News
Sokoine University of Agriculture YEESI Lab team participated in the UmojaHack Africa Hackathon 2023 https://umojahack.africa/. YEESI Lab student, Adam Jamali, a 3rd-year Irrigation student, placed third in the hackathon. The winners can be found here; https://zindi.africa/competitions/umojahack-africa-2023-beginner-challenge/leaderboard
Among other teams that we had, YEESI Lab had a team, formed by three other students (Mr Dickson Massawe, a 4th-year Agricultural Engineering student, Mr Fikiri Matatizo, a 2nd-year Physics and IT and Mr George Munishi, a 4th-year Agricultural Engineering student), which excellently with synergy represented well our lab with outstanding flying colours. Stephano Mashauri, a 2nd-year Agricultural Engineering student also placed seventh in the competition. Mr Jacob Shimba, a third-year Agricultural Engineering student also participated well in the competition. The students were supervised closely by Mr Deus Francis from the Department of Informatics and Information Technology. The team did well in all the challenges in Tanzania as shown in the public leaderboard http://zindi-metabase-v1.azurewebsites.net/public/dashboard/b9115882-27e0-4654-9f40-31beece000da
Most of our YEESI Lab students utilized YEESI Lab public shared Computing Node http://yeesi.sua.ac.tz/ to train the models.
Congratulations to our hard-working students
The hackathon involved 3000 students across 30 African countries and more than 300 universities. It was done online for two days from 18-19 March 2023 [Super Weekend]. The YEESI Lab students were positioned in the YEESI Lab premises at the Electronics and Precision Agriculture Lab, School of Engineering and Technology, Sokoine University of Agriculture.
The challenge was on Carbon Dioxide Prediction.
The Description of the challenge
The ability to accurately monitor carbon emissions is a critical step in the fight against climate change. Precise carbon readings allow researchers and governments to understand the sources and patterns of carbon mass output. While Europe and North America have extensive systems in place to monitor carbon emissions on the ground, few are available in Africa.
The objective of the challenge is to create machine learning or a deep learning model using open-source CO2 emissions data (from Sentinel-5P satellite observations) to predict carbon emissions.
These solutions will enable governments, and other actors to estimate carbon emission levels across Africa, even in places where on-the-ground monitoring is not possible.
YEESI Lab exclusively guides and trains students in Artificial Intelligence for agriculture and other allied sciences.
19-March-2023
The project team comprises of the YEESI Lab PI, Dr Kadeghe Fue and another YEESI Lab Data Team expert, Dr Neema Nicodemus Lyimo to represent YEESI Lab. The team also includes Dr Silvia F. Materu, a lecturer from SUA and Dr Ndimile C. Kilatu, a Morogoro Municipality Health Officer from TAMISEMI. Congratulations Team.
From Lacuna website, the announcement says:
Lacuna Fund is delighted to announce awards to eight teams who will create machine learning datasets in the Climate domain. Five project teams will focus on the intersection of climate and energy, studying impacts in Pakistan, Sri Lanka, Nigeria, and Mauritius. These datasets aim to improve energy systems and infrastructure for climate change mitigation and adaptation.
The remaining three teams are focused on health. Their aim is to understand climate harms to health and livelihoods, and they will be conducting their work in Kenya, Malawi, Senegal, Tanzania, Uganda, and the Philippines. These machine-learning datasets for climate and energy and climate and health energy span multiple continents, contexts, and conditions. We congratulate these teams on their awards to create open, equitable datasets in low- and middle-income countries across the globe.
We extend our deep gratitude to both of our 2022 Climate Technical Advisory Panels (TAPs) and partner reviewers for their work distilling a vibrant applicant pool and selecting a diverse portfolio of projects for funding.
Tanzania Climate Sensitive Waterborne Diseases Dataset for Predictive Machine Learning
Contact: Joseph P. Telemala | josephmasamaki@gmail.com / josephmasamaki@sua.ac.tz
Advances in machine learning (ML) for healthcare applications have the potential to be an alternative and best solution to solve the problems of climate-sensitive diseases in Africa and low-income countries like Tanzania. This project will strengthen the health system in the East African region by creating a dataset that aids in the prediction and characterization of waterborne diseases as influenced by climate change. The dataset will include three waterborne diseases that are sensitive to climate change: typhoid fever, diarrhea, and amoebiasis.
Five different kinds of datasets will be used to characterize disease hotspots in five selected areas of Tanzania: Morogoro Municipal Council (MC), Singida MC, Dodoma City Council (CC), and Dar es Salaam CC (Temeke MC, Ilala MC). Datasets will be collected in five categories: (i) demographic characteristics of the waterborne diseases, (ii) locations of the toilets and quality of the toilets, (iii) management of solid wastes and dump sites, (iv) meteorological information of the hotspots, and (v) location of the water sources used by local people for daily household activities. The combination of all these datasets in tabular form will be used to train powerful machine learning algorithms to predict and characterize the outbreaks of water borne diseases in the study areas. Furthermore, the predictive models can be embedded into early warning systems to support council managers and healthcare providers to make informed decisions to control and eliminate the outbreak of waterborne diseases.
“The effects of climate change on human health are real. Outbreaks of climate-sensitive waterborne diseases in developing nations are a common disaster. If a curated dataset is made available and accessible for AI researchers to use, they can develop powerful predictive AI models that can forecast outbreaks, prevent epidemics, and save lives. With the support from Lacuna, we will develop Tanzania’s first machine learning dataset for forecasting climate-sensitive waterborne diseases”.
— Joseph P. Telemala, Sokoine University of Agriculture
Read More: https://lacunafund.org/announcing-awards-for-climate-datasets-health-and-energy/
Tanzania is one of the fastest-growing economies in Africa and bears a lot of potential in many areas, one of them being innovation. This promising and dynamic area has seen rapid developments in the last few years as the country moved 26 places up on the Global Innovation Index, from ranking 123 in 2013 to 97 in 2019.
