Projects

Excellence in Agronomy: Integrated Data Monitoring, Management, and Analytical Platforms

Output: Real-time monitoring dashboard, Data standardization frameworks, ETL pipelines, Spatial data tools, Documentation, and Capacity-building training sessions.

Description:

• Data Collection Monitoring Platform: Created and managed dashboard for near real-time monitoring of field data collection,, enhancing data quality and oversight.
• Data Management: Designed ETL workflows, implemented data standardization frameworks, and maintained databases to support efficient data storage, retrieval, and analysis.
• Spatial Data Tools: Developed and optimized functions for retrieving and standardizing diverse spatial datasets for crop modeling applications.
• API Development: Built APIs to facilitate seamless access to key performance indicators and carob datasets, enabling streamlined reporting.
• Capacity Building: Led training sessions and developed detailed documentation to empower stakeholders with the skills to utilize data and tools effectively.
• Cloud Resources: Leveraged AWS and Azure for scalable and secure storage, deployment, and access to analytics and insights.
• Collaboration: Collaborated closely with cross-functional project teams to customize tools and integrate new use cases effectively.
• Research Contribution: Co-authored articles on CGSpace, shared methodologies, and contributed to knowledge dissemination within the agronomy and CGIAR research community.

Skills: Database Management (PostgreSQL, PostGIS), Data Analysis (R, Python), GIS & Remote Sensing (Google Earth Engine), Data Visualization (R Shiny, Streamlit), Cloud Computing (AWS, Azure, GCP), ETL & APIs (R, Python, JavaScript), Version Control and Collaboration (Git, GitHub).

Excellence in Agronomy: Sampling Framework for Validation Trials

Output: Interactive web-based interface, Scripts translation, High-resolution maps and documentation.

Description: Sampling Framework platform enables users to select optimal locations for validation trials.

• Interactive Web Interface: Built an interactive, user-friendly web interface that allows stakeholders to visualize and select optimal trial locations dynamically, leveraging **Google Earth Engine** for real-time data exploration.
• Algorithms: Translated and implemented location selection algorithms scripts using **Google Earth Engine**, **Python**, and **JavaScript** to ensure that trial sites are geographically diverse and representative of targeted areas.
• Spatial Data Analysis: Leveraged high-resolution satellite imagery and spatial data analysis techniques to identify and prioritize the most relevant areas for trial validation, accounting for environmental and geographic factors.
• Collaboration: Collaborated with agronomy experts and project teams to refine the tool and ensure it met the specific needs of validation trials for different crop types and project objectives.

Skills: GIS & Remote Sensing (Google Earth Engine, QGIS), Data Analysis (Python), Web Development (JavaScript), Spatial Data Analysis, Location-based Algorithms, Cloud Computing (Google Cloud Platform).

ICT4BXW: Banana Xanthomonas Wilt (BXW) Early Warning System

Output: Real-time disease monitoring dashboard, Automated workflows for risk assessment, High-quality impact reports, Interactive web-based dashboard, Multitemporal spatial analysis.

Description: The project includes creation of an interactive dashboard for real-time monitoring of disease incidence and automated analysis to predict Banana Xanthomonas Wilt (BXW) disease outbreaks and climate change impacts on disease spread.

• Dashboard Development: Designed and developed an interactive dashboard for real-time monitoring of Banana Xanthomonas Wilt (BXW) disease, providing timely alerts to farmers and stakeholders for early intervention.
• Automated Analytical Workflow: Automated the analytical workflow for assessing and communicating disease risk.
• Spatial Analysis: Conducted multitemporal analysis using remote sensing to monitor spatial and temporal changes in BXW incidence.
• Decision Support: Delivered high-quality impact reports, enabling effective decision-making and strategic planning to mitigate disease spread.
• Collaboration: Worked with local research teams and farmers to validate the tool’s efficacy, tailoring recommendations based on regional disease patterns and conditions.

Skills: GIS & Remote Sensing (ArcGIS, Google Earth Engine), Data Analysis (R), Machine Learning, Web Development (HTML, CSS, JavaScript), Data Visualization (R Shiny, Dash Plotly), Spatial Data Analysis, Cloud Computing (AWS, Digital Ocean, Firebase), Version Control and Collaboration (Git, GitLab, GitHub).

