Today, on June 23, 2023, at the EPAL Lab, a sister lab of YEESI Lab, we had a training session on NLP, specifically focusing on Sentiment Analysis for the Zindi Africa COVID-19 Tweet Classification Challenge. This competition provided an opportunity to enhance our skills in handling data compared to the recent hackathon, the Customer Sentiment Analysis Challenge for the Telecom Sector in Tanzania, which concluded just a day ago. In that competition, the YEESI Lab, along with our sister lab EPAL, achieved fourth place with the outstanding performance of Ms. Jackline.

During today's session, we had participants Mwajabu A Mgamba and Gervas Abel Lusele, among others, who shared their thoughts and experiences.

Mwajabu A Mgamba, a third-year student at Sokoine University of Agriculture in Irrigation and Water Resources Engineering, said "My name is Mwajabu A Mgamba. Today, in the Yees Lab, we learned about the modifications to the previous training regarding sentiment analysis and how to train COVID-19 tweet analysis. Now we have expanded our knowledge on training and testing these analyses".

Gervas Abel Lusele shared his insights, stating, "My name is Gervas Abel Lusele. Today's session revolved around the Zindi challenge, which involves creating and training a model to analyze the sentiment of COVID-19 tweets. We discussed the code used in the previous IndabaX challenge, specifically focused on sentiment analysis for telecommunication tweets in Tanzania. Drawing on our knowledge from that challenge, our goal is to develop a superior model that accurately predicts the sentiment of COVID-19 tweets. We aim to build upon our previous experience and utilize the insights gained to create the best possible solution for analyzing sentiments related to the pandemic."

These participants, along with the rest of the group, are motivated to apply their newfound knowledge and skills to excel in the Zindi Africa COVID-19 Tweet Classification Challenge. By leveraging their expertise and incorporating grammatical English, they are determined to create a top-performing model and contribute to the field of sentiment analysis during the ongoing pandemic.

23 June 2023

Congratulations to Ms. Jackline Ulenje, studying BSc in Irrigation and Water Resources Engineering, SUA for her achievements in the Tanzania IndabaX Hackathon 2023 and the SUA YEESI Lab! Placing 4th in the Tanzania IndabaX Hackathon is a great accomplishment, and it shows her skills and dedication in the field of hacking and problem-solving. Additionally, securing 1st place in the SUA YEESI Lab further demonstrates her expertise and the recognition she has received for her model. It's fantastic to see Jackline's hard work paying off, and I wish her continued success in her future endeavours.

Congratulations to Mr Barnabas Nsonga and Mr Stephano Mashauri for placing 2nd and 3rd in the hackathon. You have devoted so much invaluable time to learning and improving your Machine Learning skills. 

Keep it Up!

22 June 2023

It feels so good to hear that students at Sokoine University of Agriculture (SUA) are participating in the Tanzania IndabaX Hackathon 2023! Hackathons provide an excellent platform for students to showcase their skills, creativity, and problem-solving abilities in the field of machine learning.

The Tanzania IndabaX Hackathon is an exciting event that brings together aspiring data scientists, researchers, and technology enthusiasts from across Tanzania. Participants collaborate in teams to tackle real-world challenges using machine learning techniques and methodologies. These challenges could range from data analysis and predictive modelling to computer vision and natural language processing.

Participating in hackathons like Tanzania IndabaX Hackathon 2023 allows students to apply their knowledge of machine learning algorithms, data preprocessing, feature engineering, and model evaluation in a competitive and time-constrained environment. They have the opportunity to work with large datasets, explore various machine learning frameworks and libraries, and implement innovative solutions to solve complex problems.

Hackathons not only foster technical skills but also promote teamwork, communication, and critical thinking. Students will collaborate closely with their teammates, sharing ideas and expertise to develop creative solutions. They will also have the chance to network with industry professionals and mentors who can provide valuable guidance and insights into the field of machine learning.