However, while Tanzania's innovation performance has improved, startups struggle to grow and scale. One of the major issues is the lack of systematic approaches for collecting, storing, and disseminating key information about the key actors in the innovation and entrepreneurship space. Those wishing to support actors in this sector must rely on competitions and public calls to solicit interests. Further, while entrepreneurship support organisations (ESOs) keep records of startups they work with, the information is often for the most current programs and rarely shared with the ecosystem. The information gap also persists in relation to entrepreneurship support organisations (ESOs). It is unclear what actors provide services and for which type of enterprises. Likewise, no consistent source disseminates both local and international opportunities.
Being an apex body in the ecosystem, the Tanzania Startup Association (TSA) is identifying and profiling ecosystem actors for the annual status of the startup ecosystem. The information collected from this activity will be shared widely within and outside the ecosystem to inform priorities towards developing better interventions and promoting synergies.
In this regard, the TSA requests startups, incubators, accelerators, fabrication labs, maker spaces, capital providers, academic institutions, regulatory agencies, and other actors operating in Tanzania's startup ecosystem to share their information by filling out this form. Start by selecting the category most relevant to your organisation's activities, and actor-specific questions will follow up. However, all information shared here shall not be distributed to third parties without your consent.
In case of any concerns, please contact them through info@tsa.co.tz and include "Ecosystem Actors Identification and Profiling 2022" in your email subject.
https://tsa.co.tz/publication/publication_1.html
16-Feb-2023
Mr Dickson Massawe operating the modern SnapMaker 3-in-1 A350T, a 3D printing, CNC carving and Laser engraving machine at the Electronics and Precision Agriculture Lab (EPAL). EPAL develops automation technologies and sensors for Precision Agriculture.
YEESI LAB STUDENT MR DICKSON MASSAWE WINS ZINDI-INDABAX ARTIFICIAL INTELLIGENCE HACKATHON CHALLENGE 2022
An IndabaX is a locally organized Indaba (i.e. gathering) that helps develop knowledge and capacity in machine learning and artificial intelligence in individual countries across Africa. A deep Learning IndabaX is a locally organized Indaba that helps spread knowledge and builds capacity in machine learning.
Tanzania IndabaX 2022 was organized by Tanzania AI Lab in collaboration (co-hosted) with the Nelson Mandela Institution of Science and Technology, AI for Development Lab at the University of Dodoma and dLAB at the University of Dar es Salaam. The event organized a hackathon hosted at Zindi Africa, the largest professional network for data scientists in Africa. The challenge was for an African telecommunications company that provides customers with airtime and mobile data bundles. The challenge aimed to develop a machine learning model to predict the likelihood of each customer “churning,” i.e. becoming inactive and not making any transactions for 90 days. The challenge attracted more than 50 participants.
YEESI Lab was presented by several members. Six members placed in the top 20 of the challenge. Mr Dickson Massawe placed first in the challenge. Mr Massawe, a member of YEESI Lab, also works as a manager of the Electronics and Precision Agriculture Lab (EPAL), which hosts YEESI Lab at the Department of Agricultural Engineering. YEESI Lab PI, Dr Fue is the PI of EPAL that runs seven other extramural funded projects and two international consultancies. Mr. Massawe is a 4th Year Agricultural Engineering Student at SUA. Mr Massawe was introduced to Machine Learning and Vision technologies by YEESI Lab Online courseware, in which he has proved that Problem based Learning (PBL) approaches may groom very effective experts in the field of Artificial Intelligence for Agriculture.
For more information, visit: https://zindi.africa/competitions/indabax-tanzania-2022/leaderboard
On 10th of December, 2022, The Tanzania IndabaX 2022 will conclude. If you are looking to attend physically or virtually, let us know at info@yeesi.org
08/12/2022
IndabaX: A way to experiment with the ways in which we can strengthen our Machine Learning community, and to allow more people to contribute to the conversation. The IndabaX programme started in 2018 as an experiment in strengthening our machine learning community beyond the annual Deep Learning Indaba, to allow more people to contribute to the conversation on artificial intelligence and machine learning. We join hands across our beautiful continent Africa. The initiative continues in 2022, and it is YOUR initiative!
An IndabaX is a locally-organised Indaba (i.e gathering) that helps develop knowledge and capacity in machine learning and artificial intelligence in individual countries across Africa.
A Deep Learning IndabaX is a locally-organised Indaba that helps spread knowledge and builds capacity in machine learning.
TZ IndabaX in Tanzania is organized by Tanzania AI Lab in collaboration (co-hosted) with Nelson Mandela Institution of Science and Technology, AI for Development Lab at the University of Dodoma and dLAB at the University of Dar es Salaam.
See the presentations here: https://docs.google.com/presentation/d/16-k3YN7kGziBOtNQEU3Dt37YvCOznxAjrb3lqWXvsso/edit?usp=sharing
You can register and participate in challenges: https://geoaichallenge.aiforgood.itu.int/match/matchitem/64
04-12-2022
The PI, Dr Kadeghe Fue was invited to attend the 1st Enhance Mind Artificial Intelligence (EMAI) conference which happening from 14th to 16th November 2022 held at the COICT University of Dar es Salaam as a Key Note Speaker.
The PI presented his ideas on YEESI Lab research and shared with innovators on gaps available that can be solved by using Artificial Intelligence technologies that are relevant to the majority of the farmers and that are posed to change farming in Tanzania. He talked about how AI experts can position themselves in Digital Economy and contribute to the targets of the National Five-year Development Plan (FYDP) III.
