is the largest publicly accessible dataset for banana leaves and stems and includes 16,092 images. The dataset is significant and can be used to develop machine learning models for early detection of diseases affecting bananas. This dataset can be used for a number of computer vision applications, including object detection, classification, and image segmentation. The motivation for generating this dataset is to contribute to developing machine learning tools and spur innovations that will help to address the issue of crop diseases and help to eradicate the problem of food security in Africa.

Plant phenotyping relevance

バナナの健全・罹病状態を画像で記録した公開データセットが論文の中心であり、植物病害状態の画像ベース表現型解析に利用できる。

Code and data availability

The paper's banana leaf/stem image dataset (16,092 images) is publicly deposited on Harvard Dataverse (doi:10.7910/DVN/LQUWXW), and annotation was done with the Makerere AI Lab public web annotation tool on GitHub. Both are paper-specific, public, and actionable.