By participating in the Tanzania IndabaX Hackathon 2023, students from Sokoine University of Agriculture will gain practical experience, enhance their problem-solving abilities, and broaden their understanding of machine learning applications. It is an exciting opportunity for them to showcase their talent, learn from their peers, and contribute to the advancement of data science in Tanzania.

With the support from the YEESI Lab project and EPA Lab, the event will continue from tomorrow 20th to the 21st of June 2023 at Mazimbu Video Conference Room in which each student will be training models individually and submitting them. 

Wishing all the participants from SUA the best of luck in the Tanzania IndabaX Hackathon 2023!

19-June-2023

The Electronics and Precision Agriculture Lab (EPAL Lab), a sister lab of YEESI Lab, held a session on Sunday, 18th June 2023, that was focusing on Natural Language Processing (NLP) and Sentiment Analysis. The workshop was led by Mr Dickson Massawe, a 4th year Ag Engineering student. The team successfully fine-tuned a pre-trained model from Hugging Face specifically for AutoTokenization, enabling them to classify SMS messages as either spam or ham. This process involved training the model on a labelled dataset of SMS messages and optimizing its ability to accurately differentiate between spam and ham messages. Sentiment analysis is a valuable technique in NLP that allows for the understanding and classification of the sentiment expressed in text data.

One of the participants, Ms Jackline Joel Ulenje, introduced herself as a third-year student at Sokoine University of Agriculture, specializing in irrigation and water resources engineering. During the YESSI Lab training session on June 18, 2023, Jackline expressed her engagement with Sentiment Analysis. She mentioned her ability to utilize various libraries, perform training on datasets, and conduct tests to effectively solve problems. This indicates her preparedness for the upcoming IndabaX competition, suggesting a solid understanding of the concepts and techniques covered during the session.

Another participant, Ms Mwajabu Mgamba, shared her insights from the session. They focused on the topic of sentiment analysis with Hugging Face, specifically discussing aspects such as word tokenization, changing label dummy variables, and label encodings. Word tokenization involves splitting text into individual words or tokens, which is a crucial step in many NLP tasks. Changing label dummy variables and label encodings are techniques used to represent and process categorical data in machine learning models. Mwajabu's comments suggest an understanding of the practical application of sentiment analysis using the Hugging Face library.

Overall, the EPAL Lab session provided participants with valuable knowledge and hands-on experience in Natural Language Processing and Sentiment Analysis. Through fine-tuning a pre-trained model from Hugging Face for AutoTokenization, the team was able to successfully classify SMS messages as spam or ham. The comments from participants like Jackline Joel Ulenje and Mwajabu Mgamba indicate their readiness for the upcoming IndabaX competition, demonstrating their proficiency in using libraries, training models, and applying various techniques in NLP.

18-June-2023

Electronics and Precision Agriculture Lab (EPAL Lab), a sister lab of YEESI Lab, has shown remarkable progress in the machine learning training for the QoS Prediction Challenge by ITU AI/ML in the 5G Challenge. EPAL conducted a hands-on training workshop for SUA students (10 males and 3 females) on Saturday, 11/06/2023 which was done by Mr Dickson Massawe. With meticulous analysis of the dataset, they identified crucial features for accurate QoS prediction. By employing robust data cleaning techniques, EPAL Lab ensured data integrity and reliability, enhancing the quality of predictions. Through continuous evaluation and refinement, their team achieved exceptional results, surpassing the challenge benchmark. 

Members of EPAL Lab showcased expertise and innovation in model training, highlighting their excellence in machine learning. They created accurate submission files adhering to challenge requirements and promptly submitted them, validating the quality and reliability of their trained model. EPAL Lab's remarkable progress and dedication position them as strong contenders, eagerly awaiting evaluation and results, confident in their positive impact on the field of machine learning.

The students submitted solutions to the web; https://zindi.africa/competitions/qos-prediction-challenge/leaderboard 

10-June-2023

It is exciting news that the Yuneec H520e drone has been delivered to the Lab for multispectral data collection in the field of machine vision work. The availability of this advanced drone technology will significantly contribute to research and analysis in agriculture in Tanzania.