16-11-2022
Last week three staff from USAID & PEER visited SUA for M & E assignment. At SUA, there are three projects which are or have already been funded by them. These are:
a. Exploring the fate of mercury in artisanal gold mining of the Lake Victoria Gold Field under Principal Investigator (PI), Prof. Clavery Tungaraza - https://sites.nationalacademies.org/PGA/PEER/PEERscience/PGA_181434
b. Enhancing postharvest technologies and food safety innovations in fresh tomato value chain under PI, Yasinta Muzanila - https://sites.nationalacademies.org/PGA/PEER/PEERscience/PGA_195550
c. Morogoro youth empowerment through establishment of social innovation (YEESI) lab for problem-centered training in machine vision under PI, Dr. Kadeghe Fue - https://sites.nationalacademies.org/PGA/PEER/PEERscience/PGA_365059
First the M&E team from USAID and PEER with the Principal Investigators of funded project made a courtesy call to the office of Vice Chancellor (VC). Then they visited the Electronics and Precision Agriculture Lab (EPAL) at School of Engineering and technology (SoET) before visiting the SMC.
At SMC, they had meeting with PI’s for the three projects with their respectively research team members. The presentation was based on what has been done to accomplish the research objectives as stipulated in the approved proposal. Also, there were question and answer session (Q&A).
After the presentation and Q&A session, they made a visit to Prof Tungaraza’s lab.
In concluding their assignment, they gave a well done job to all their projects. They were real impressed by the outstanding results, outcome and impacts.
At last, the three staff from USAID & PEER had a courtesy call to the office of Principal of CoNAS, Dr Karugila.
18-October-2022
YEESI Lab PI meets K.I.E.Z, an initiative dedicated to facilitate entrepreneurs in AI with scientific expertise as well as access to capital, industry partners and hiring talent. From the first idea to the successful business, we provide science-based AI startups with everything they need to grow fast and sustainably.
YEESI Lab PI Dr. Kadeghe Fue, Dr. Susanne Perner, and Mr. Jonathan Zebhauser discussed on conducting an activity that will involve SUA students especially who are working with YEESI Lab together with other four Germany universities; Technische Universität Berlin, Humboldt-Universität zu Berlin, Freie Universität Berlin and Charité Universitätsmedizin.
The activity will be working on real-world challenges to smallholder farmers in Tanzania: students will be grouped in interdisciplinary and international teams and will learn how to transform business ideas into an early business concept using AI technology.
The event will tentatively happen 23rd - 24th November (all-day).
Visit kiez.ai
17-Oct-2022
Ms. Joan Jonathan, YEESI Lab Data Specialist, member of the academic staff at SUA, visited Dakawa ward located in Mvomero district, Morogoro Region in Tanzania. With the assistance of an extension officer, Mr Rajun Ismail Kisavuli, we were able to visit fields that grow plants such as maize, sunflower, tomatoes, watermelon, and vegetables, including Okra and Ngogwe/Nyanya chungu. We found healthy and unhealthy plants which are affected by pests or diseases. The maize plants were primarily affected by fall armyworm; however, rusty brown leaf spots affected Okra and Ngogwe/ Nyanya change plants. Furthermore, Ngogwe/ Nyanya chungu plants were found with a deficiency of nutrients such as Phosphorus and Calcium.
YEESI Lab congratulates Ms Joan for her excellent work.
28-Aug-2022
Ms Joan (Yellow shirt) with farmers and extension agents from Dakawa wards
Ms Anna Geofrey and Ms Bertha Msuliche, YEESI Lab Data Specialists, members of the academic staff at SUA, were privileged to work with Morogoro rural areas in Tanzania. They visited Mkuyuni ward villages; Madamu and Mkuyuni. They also visited the Mikese ward. They worked with an extension agent, Mariamu Kayenzi. They worked hard on hills to fetch crops, diseases, weeds and pests. They collected through observation and farm visitation with extension officers and farmers. The major challenge in their crop cultivation is maize, cassava, bananas, vegetables and fruits. Maize streak virus and fall armyworm were 80% of the challenges in maize, followed by aphids. Most vegetables like tomatoes and onions were affected by leaf miner, powdery mildew, early bright, late bright, Tuta absoluta, and red and white spider mites. The team collected more than 5000 images of crops diseases, pests and weeds. YEESI Lab is excited to work on the data for the future of Tanzanian farmers.
26-Aug-2022
Ms Anna and Ms Bertha at Mkuyuni village
Ms Faraja and Dr Neema with Ihenge farmers and Extension Officers
Ms Faraja and Dr Neema Nicodemus, members of the academic staff at SUA, were in charge of Gairo District data collection. They have taken pictures of five different crops. That is, spinach, bell pepper plant (Capsicum annuum), Brassica Chinensis, tomatoes, and maize from four wards in the district. Data on bell pepper plants, Brassica Chinensis, and maize were collected from Chigela village in the Ihenje ward. The crops are frequently affected by diseases like Bacterial leaf spots, Mosaic virus, Fusarium wilting disease (which affects plants from the roots), and Fall Armyworm in Maize.
Spinach and tomato datasets were collected from Ukwamani, Mkwamani and Mkalama wards. Tuta Absoluta-Leaf Miner (Kantangaze) was the most common disease found in tomatoes, and they captured it at different stages. Anthracnose and hotspots diseases mostly affected spinach vegetables. The team also collected data for two types of weed commonly found on the farms.
This is the first team to bring back weed collection data. YEESI Lab congratulates the team for their hard work.
26-Aug-2022
Ms Susan Mbacho, a member of the academic staff at SUA, was privileged to be part of YEESI Lab DATA team. Ms Susan visited Mgeta ward located in Mvomero district, Morogoro Region in Tanzania. Ms Susan found out there were various crop species grown. With the assistance of an extension officer, Mr Eliewaha Venance Kivunge, they were able to visit fields with various crops. The crops include, among others, vegetables such as Cabbage, Green Pepper, carrot, and ‘Ngogwe/ Nyanya chungu’. During the field visit a number of farmers’ fields had grown cabbages. A number of plants were healthy in most of the fields visited, however they found unhealthy plants with pests or diseases as well. The disease which was prevailing in most of the field is black rot. There were also cabbage pests found, which caused the plant leaves to be damaged. Also, there were plants identified with deficiency of nutrients such as Phosphorus and Calcium. A very interesting dataset was collected and classify by the Extension Officer who is an expert in agriculture.
