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PARTNERSHIPS FOR ENHANCED ENGAGEMENT IN RESEARCH (PEER)
Cycle 9 (2020 Deadline)


Morogoro youth empowerment through establishment of social innovation (YEESI) lab for problem-centered training in machine vision

PI: Kadeghe Fue (kadefue@sua.ac.tz), Sokoine University of Agriculture
U.S. Partner: Glen Rains, University of Georgia
Project Dates:

Project Overview:
 
The project proposes to establish a social innovation lab (YEESI) for a machine vision program that will be used by youth in the Morogoro region of Tanzania. There are young people in the area 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 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. The main agricultural problems can be classified into five categories, as explained below:
  1. 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.
  2. Weed Classification: The project will develop algorithms that accurately identify weeds and contribute to the growing scientific database for automatic weed detection.
  3. 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.
  4. 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.
  5. 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.
The proposed project is expected to have several development impacts. Technologies that are going to be developed by youth will be used for data collectors, data labelers, and systems developers who will be employed on a short-term or long term basis. Some will become innovators and entrepreneurs who can develop start-ups, spin-off, and innovative companies. Youth engaged in this project will also develop an interest in farming knowledge that would be crucial in the development of agriculture in the country and inspire other youth to engage in farming. Farmers who will use tools developed from this project will improve knowledge in crop management. These tools will help protect the environment, as they will enable farmers to produce prescriptive maps to help them to perform variable-rate application of pesticides and other farm inputs as determined by machine vision. The tools will also support farmers’ decisions on crop production by helping them avoid less fertile land and better control pests and diseases.


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