Status and future prospects for mobile phone-enabled diagnostics in Tanzania.

Introduction: Diagnosis is a key step towards the provision of medical intervention and saving lives. However, in low- and middle-income countries, diagnostic services are mainly centralized in large cities and are costly. Point of care (POC) diagnostic technologies have been developed to fill the diagnostic gap for remote areas.

Common beans imagery dataset for early detection of bean rust and bean anthracnose diseases.

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.

Corrigendum: Poultry diseases diagnostics models using deep learning.

[This corrects the article DOI: 10.3389/frai.2022.

Poultry diseases diagnostics models using deep learning.

Coccidiosis, Salmonella, and Newcastle are the common poultry diseases that curtail poultry production if they are not detected early. In Tanzania, these diseases are not detected early due to limited access to agricultural support services by poultry farmers. Deep learning techniques have the potential for early diagnosis of these poultry diseases.