This is the first team to bring Nutrient Deficiency images for training. YEESI Lab is excited to explore all such wonderful brought by the team.
26-Aug-2022
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.
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.
Dr. Barakabitze’s team will receive a grant of *USD 51,000* and other support from the African Technology Policy Studies Network (ATPS) and partners (International Centre of Insect Physiology and Ecology (iCIPE) and KUMASI HIVE), who are funded by IDRC to form AI for Agriculture & Food Systems (AI4AFS) innovation research network [https://atpsnet.org/default-item/artificial-intelligence-for-agriculture-project/] to implement their innovative project that will benefit small scale farmers and other stakeholders in Agriculture and Food Systems in Tanzania and across Africa. Fig. 1 indicates images of maize and sorghum farms where the project will be used for the implementation and testing.
The proposal finalisation stages, including the final workshop, were supported by YEESI Lab.
The Project Proposal was written by:
Dr Alcardo Barakabitze, a YEESI Lab co-PI
Prof Camilius Sanga, a YEESI Lab co-PI
Dr Kadeghe Fue, a YEESI Lab PI
Dr Neema Nicodemus, a YEESI Lab Data Specialist
Dr Joseph Telemala, , a YEESI Lab Instructor
Dr Michael Mahenge, a YEESI Lab Instructor
24 August 2022
Dr Barakabitze, Dr Fue and Dr Banzi from left with UDOM colleague at the workshop
The AI4D Multidisciplinary Research Lab is a project run in collaboration between two public academic institutions in Tanzania, The University of Dodoma (UDOM) & Nelson Mandela African Institution of Science and Technology (NM-AIST).
The AI4D lab presented a two-day workshop which took place from 10th-11th August 2022 conducted in hybrid (virtual and physical at the University of Dodoma, Dodoma - Tanzania). The workshop featured paper presentations, keynote talks, AI solutions demonstrations, and interactive panel discussions. The workshop explored issues around four thematic areas, namely: Healthcare, Environmental Conservation and Agriculture, Digital Economy and Small-Scale Enterprises, and AI Infrastructure and Data Ecosystem. Researchers, Innovators, Students, and Policymakers from across the continent participated.
YEESI Lab PI, Dr Kadeghe Fue and co-PI Dr Alcardo Barakabitze were among the people who were invited to present keynotes on AI for Agriculture and the work we do at YEESI Lab. See https://ai4dlab.or.tz/pages/ai4d-lab-workshop/speakers.html.
Dr Fue and Dr Barakabitze presented well for the YEESI Lab and demonstrated the way forward in the development and implementation of the AI-based machine vision systems in Agriculture.
18 August 2022
YEESI lab, in collaboration with the DICT office at SUA, shared one desk to demonstrate and exhibit work done by YEESI Lab.
Nane Nane Day on 8 August celebrates to recognize the important contribution of farmers to the national Tanzanian economy. Nane Nane means "eight eight" in Swahili, the national language of Tanzania (and of Tanganyika and Zanzibar, the two countries whose union created the United Republic of Tanzania in 1964).
Nane Nane also may refer to the Agricultural Exhibition, a one-week fair that takes place every year around this date [8/8] in varying locations of Tanzania. In the Nane Nane Agricultural Exhibition, farmers and other agricultural stakeholders (e.g., universities and research institutes, input suppliers or fertilizer producing industries) showcase new technologies, ideas, discoveries and alternative solutions concerning the agricultural sector. Nane Nane is a fair where government and private firms present their services and activities to the public.
Every year the national Nane Nane show takes place in different locations, for example in Ngongo, Lindi Region (2014), while there are also regional Nane Nane shows held in seven zones, namely in Arusha for Northern Zone; Eastern in Morogoro; Lake in Mwanza and Simiyu; Highlands in Mbeya; Southern in Lindi, Mtwara or Songea; Western in Tabora; and Central in Dodoma.
YEESI Lab was represented by Mr Matatizo, Mr Massawe, Mr Swai and Dr Fue, YEESI Lab PI. Posters and brochures were shared to NaneNane visitors. The YEESI Lab in turn collected good imagery dataset of horticulture nursery from the Crop Science and Horticulture Department Model Farms at Nane Nane.
8-Aug-2022
From 18th to 22nd of July 2022, Data Science Africa (DSA) organized a workshop in Arusha, Tanzania, bringing together data science experts in Africa to discuss topics related to Data, Technology, and Community.
Dr. Kadeghe G Fue, a lecturer from Sokoine University of Agriculture and Project Leader of SUA YEESI Lab, was one of the Keynote Speakers invited to present on digital and precision agriculture research in Africa and the work SUA YEESI Lab is doing on Data Science.
His presentation explained the role of Data Science Research in Digital and Precision Agriculture for African countries and the importance of integrating super neat and intelligent data-driven innovations for smallholder farmers.
Moreover, he suggested low-cost innovative solutions that would easily penetrate African communities and bring about positive change, but he also cautioned against the digital divide prevalent in African societies.
He concluded by describing how Data Science can be used to deliver tailored solutions that might be able to cater to the growing needs of smallholder farmers in the future.
You can find more details about the event and SUA YEESI Lab by visiting the links http://www.datascienceafrica.org https://www.datascienceafrica.org/dsa2022arusha/speakers
You may browse the Presentation here: https://drive.google.com/file/d/1dH7ghH3u__dIVNXWqY5MmNcclsnU8atO/view
https://www.sua.ac.tz/news/yeesi-lab-participates-data-science-africa-2022
On 29 July 2022
Prof Sanga, a YEESI Lab coPI attended a workshop on Creating Awareness on Open Science and Infusing it into Research Practice and Processes within Higher Educational Institutions in Eastern and Central Africa which took place at Multifunctional Hall of the Confucius Institute of the University of Dar es Salaam, Dar es Salaam, Tanzania from 14-15 July 2022.