Diet Quality Monitoring System in Rwanda

Output: Real-time diet quality analysis platform, Crowdsourced data integration, Visualizations of dietary trends, Impact reports for food security.

Description: A Diet Quality Monitoring system with integrated crowdsourced data to assess dietary habits in Rwanda.

• Dashboard Development: Developed a system to monitor and analyze diet quality in Rwanda using crowdsourced data and digital tools, enabling real-time insights into dietary patterns across the population.
• Data Integration and Analysis: Integrated crowdsourced data from multiple platforms for in-depth data analysis to identify dietary trends accross various socio-economic groups.
• Visualization and Reporting: Shared visualizations to communicate insights on diet quality and trends, supporting project goals in food security strategies.
• Research Contribution:: Co-authored articles and contributed to knowledge dissemination via automated reports that informed decision making based on diet quality data.
• Collaboration: Worked closely with local and international partners to ensure the accuracy and relevance of data and the platform.

Skills: Data Collection (ODK, ONA), Big Data Analytics, Data Analysis (R), Visualization (Tableau, Dash Plotly), Web Development (HTML, CSS, JavaScript), Data Integration, Cloud Computing (AWS, Digital Ocean), Version Control and Collaboration (Git, GitHub).

RTB Crops Climate Suitability Platform

Output: Real-time climate suitability analysis platform, Interactive dashboard, Automated data pipelines.

Description: Created a platform to assess climate suitability for Root and Tuber Crops (RTB) under changing climate conditions. The platform provided vital insights for farmers in the Great Lakes Region.

Skills: GIS & Remote Sensing, Data Analysis (R), Data Visualization (R Shiny), Cloud Computing (AWS-S3).

RTB Pests and Diseases under Climate Change

Output: Automated analytical workflows, Risk assessment models, Real-time impact analysis platform, High-quality research reports.

Description:

• Automated Workflow Development: Developed automated workflows for assessing the impacts of climate change on pests and diseases affecting RTB (root, tuber, and banana) crops in the Great Lakes Region using machine learning techniques.
• Risk Assessment Models: Built and implemented machine learning-based models to predict pest and disease risks under various climate scenarios, integrating climate data and pest/disease incidence records.
• Data Analysis: Performed spatial and temporal analysis using R and Python, analyzing historical pest and disease data, climate variables, and environmental conditions to forecast future trends.
• Research Contribution: Authored research papers and high-quality reports based on the analysis, sharing findings and recommendations for improving pest management under changing climatic conditions.
• Collaboration: Collaborated with agricultural scientists, climate experts, and local communities to validate models and adapt them to regional pest management strategies.

Skills: Data Analysis (R, Python), Machine Learning, Climate Modeling, Spatial Data Analysis, Data Visualization (Tableau, Power BI), GIS & Remote Sensing (QGIS, Google Earth Engine), Cloud Computing (AWS), Research Documentation.

ICT4BXW: Crop Identification, Mapping, and Change Detection

Output: Automated crop classification models, Change detection algorithms, High-resolution maps, Real-time crop monitoring platform.

Description:

• Automated Crop Classification: Developed automated models for crop identification and classification using remote sensing data, integrating machine learning techniques.
• Change Detection Algorithms: Designed and implemented algorithms for detecting changes in land cover and crop patterns over time, leveraging satellite imagery and temporal data.
• Spatial Data Analysis: Performed detailed spatial analysis to assess crop distribution, health, and changes, using GIS tools such as QGIS and Google Earth Engine.
• Research Contribution: Authored research papers and high-quality reports based on the analysis, informing stakeholders about banana crop distribution accross the country and land use changes which are largely attributed to disease spread and poor management.
• Collaboration: Collaborated with agricultural experts and local farmers to ensure model validation and relevance for real-world applications in crop mapping and change monitoring.

Skills: Crop Mapping (Google Earth Engine, ArcGIS, R), Remote Sensing, Machine Learning, Change Detection, Data Analysis (R), GIS & Spatial Data Analysis, Data Visualization, Cloud Computing (GCP).