RESUMEN
Common 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 income. Despite its significance, common bean production is often affected by diseases, particularly bean rust and bean anthracnose, resulting in low yields and diminished economic returns. To address this challenge, a comprehensive dataset of common bean leaf images has been collected by using smartphone cameras to capture the visual characteristics of healthy and diseased leaves. The dataset contains more than 59,072 labeled images, offering a valuable resource 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 generating this dataset is to facilitate the development of machine learning tools that will empower agricultural extension officers, smallholder farmers, and other stakeholders in agriculture to promptly identify and diagnose affected crops, enabling timely and effective interventions before causing significant 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.
RESUMEN
Maize is one of the most important staple food and cash crops that are largely produced by majority of smallholder farmers throughout the humid and sub-humid tropic of Africa. Despite its significance in the household food security and income, diseases, especially Maize Lethal Necrosis and Maize Streak, have been significantly affecting production of this crop. This paper offers a dataset of well curated images of maize crop for both healthy and diseased leaves captured using smartphone camera in Tanzania. The dataset is the largest publicly accessible dataset for maize leaves with a total of 18,148 images, which can be used to develop machine learning models for the early detection of diseases affecting maize. Moreover, the dataset can be used to support computer vision applications such as image segmentation, object detection and classification. The goal of generating this dataset is to assist the development of comprehensive tools that will help farmers in the diagnosis of diseases and the enhancement of maize yields thus eradicating the problem of fod security in Tanzania and other parts in Africa.
RESUMEN
Since the 2000s, Tanzania's natural resource management policy has emphasised Wildlife Management Areas (WMAs), designed to promote wildlife and biodiversity conservation, poverty alleviation and rural development. We carried out a quasi-experimental impact evaluation of social impacts of WMAs, collecting data from 24 villages participating in 6 different WMAs across two geographical regions, and 18 statistically matched control villages. Across these 42 villages, we collected participatory wealth ranking data for 13,578 households. Using this as our sampling frame, we conducted questionnaire surveys with a stratified sample of 1,924 household heads and 945 household heads' wives. All data were collected in 2014/15, with a subset of questions devoted to respondents' recall on conditions that existed in 2007, when first WMAs became operational. Questions addressed household demographics, land and livestock assets, resource use, income-generating activities and portfolios, participation in natural resource management decision-making, benefits and costs of conservation. Datasets permit research on livelihood and wealth trajectories, and social impacts, costs and benefits of conservation interventions in the context of community-based natural resource management.