Prof E. Karimuribo and Prof C. Sanga, both leaders from Sokoine University of Agriculture (SUA) attended a two-day workshop. The event was organized by The Association for African Universities (AAU) in collaboration with the Public Library of Science (PLOS) and the Training Centre in Communication (TCC Africa) from Kenya. The University of Dar es Salaam hosted it. This event targeted participation of Vice Chancellors, Rectors, Deputy Vice Chancellors, Directors of Research and Libraries of African Universities from the East African public and private universities although other participants from non-EAC regions were also allowed to participate virtually. A hybrid mode (physical and virtual) of participation was used to run the workshop.
The objectives of the workshop were:
i. To create and embrace open science and open access in respective member of academic communities
ii. To understand the current developments on open science, challenges faced in their respective academic and opportunities on open science and open access and
iii. To reduce the push back in the adoption of Open Science and Open Access by early career researchers
UNESCO defines Open Science as a movement aiming to make science more open, accessible, efficient, democratic and transparent. The transition to Open Science allows scientific information, data and outputs to be more widely accessible (open Access) and more reliably harnessed (Open Data) with active engagement of all relevant stakeholders (open to Society). However in many Higher Learning Institutions in Africa the implementation of Open Science is low. Also, there are few institutional policies or mandates that support or promote Open Science practices on the continent. We are living in a world that there are fragmented scientific and policy environment. Thus, we need a global understanding of the meaning, opportunities and challenges of open science for fair and equitable operationalization at the individual, institutional, national, regional and international levels.
The benefits of Open Data as listed by PLOS are in ensuring reproducibility; inspire trust, open opportunity to earn credit to academic member of staff and preserving data record and marketing your institution (visibility) (https://plos.org/open-science/open-data/).
The benefits of Open Science presented by UNESCO are as follows
a. It increases scientific collaborations and sharing of information for the wider society
b. Makes multilingual scientific knowledge open available, accessible and reusable by any humankind
c. Opens the processed of scientific knowledge creation, evaluation and communication to stakeholders beyond conventional scientific community
d. Increase the visibility of research outputs
In summary the term ‘Open Science’ as per UNESCO refers to “an umbrella concept that combines various movements and practices aiming to make scientific knowledge, methods, data and evidence freely available and accessible for everyone, increase scientific collaborations and sharing of information for the benefits of science and society, and open the process of scientific knowledge creation and circulation to societal actors beyond the institutionalized scientific community.”
Date: 16 July 2022
Sokoine University of Agriculture is among of the three universities participating in the Innoversity Project. This project was launched by French Embassy. Other collaborators are University of Iringa and Nelson Mandela African Institution of Science and Technology.
About Call for University Entrepreneurs
The call for university entrepreneurs provides opportunity for university students both undergraduate and postgraduate with innovative business idea on agriculture particularly agri-food sector. The call focus on identifying startups at ideation ,prototype, or Minimal Viable Product stage that will lead to sustainable technology businesses and companies with high growth potential.
Benefits
Successful startups will undergo an incubation program run by Sahara Ventures (https://saharaventures.com/) and thereafter will be linked to on-campus innovation hubs and technology transfer offices to encourage innovation, entrepreneurship, technology transfer, and research commercialization to create new solutions and employment opportunities.
About the Project
Innoversity project is a two-year project funded by the France Embassy in Tanzania and implemented by Sahara Ventures. The project supports students, lecturers, researchers, and management to integrate innovation and entrepreneurial approaches in their works to address the skills gap and create employment opportunities for youths.
Deadline for Applications: 20th August 2022
For more information about the call, visit https://forms.gle/3wnTnro9FVdSEAML6
19-Jul-2022
In this Omdena, AI Challenge with the Global Partnership for Sustainable Development Data, we created a simple but powerful application using GEE images to estimate crop yield in Senegal.
For food security understanding the food system is essential. Accurate crop type details in near real-time will provide insights into the food system for policymakers and will provide information on crop diversity and nutrition outcomes. So, we decided to create an application using open-source satellite images to identify the crops and estimate the yields for any given area.
Follow the link below for Details:
Sokoine University of Agriculture (SUA) is a public and student-centered university continue to improve preparing students to be competent, professional and skilled graduate through training.The University integrate research, consultancy and technical operations to produce skilled labour force that can be employed or self-employed and contribute to the national, regional and global development as per National Five Year Development plan (FYDPIII) of 2021and SDG 1(no poverty),SDG 2 (no hunger), SGD 4 (quality education), SDG 8(Decent Work and Economic Growth). To impart required skills, confidence, experience and to establish, run and manage enterprises the University established SUA STEP program to empower students to self-employ, as a way to reduce unemployment.
Dr Kadeghe Fue, YEESI Lab PI, will address DSA Event in Arusha, Tanzania as a keynote speaker. He will discuss the role of data science in precision agriculture.
Data Science Africa, an NGO, is an important stakeholder in moving forward the agenda of digital agriculture for Data-driven agriculture. Dr Fue will commend their work and establish a strong collaboration with DSA on research and development. YEESI Lab will play a great role in promoting data science in Agriculture in Africa.
The event will take place from 18th to 22nd July 2022.
A University of the Western Cape (UWC) team has won an overseas trip to the US, with an additional three students claiming a cash prize of 100 dollars each.
This big win came at the annual USSAVI (US-Embassy SA Virtual Incubator initiative) entrepreneurship programme on 5 May 2022, where UWC claimed five of the eight available prizes.
UWC’s Centre for Entrepreneurship and Innovation (CEI) facilitated the partnership with USSAVI in its quest to foster an entrepreneurial mindset amongst UWC students and graduates.
The programme, sponsored by the US Embassy in South Africa, took competitors through the programme week by week, culminating in the pitching event.
