Project Description

Morogoro YEESI Lab is a PEER Project hosted at the Sokoine University of Agriculture. This project is funded by National Academy of Sciences, US Agency for International Development and US Department of Agriculture. This project received PEER Cycle 9 Grant.

The project established Youth empowerment through the Establishment of Social Innovation (YEESI) Lab for problem-centered training in machine vision that is used by youth in the Morogoro region of Tanzania. There are young people in Morogoro who have studied information technologies and allied sciences, and while most of them can write computer programs, they cannot solve machine vision problems. This project aims to increase awareness among the youth of Morogoro and nearby regions to address machine vision problems in agriculture. Machine vision is a new and understudied practice in Tanzania; hence, this project will contribute to efforts in the creation of scientific societies that address the most pressing problems faced by more than 80% of Tanzania’s population who engage directly or indirectly in farming. The project expects to train more than 50 young technology enthusiasts who will be able to address the most pressing problems in agriculture and develop advanced digital tools to solve these problems.

Project Scope and Duration

The main agricultural problems can be classified into five categories, as explained below:

  • Disease Detection and Classification: The project will develop experts who will solve problems in disease identification using machine vision for most of the diseases in crops and livestock, which are misdiagnosed by farmers.

  • Weed Classification: The project will develop algorithms that accurately identify weeds and contribute to the growing scientific database for automatic weed detection.

  • Pest Detection and Classification: Appropriate tools using machine vision for Integrated Pest Management (IPM) are needed in Tanzania, as IPM has been hindered due to a lack of extension officers to train farmers on mitigation and identification of pests in agriculture.

  • Crop Seedlings Stand Count and Yield Estimation: Use of machine vision and drones instead of scouting manually to estimate stand counts would provide appropriate mitigation strategies for replanting that would be beneficial to commercial farmers. Also of importance are algorithms to sort and estimate yield by counting the fruits and to estimate the amount of other agricultural products.

  • Crop Vigor Estimation: Most farmers apply inputs evenly across the farm because they cannot predetermine crop vigor. Accurate estimation of crop health would help farmers to mitigate the problems earlier and improve crop performance and avoid failure. Algorithms to determine crop vigor developed in this project will contribute to the improvement of the methods to estimate crop performance earlier.

Project Dates: May 2021 – April 2023

Project team members

  1. Dr. Kadeghe G. Fue — Principal Investigator (PI)
  2. Prof Glen Rains — Co-PI
  3. Prof. Camilius Sanga — Co-PI
  4. Prof. Wulystan Mtega — Co-PI
  5. Dr. Alcardo Barakabitze — Co-PI


Mr. Deus F. Kandamali — Instructor: Machine Learning in Agriculture
Ms. Catherine F. Mangare — Instructor: Machine Learning in Agriculture
Dr. Alcardo A. Barakabitze — Instructor: Mobile App Development
Dr. Sixbert K. Maurice — Instructor: Introduction to Digital Agriculture
Mr. Denis Olgen — Instructor: Introduction to Digital Agriculture
Ms Rehema Mwawado — Instructor: Machine Vision in Agriculture
Mr. Hussein Mkwazu — Instructor: Problem-solving and Program Design with Python
Dr. Joseph Telemala — Instructor: Problem-solving and Program Design with Python
Dr. Michael Mahenge — Instructor: Entrepreneurship for Artificial Intelligence
Mr. Juma Kilima — Educational Technology and E-Learning Expert
Ms. Evetha Richard — Computer Support and Innovation Hub Technologist

Project Website