The objective of utilizing the Yuneec H520e drone is to gather multispectral data that will aid in validating theories related to crop emergence, crop vigor measurement, and identification of crop failure caused by pests or diseases. By capturing high-resolution images from the drone, researchers will be able to visualize and analyze the crops, further enhancing their understanding of crop characteristics.

The YEESI Lab students will have the opportunity to work with UAV imagery data and gain practical experience in analyzing and interpreting crop-related information. They will learn how to leverage artificial intelligence (AI) programs to characterize crops, estimate crop vigor, identify crop failure, and even count seedlings. This hands-on experience will not only enhance their technical skills but also foster a deeper understanding of precision agriculture and its applications.

The drone is currently located at the Electronics and Precision Agriculture Lab, providing a centralized location for students and researchers to access and learn about unmanned aerial vehicles (UAVs). This facility will serve as a hub for exploring the potential of drone technology in agricultural research, offering valuable insights into the field of precision agriculture.

It is noteworthy that the Yuneec H520e drone is the first of its kind to be delivered in Tanzania, making it a significant milestone for the country's research and technological advancements in agriculture. The introduction of this cutting-edge drone technology opens up new possibilities for data collection, analysis, and decision-making processes in the agricultural sector.

Overall, the arrival of the Yuneec H520e drone to the Lab marks a significant step forward in the application of UAV technology for agricultural research and analysis in Tanzania. The opportunity for students to engage with this advanced equipment and explore the potential of UAV imagery data will undoubtedly contribute to their academic and professional growth, as well as to the advancement of precision agriculture practices in the country.

3rd June 2023

Mr Fikiri Matatizo, a YEESI Lab and 3rd year PIT Student at Sokoine University of Agriculture placed first in a pitch competition that was conducted at the National Carbon Monitoring Centre (NCMC). Mr Fikiri presented his final year project in which he is developing a "Plant Diseases Detection and Monitoring App". This mobile app is using machine vision technology (AI-based) to detect diseases affecting tomato plants. Mr Fikiri is using the YEESI dataset to train the model. The app is based on Flutter technology with its backend using TensorFlow Lite. Mr Fikiri is advised on this project by the YEESI Lab PI, Dr Kadeghe Fue.

The pitch competition awarded the leading candidate with 1.5 Million TZS and the winner certificate, the 1st runners-up took 1.0 Million TZS while the 2nd runners-up took home 0.5 Million TZS.

Congratulations Mr Fikiri for the milestone achieved. The sky is the limit.

for more information, http://suamedia1994.blogspot.com/2023/05/sua-yaboresha-mitaala-yake-kuzalisha.html

23-May-2023

The Tanzanian agriculture industry faces a great challenge caused by pests and diseases threatening food security. Pests such as tomato leaf miners, aphids, fall armyworms (FAW), and bean leaf miners devastate crops. Also, diseases such as maize streak virus, early blight, Powdery mildew, Leaf spot, Rusty brown leaf, foliar disease, Bacterial Wilt, Blossom end rot, Flower abortion, Leaf Curl and Black rot have caused the crop failure that leads to yield reduction. So, precisely and accurately detecting such pests and diseases to improve agriculture productivity in the country is paramount. However, manual detection is cumbersome, time-consuming and costly. So, automating the procedure using machine vision technologies is necessary for sustainable prosperous agriculture.

Therefore, this dataset presents the first Tanzanian agricultural classification dataset that contains 7992 healthy and unhealthy crops images (maize, beans, green peppers, onions, okra, watermelons, sunflowers, African eggplants, tomatoes, Chinese cabbage, hot peppers, wheat, leaf kale and cabbage). Images were collected in real-world conditions in Morogoro, Tanzania, in August and September 2022, using smartphones and professional GoPro Hero 9 cameras. The dataset is called YEESI Dataset. It is used as Open Data. The authors expect this dataset to revolutionize applications of Artificial Intelligence (AI) in agriculture for evaluating classification models related to crop pests, diseases and weed problems from open data.