Read more:
Boost Up 2022 Application
Je wewe ni mbunifu? Usikose kutuma maombi ya kushiriki Shindano la BOOST UP kwa 2022. Maombi yamefunguliwa rasmi tarehe 1 Juni 2022 na yatahitimishwa tarehe 30 Juni 2022. Bonyeza link hii kukamilisha maombi yako
Shindano la Boost up hufanyika kila kila mwaka linaloandaliwa na kuratibiwa na Tume ya Taifa ya Sayansi na Teknolojia (COSTECH) kwa kushirikiana Jumuiya ya Ubunifu ya Nchi za Kusini mwa Afrika (SAIC).
#CostechTanzania #boostup2022 #application #applicationAreOpenNow
Morogoro Youth Empowerment through Establishment of Social Innovation (YEESI) lab hosts a renowned Prof in Precision farming
Sokoine University of Agriculture (SUA) has said that if there is sustainable use of Information and Communication Technology (ICT)in the country without restrictions then agriculture in this country can grow rapidly and be productive for the individual farmer and the Nation as a whole.
This was stated by Prof. Wulystan Mtega from the Department of informatics and Information technology while giving a speech on behalf of Principal of the College of Natural and Applied Sciences (CONAS) at Solomon Mahlangu Campus. This was done on 24 May 2022 at the opening of the two-day workshop organized by the ‘SUA YEESI Lab’ Project which enables young innovators to use smart technologies (e.g. artificial intelligence & machine learning, machine vision, Internet of Things, precision agriculture etc) in solving various challenges in agriculture.
Prof. Mtega said currently the number of experts is small in reaching farmers scattered in different locations in the country so the use of smart technologies can address various challenges in agriculture through access to accurate and timely information.
And the Principal Investigator of the Project, Dr. Kadeghe Fue who is also a SUA Lecturer said the project aims to empower young innovators from Morogoro in ensuring they have proper skills, knowledge and competency in smart technologies needed to solve different challenges facing farmers.
Dr. Fue said by using the artificial intelligence technology students can learn how to look at challenges in agriculture such as Disease Detection and Classification, Weed Classification, Pest Detection and Classification, Crop Seedlings Stand Count and Yield Estimation and Crop Vigor Estimation.
He said due to the expertise they will acquire from YEESI Lab, the learners can become innovators and entrepreneurs who can develop start-ups, spin-offs, and innovative companies. Ultimately they will be a catalyst for change in how to do productive agriculture.
A renowned Professor from the University of Georgia in the United States, Prof. Glen Rains presented a lecture titled ‘Sensors for tracking classification and identification in Agricultural Applications’.
https://scholar.google.com.au/citations?user=QjBXCsQAAAAJ&hl=en
He stated that since the end of World War II in 1945, the world, including the United States, has been hit hard by environmental degradation and modern technologies have been introduced to ensure sustainable agricultural production.
He said it was important for farmers to use the latest smart technologies including precision agriculture in ensuring they solve various challenges in order to increase agricultural production.
For more information visit:
https://sites.nationalacademies.org/PGA/PEER/PEERscience/PGA_365059
In Swahili: SUA imesema matumizi ya Teknolojia ya Habari na Mawasiliano yatatatua changamoto za kilimo nchini http://suamedia1994.blogspot.com/2022/05/sua-kimesema-matumizi-endelevu-ya.html
Prof Glen Rains visited SUA from 23-June-2022 to 27-June-2022
The YEESI Lab PI, Dr Kadeghe Fue was invited by Anzisha Venture Studios to speak on Innovation Week Tanzania 2022 (IWTz2022) Morogoro Region Edition which was commenced on Friday of 13th May 2022 at Morena Hotel, Morogoro. The PI spoke on a session titled "As the entire world is moving towards technopreneurship, what is the current state in Tanzania, and how does the regulatory environment and media support innovations, technology and startup entrepreneurship?"
The Guest of Honor was the Regional Commissioner Hon. Martin Shigella who was represented by the District Commissioner, Hon. Adv. Albert Msando.
The YEESI Lab PI discussed Digital Agriculture and the importance of using Machine Learning and Vision to improve the work of farming communities. He discussed the priorities mentioned in Tanzania's Five Year National Development Plan (FYDP III) that was presented by the Minister of Finance, Dr Mwigulu Nchemba on June 2021. FYDP III mentions Precision Agriculture, Artificial Intelligence and Innovation Hubs to be leading agendas for youth to unlock opportunities and improve agriculture.
The theme of the event was "Innovation for Sustainable Development".
13th may 2022
More details at anzisha.co.tz and anzishafest.co.tz
yeesi lAB Participates in sua innovation day
YEESI Lab PI,Dr Kadeghe Fue and students participated in SUA innovation day to showcase their progress on research to SUA Community and District Commissioner Adv. Albert Msando.
7-May-2022
yeesi LAB participates in machine learning competition for an internship at zindi africa
Some of the YEESI Lab students and the PI participated in a machine learning competition [https://zindi.africa/competitions/loan-default-prediction-challenge] which provides winners with the opportunity to participate in an internship interview invitation. The Loan Default problem is among the most pressing issues for farmers that have led to high monthly premiums and unaffordable interests in farming society. One of the students who placed 6th, Mr. Fikiri, was invited for the interview. The PI participated in the competition independently to learn how to teach students to participate in the machine learning competition. The PI placed 3rd place (1st in Tanzania) and his code when run on Google Collab placed 1st on the leaderboard. The code will be used by students to learn how to efficiently do winning feature engineering and compete with peers in African countries. YEESI Lab believes not only in teaching AI models but also in training efficient, world-class, and accurate models.
