Common bean (Phaseolus vulgaris) is an important food and cash crop in Tanzania, where it contributes to household nutrition and income among smallholder farmers. Its production is, however, constrained by diseases such as bean rust and bean anthracnose, which can cause substantial yield losses and reduce crop quality. This article presents a labelled image dataset of common bean leaves collected under field conditions in Northern Tanzania to support research and application development in computer vision, machine learning and digital crop health. The dataset comprises 155,842 labelled multi-season images belonging to three classes: healthy leaves, bean rust and bean anthracnose. Images were acquired from farms in Arusha, Kilimanjaro and Manyara regions using smartphone cameras and were subsequently reviewed and validated with support from agricultural extension officers and plant pathologists to improve annotation reliability. The dataset is organized into class-specific compressed files and is publicly available through the Zenodo repository. By providing a large field-based image resource captured under variable real-world conditions, this dataset can support the development, training and evaluation of image-based models for common bean disease identification.
Mduma et al. (Fri,) studied this question.