The dataset can be found here: doi: 10.5281/zenodo.7729285 or here https://www.zenodo.org/record/7729285

It can be cited as; Fue, Kadeghe, Barakabitze, Alcardo, Geofrey, Anna, Lebalwa, Bertha, Lyimo, Neema, Mwaipaja, Faraja, Jonathan, Joan, Mbacho, Susan, Sanga, Camilius, Rains, Glen (2022) The YEESI Lab Dataset. doi: 10.5281/zenodo.7729285

My name is Restuta George, I am a first-year student at Sokoine University of Agriculture pursuing a Diploma in IT. My experience at the YEESI Lab Weekend provide me with exposure to understanding what machine learning actually is and how it can help to solve problems facing societies, as well as techniques involved in machine learning. The weekend was an amazing opportunity to learn and enhance my skills in artificial intelligence, data science and machine learning. I am committed to working hard on AI/ML .so that I can be able to solve real-world problems. Thanks

By Restuta George, 08-May-2023

During the YEESI lab weekend, I experienced many things in machine learning;

• first it's due to punctuality in training models 

• I improve my experience in ML by training models more and more through learning various algorithms configuration concerned with machine learning to develop the best model

• Also It is good  that the YEESI lab helps us to get together and encourages us to participate in machine learning hackathons and open up our minds 

• Finally, through the Weekend YEESI lab I got a view on how to train models and how to read and practice machine learning 

 By Ibrahim Meshack, 08-May-2023

The YEESI lab competition that took place extensively for about two days has introduced me to the world of extreme programming and software development, and how hard and challenging coding can be.

 I actually didn't know how artificial intelligence and machine learning worked well and as a beginner I realized where to start, python packages, and engineering features which seems to be more complicated during the competition, 

 Up to date, I believe I can improve and become good Artificial Intelligence tech developer. 

 By Stephano Mashauri, 08-May-2023

Predicting biomass from satellite images is a challenging and important task in the field of remote sensing and environmental monitoring. By working on this project, I have likely gained valuable experience in various aspects of machine learning, such as data preprocessing, feature engineering, model selection, and hyperparameter tuning.

 Specifically, I may have learned techniques for handling large and complex datasets, extracting relevant features from remote sensing data, dealing with imbalanced and skewed data, and selecting appropriate machine learning models for regression tasks. I may have also gained insights into the use of convolutional neural networks (CNNs) for image processing and deep learning.

 Overall, the skills and knowledge I acquired during this project can be applied to a wide range of machine-learning tasks in various domains, such as environmental monitoring, agriculture, and forestry. These skills can help me become a better data scientist and make meaningful contributions to the field of machine learning.

 By Dickson Massawe, 08-May-2023

YEESI lab weekend was concluded in style where the best-performing students were awarded prizes. Mr Dickson Massawe, Mr George Munishi and Mr Fikiri Matatizo consecutively performed extraordinarily and amassed home the whole YEESI Lab fortune. The YEESI Lab team send their appreciation to all participants. Their eagerness to win was everything that we were building for the past two years. We wish our students all the best as they continue competing in Africa Biomass Challenge and take the $10,000 prize that has been allocated for this competition by GIZ.

Why Hackathons?

Machine learning hackathons can be important to improve students' understanding of machine learning in several ways. Firstly, hackathons offer students an opportunity to apply their theoretical knowledge to real-world problems, helping them to develop practical skills and gain hands-on experience in machine learning. Secondly, hackathons provide an environment for students to collaborate with peers and experts in the field, promoting teamwork and networking. Finally, hackathons can help students to build their confidence in their abilities and to learn from their mistakes, enabling them to approach future challenges with a growth mindset. Overall, machine learning hackathons can be a valuable tool for students to develop their skills and deepen their understanding of the field.