18-April-2022
yeesi lab pi invited for a public lecture at the Federal University of Lavras, Brazil
YEESI Lab PI was invited to present a video-conference public lecture to students and precision agriculture experts at the Federal University of Lavras, Brazil. He presented the role of Artificial Intelligence and Machine Learning in Digital Agriculture. He also presented the agenda that has been proposed in YEESI Lab to train youth on ML and AI. The main theme is how the youth can initiate their desire to learn ML/AI and utilize online learning platforms. He also talked about instructors recording videos and sharing talks to raise awareness and share experiences in Digital Agriculture. Also, He discussed the establishment of the start-ups in Digital Agriculture to boost the agenda in Africa and South America like how it is done in America, Europe, and Australia.
The PI of YEESI Lab was invited by Prof. Adão Felipe dos Santos, a Professor of Precision Agriculture
12-April-2022
YEESI Lab participates in International Girls in ICT Day 2022
Science, technology, and innovation are the key drivers of our increasingly global and digital society. For girls and young women to thrive in science, technology, engineering, and mathematics (STEM) careers, they need safe and reliable access to digital tools. In line with the International Girls in ICT Day that will be celebrated on 28 April, YEESI Lab and the Tanzania Communication Regulatory Authority (TCRA) conducted a 1-day event with students from Kilakala secondary school on 9th April 2022 at SUA. The organisers of this event at SUA are Dr. Alcardo Alex Barakabitze and Prof. Camilius Sanga from YEESI Lab in collaboration with TCRA. The aim of this event was to encourage girls and young women to pursue science, technology, and engineering education and work in STEM careers. The main topic of the event focused on demonstrating to students from Kilakala secondary school the uses of ICTs and their impacts on the agriculture industry. Two female ICT scientists from SUA, Dr. Neema Sumari, Ms Joan Jonathan, and Dr. Neema Nicodemus presented their topics regarding “ICT for Future Smart World” and “Artificial intelligence (AI) and Machine learning (ML) in agriculture” respectively. Dr. Kadeghe Fue from YEESI Lab also presented smart technology and machine vision for precision agriculture where a robotic solution for cotton harvesting was demonstrated.
The TCRA at this event was represented by the Principal ICT officer and Eng. Aneth Kilaja.
#GirlsInICT
9-April-2022
Girls in ICT TCRA Visit at SUA Video
REMOTE SENSING SEMINAR
You are invited to a scientific seminar on Machine Learning and Remote Sensing. It is sponsored by the research project: YEESI Lab
More info on www.yeesi.org
Join Zoom Meeting on April 4th, 2022 at 2:00pm
https://us02web.zoom.us/j/82170986897
Meeting ID: 821 7098 6897
Please find attached. Please let us know if you have any questions or if you would like to be reminded to attend.
Title: Detection of Geographic Faults using Deep Learning Model from DEM and Remote Sensing Data in Djibouti.
Abstract
Underground water flow delineation is critical for understanding the groundwater cycling systems and their utilization at arid land areas, such as in Eastern African Djibouti. However, such regions lack essential data, such as borehole data, and this becomes a challenge. One solution is through fault detection to evaluate the possibility of fault-driven groundwater flow into the water cycling systems. This study focuses on geographic faults which exist in Djibouti where the plate tectonic activities are remarkable. Our study aimed to utilize the fault lines delineated on existing geology maps since fault lines distribution has potentiality for high correlation with groundwater flow volumes. It is essential to evaluate if the fault systems can contribute to the simulation of groundwater volume modules. This work develops on our previous research of fault inspection using relief image in fault distribution derived from adaptive gradient filter applied on PALSAR-1/2 image data. In this seminar, deep learning techniques are shown in fault detection analysis based on Digital Elevation Model (DEM) and remote sensing data in training a multi-input deep convolutional neural network (Deep CNN) model.
Used datasets are ortho-rectified PALSAR-1 RTI and PALSAR-2 Global Mosaic, DEM data as well as curvature and slope images derived from the DEM.
Also, fault labels digitized and rectified from the existing geology map, specifically on the selected catchment areas.
The proposed deep CNN model could be applied to other watersheds in Djibouti to help in groundwater flow model simulations and eventually help locate the potential area for groundwater resources in entire Djibouti.
What will be covered
- Data collection and required set up
- Data processing – software and programs
- Data analysis – software and programs
31-Mar-2022
YEESI LAB HOSTED AT DIRECTORATE OF ICT (SUA) IS PARTICIPATING IN UmojaHack Africa 2022
The emerging Innovators from YEESI Lab are participating in UmojaHack Africa 2022 which is done across 260 universities in Africa. It will be done for two days from Saturday to Sunday (19-20th March 2022). Each participant can develop a machine learning model and submit test results to Zindi Africa at least 75 times a day. The UmojaHack is organized by a very famous machine learning community portal in Africa called Zindi Africa. The UmojaHack competition is supported by multinational companies such as Microsoft, NVidia, InstaDeep, ABSA, H20 AI, and Data Science Academy. The UmojaHack is categorized in three sub competitions (i.e. beginner, intermediate and advanced). The emerging Innovators from Sokoine University of Agriculture trained at YEESI Lab are participating into beginner and intermediate sub competitions. The emerging innovators have been trained by subject matter experts (SMEs) from SUA. The training is still done online. It can be accessed at yeesi.org. The training approach is through Problem-based Learning in a curriculum which is developed in principles of Student-Centered Learning.
For more information about how the training was done, visit the online training videos from Dr Kadeghe Fue, Dr Michael Mahenge, Ms Rehema Mwawado, Mr Deus Francis and Dr. Joseph Philippo Masamaki.
19 March 2022
YEESI Lab PI talks to two AI start-ups: NeuroTech Africa and NileAGI.com
YEESI Lab PI talked to two AI-tech start-ups that have shown interest to work and collaborate with YEESI Lab and Sokoine University of Agriculture. He has agreed with the founders to establish official agreement that shall make SUA support the startups on computing nodes and ML tech collaborations.
