Laizer, HudsonMduma, NeemaMachuve, DinaMaganga, Reinfrid2024-09-102024-09-10202410.5281/zenodo.8286125www.elsevier.com/locate/dibhttps://repository.must.ac.tz/handle/123456789/171This journal article was published by Elsevier in 2024Common bean plays a crucial role in the agricultural sector in Tanzania. To most smallholder farmers, the crop serves as a principal source of protein and an essential source of in come. Despite its significance, common bean production is often affected by diseases, particularly bean rust and bean anthracnose, resulting in low yields and diminished eco nomic returns. To address this challenge, a comprehensive dataset of common bean leaf images has been collected by using smartphone cameras to capture the visual character istics of healthy and diseased leaves. The dataset contains more than 59,072 labeled images, offering a valuable re source for developing machine learning models and user friendly tools capable of early detection and diagnosis of bean rust and bean anthracnose diseases. The aim of gen erating this dataset is to facilitate the development of ma chine learning tools that will empower agricultural extension officers, smallholder farmers, and other stakeholders in agri culture to promptly identify and diagnose affected crops, en abling timely and effective interventions before causing sig nificant economic loss. By equipping farmers with the knowledge and tools to combat these diseases, we can safeguard bean production, enhance food security, and strengthen the economic well-being of smallholder farmers in Tanzania and other parts of Africa.enArticle