View Album: https://photos.app.goo.gl/5nEzC94mZekpzJ8c9 

07-May-2023

An AI Hackathon Invite for members of YEESI Lab

Register for this Machine Vision Hackathon that will be held in-person on 5th, 6th and 7th of May 2023 at SUA Mazimbu Campus -- Video Conference Lab. SUA YEESI Lab is going to award best YEESI Lab students who places the best on the Zindi  Africa Biomass Challenge that can be accessed here  https://zindi.africa/competitions/africa-biomass-challenge. You can start attempting the problem ASAP but we shall conclude on Sunday the 7th of May 2023 by 5:00 pm.

The Challenge

Cocoa farming has driven massive deforestation in West Africa: Cote d’Ivoire, the world’s leading exporter, has seen a loss of 80% of its forests since its independence in 1960. While cocoa is traditionally grown in monoculture, it can thrive under the shade of tall trees. To restore some of the lost tree cover, the planting of shade trees is a high priority for the country as well as the private sector, to reverse the impacts of deforestation and improve carbon sequestration by African tropical forests.

In this challenge, your objective is to predict biomass in shaded regions in Cote d’Ivoire based on GEDI, Sentinel-2 and ground truth biomass data. Remote monitoring of the increase of biomass will help measure the impact of reforestation efforts in Cote d’Ivoire as well as detect degradation of forests due to cocoa, without requiring expensive and labor-intensive biomass estimates on the ground.

What is the Purpose of this challenge?

The purpose of this challenge is to encourage YEESI Lab members to prepare for machine learning hackathons that will be conducted by the YEESI Lab later this year. You may use our GPU node: http://yeesi.sua.ac.tz/ 

Awards

The best model is $100 (TShs 250,000). The next two runners' up will take home $50 (TShs 120,000) each. The top ten will go home with the active participation certificate.

You are warmly welcome.

In 2023, the PEER program marks its 12th year of funding science around the world leading to better science capacity and significant policy change in many lower- and middle-income countries. This year, PEER continues with its series of webinars to provide principal investigators (PIs) the chance to present their work and showcase its impacts.

This webinar is the fourteenth in the series. It features Dr. Kadeghe Fue, a researcher and lecturer at Sokoine University of Agriculture in Morogoro, Tanzania and Dr. Glen Rains, a professor at the University of Georgia in the United States. Dr Fue is the PI of PEER Project 9-456: Morogoro youth empowerment through establishment of social innovation (YEESI) lab for problem-centered training in machine vision which seeks to develop artificial intelligence (AI) capacity in Tanzania and harness AI to improve agricultural output in the country. This effort is centered around the newly-formed YEESI lab at the Sokoine University of Agriculture. 

Abstract

Despite having advanced technology and reliable internet in the cities, Tanzania still lags in AI research and technologies. Most universities have not incorporated AI into degree programs and courses due to the limited number of professors with expertise in the field. Even with this consideration, AI is positioned to change and advance agriculture, a sector that employs to 60% of Tanzania’s workforce. AI can locate and characterize the emergence of diseases, pests, and weeds early, as well as count emerging seedlings for replanting, significantly increasing farmers' profits.

To address the gap in training, reduce the AI divide, and democratize the use of AI technologies, specifically in Machine Vision, while capitalizing on the potential of AI in agriculture, the YEESI lab was established at the Sokoine University of Agriculture. The lab recruited more than 100 students trained in five subjects:  Machine Learning in Agriculture, Introduction to Digital Agriculture, Problem-solving and Program Design with Python, Entrepreneurship for Artificial Intelligence and Mobile App Development. The lab also participated in an AI hackathon in which agricultural students won prizes for machine learning technologies that demonstrated the practicality of the lab’s problem-based curriculum. 

https://vimeo.com/809069869

Mar 14, 2023

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/

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

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

Ms Joan (Yellow shirt) with farmers and extension agents from Dakawa wards

Ms Anna and Ms Bertha at Mkuyuni village

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

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

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

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.

https://www.datascienceafrica.org/dsa2022arusha/speakers/

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

https://www.yeesi.org/

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 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

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 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

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

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

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