YEESI Lab is a youth led initiative for youth of our great nation Tanzania. With the help of our partners, we are looking to organize and collaborate with AI-tech start-ups around the country and make positive collaborations.
14 March 2022
Being a learner through Problem based Learning
YEESI Lab has started offering training through Student-centred curriculum using PBL approaches.
Innovators are welcome to free learn anytime anywhere...
learn About YEESI from Dr Fue
https://www.youtube.com/channel/UCzuDdrNLhLW-zsriwrZ9L0A
Learn from Dr Masamaki
https://www.youtube.com/c/JosephMasamakiSUA
Learn from Catherine and Deus
YEESI Lab meets ‘Anzisha Venture Capital”
Yesterday it was a wonderful day when the PIs of YEESi Lab had meeting with a team from “Anzisha”.
The meeting started at 5:00 pm and it ended at around 7:30pm. The central agenda of the meeting was geared towards how to link the innovation lab hosted at SUA with the Anzisha and Venture Capital from Germany. Anzisha has a mission of venture building and bridging pre-seed startup funding gaps in Tanzania. This will add value and complement the mission of the YEESI lab initiative. Since YEESI lab aims at producing startups and spin-off companies from machine vision in Agriculture, it is our hope Venture Capital organizations such as Anzisha will help us during commercialization and scaling up the entrepreneurial and innovative products. Anzisha will also act as ‘Accelerator’. In the near future, we are anticipating having joint and participatory activities in different entrepreneurial and innovations phases between YEESi Lab and Anzisha. The expectant innovators and entrepreneurs from YEESI need to learn how to navigate in corporate venture capital from their day one of their training.
For more information about Anzisha, visit:
https://tz.linkedin.com/company/anzisha-ventures?trk=public_profile_topcard-current-company
10 Feb 2022
AfDB - RECRUITMENT OF A START-UP FINANCE SPECIALIST
Call for Proposals: Transitioning Women Entrepreneurs From the Informal to the Formal Economy
YEESI LAB ONLINE COURSES TO BOOST STUDENTS/TECHNOLOGISTS KNOWLEDGE ON MACHINE VISION HAVE BEEN LAUNCHED
I would like to let you know that we have launched the YEESI lab today. And we would like to share the timetable that you can consult with instructors (https://www.yeesi.org/instructors).
We have a tight timetable to follow and some of you have started University Examination.
However, all the instructors have already recorded.
We shall have an intensive 2-month training course using videos and online discussions.
You may access the timetable here:
https://docs.google.com/spreadsheets/d/1YaoiOqUnbxR0taPfSx6n6NWbl0A9WBUEYwU61s4caZM/edit?usp=sharing
You can contact an instructor directly by checking their contact information from the web: https://www.yeesi.org/instructors or emails seen here at CC.
Instructors will share links to the google meeting or zoom meeting. Instructors may share their meeting IDs on this email.
07-Feb-2022
PI, co-PI, and Instructors from YEESI Lab have won MACHINE LEARNING/VISION RELATED PROPOSALS IN THE SUA RESEARCH AND INNOVATION SUPPORT (SUARIS) second PHASE
The following five instructors from YEESI Lab have won SUARIS funding
1. Towards a Nationwide Automatic Irrigation Scheduling System using Geospatial Artificial Intelligence (NAISS-GAI) by Dr K. Fue
2. Intelligence-based technology for reducing crop-raiding by elephants and its effectiveness in preventing human-elephant conflict in Tanzania by Jamal F. Banzi
3. Plant pests prediction and Emerging Disease detection using Image processing, webGIS, and machine learning Techniques (PREDiCT) by Dr Micahel P. J. Mahenge
4. An Integrated Machine Learning Internet of Things (IoT) –Adaptive Smart Cloud Farming System for Small-Scale Farmers in Tanzania by Dr Alex Alcardo B.
5. Towards Breeding for Fall Armyworm Resistant Maize Varieties: A Deep Learning Computer Model for Shortening the Selection Cycle by Ms. Rehema Mwawado
Also, our Co-PI (Prof Sanga) is participating in research won by the following staff:
1. Towards Developing a Model to Monitor Rapidly Expanding Cities and its Impact on Urban Agricultural Land using Time Series Remote Sensing Images: A case of five Tanzania Growing Cities by Dr Neema Sumari
2. Analysis of Electronic Waste Management Practices in Tanzania: The Case of Selected cities by Dr Respikius Martin
For more information visit: https://www.sua.ac.tz/announcements/list-successful-projects-under-sua-research-and-innovation-support-suaris-2nd-phase
04-Feb-2022
Quad ai workstation arrived at sokoine university of agriculture
Our project lab received a high computing node from Lambda Labs on November, 4th, 2021. The master Quad AI workstation equipped with twin NVIDIA RTX A5000 with intel i9 18-cores processor running on massive 256GB RAM arrived in SUA's premises to support AI research efforts in Agriculture. This is the first modern GPU-installed node to have arrived at SUA. This server has been set to support YEESI Lab developers using JupyterHub server. It can be accessed on (locally) http://10.10.97.236/hub/login or (globally) http://41.59.85.17/hub/login
You are all welcome to explore AI work and support the state-of-the-art AI research in Agriculture.
04-Nov-2022
Jupyterhub server and e-learning system set to implement ai in agriculture
We have configured the JupyterHub server to be shared by peers in our lab to train models. The server can be accessed on (locally) http://10.10.97.236/hub/login or (globally) http://41.59.85.17/hub/login
Also, the e-Learning Portal was deployed at http://41.59.85.2:8390/ to share materials and knowledge in AI.
Recently, the lab developed a comprehensive student-centered curriculum for machine vision. The curriculum features Problem-based Learning courses such as Problem-solving and Program Design with Python, Introduction to Digital Agriculture, Mobile Application Development, Machine Learning in Agriculture, Machine Vision in Agriculture, and Entrepreneurship for Artificial Intelligence that will empower the youth of Morogoro on next-generation AI research and development.