Company or Institution
The Nelson Mandela African Institution of Science and Technology
Industry
Farming
Website
Country
Tanzania, the United Republic of
Sustainable Development Goals (SDGs)
SDG 2: Zero Hunger
General description of the AI solution
Common beans and Irish potatoes are among the important food and cash crops to most smallholder farmers in Tanzania. Despite their importance in the household economy and food security, yields are generally low due to the effects of diseases, specifically Bean rust and Bean anthracnose for Common beans, and Early and Late blight for Irish potatoes. The current management of these four diseases includes the removal of the affected leaves and plants to reduce their spread, signifying that early detection is the key to successful management. This project will therefore develop a Deep Learning tools to detect early these four diseases based on leaf imagery data and enable the farmer to make the appropriate decision for managing the spread of the diseases. The proposed project consortium of agricultural and machine learning researchers aims to deliver a two-way approach for the effective management of these crop diseases in Tanzania and other parts of Africa using Artificial Intelligence.
Publications
▪ Poultry diseases diagnostics models using deep learning. Frontiers in Artificial Intelligence. 5:733345. https://doi.org/10.3389/frai.2022.733345 (Co-authored with Dina Machuve, Ezinne Nwankwo and Jimmy Mbelwa)
▪ Combining Clinical Symptoms and Patient Features for Malaria Diagnosis: Machine Learning Approach. Applied Artificial Intelligence, 2022, https://doi.org/10.1080/08839514.2022.2031826 (Co-authored with Martina Mariki and Elizabeth Mkoba)
▪ Machine Learning Model for Predicting Student Dropout: A Case of Tanzania, Kenya and Uganda. 2021 IEEE AFRICON, 2021, pp. 1-6, doi: 10.1109/AFRICON51333.2021.9570956. (Co-authored with Dina Machuve)
▪ Document Digitization Technology and Its Application in Tanzania. In: Arai K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-82196-8_53. (Co-authored with Mbonimpaye John, Beatus Mbunda, Victor Willa, Dina Machuve and Shubi Kaijage)
▪ An Ensemble Predictive Model Based Prototype for Student Drop-out in Secondary Schools – Journal of Information Systems Engineering & Management, 4(3). DOI: https://doi.org/10.29333/jisem/5893 (Co-authored with Khamisi Kalegele and Dina Machuve)
Needs
Funding