Enhancing Farm-scale Crop Yield Predictions

Project Description

Dr. Alcardo Alex Barakabitze and his team from the Sokoine University of Agriculture, RECODA and Sahara Ventures announced among the winners of the Artificial Intelligence for Agriculture and Food Systems (AI4AFS) Innovation Research Network in Africa with the project titled “Enhancing Farm -scale Crop Yield Predictions using Machine Learning Models for Internet of Agro- Things in Tanzania”. It is worth mentioning that 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.

Project Scope and Duration

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 (IoAT) 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 IoAT.

Project team members

  1. Dr. Alcardo Alex Barakabitze — Principal Investigator (PI)
  2. Prof. Camilius A. Sanga — Co-PI
  3. Sahara Ventures — Co-PI (collaborator)
  4. RECODA — Co-PI (collaborator)
  5. Dr. Kadeghe G. Fue — Team member
  6. Dr. Neema Nicodemus — Team member
  7. Dr. Michael P. J. Mahenge —  Team member
  8. Dr. Joseph P. Telemala — Team member