The key project objectives include:

• To develop a model that utilizes historical multi-source data to predict maize and sorghum yield at the district level     
• To deploy a small-scale smart farming system using low-cost Internet of Agro Things (IoAT) sensors and interactive cloud-based big data analytics to monitor and evaluate crops' performance in real-time.    
• To pilot a big data model to predict farm-level yield using low-cost agricultural Internet of Agro Things (IoT) sensor data by enhancing district-level resolution yield data to farm-level resolution yield data using Generative Adversarial Networks (GANs).    
• To conduct the economic feasibility of using agricultural IoT and big data for small-scale farm monitoring and yield prediction.      
• To formulate a data-driven policy brief on crop prediction using multi-source big data. The research will identify and reach potential farmers to use agricultural IoT. 

It is worth mentioning that the accurate prediction of crop yields at the farm scale can help smallholder farmers to estimate their net profit and enable insurance companies to ascertain payouts and agri-related loans to farmers. 

The team will receive the grant and other support from the African Technology Policy Studies Network (ATPS) and partners (International Centre of Insect Physiology and Ecology (iCIPE) and KUMASI HIVE) to implement their innovative project that will benefit small scale farmers and other stakeholders in Agriculture and Food Systems in Tanzania and across Africa.  

Read more details here: https://www.yeesi.org/news