Projects & Work
A selection of technical and research projects by Badejo Adegoke Timothy — spanning agricultural data tools, mobile applications, and statistical modelling work.
A cross-platform mobile application built with Flutter that enables smallholder farmers to monitor crop health, soil moisture, and weather data in real time using IoT sensor integration and an intuitive dashboard.
AgroSense is a proof-of-concept mobile application designed to bridge the digital divide in smallholder agriculture. The app pulls data from affordable IoT soil sensors and weather APIs, presenting actionable insights — irrigation alerts, pest risk forecasts, and yield tracking — in a simple, low-data-usage interface suited for rural connectivity environments.
Key Features:
- Real-time soil moisture and temperature readings from IoT sensors
- Automated irrigation recommendations based on crop type and growth stage
- Offline-capable data caching via Firebase Firestore
- Multi-language support for broader farmer accessibility
A data analytics web application for farm managers to track crop yield trends, livestock health records, and financial performance — turning raw farm data into actionable management insights.
Built to address real operational needs encountered at Sonik Farms, this dashboard consolidates production records, health logs, and financial data into a single interface. Charts and trend lines help farm managers identify underperforming crop blocks, track livestock mortality rates, and forecast monthly revenue — all without requiring specialist data skills.
Key Features:
- Crop yield heatmaps by plot and season
- Livestock health event timeline with medication tracking
- Monthly P&L summaries and input cost breakdowns
- Export reports as PDF for management review
An R-based statistical model that predicts early maize variety yields based on organomineral fertiliser treatments, soil parameters, and rainfall patterns — extending the research from the FUNAAB undergraduate thesis.
Developed as an extension of the undergraduate thesis on organomineral fertiliser effects on Zea mays, this project applies multiple regression and ANOVA techniques in R to build a predictive yield model. The model factors in fertiliser application rate, soil pH, organic matter content, and cumulative rainfall during the growing season.
Key Outcomes:
- Identified optimal N:P:K ratios for specific soil classes in southwest Nigeria
- Produced visualisations of growth-stage interactions using ggplot2
- Model validated against field trial data with R² > 0.78
- Findings applicable to extension services advising smallholder maize farmers
A personal portfolio and academic profile website built with TanStack Start, React 19, and Tailwind CSS — SEO-optimised to establish a searchable online presence for Badejo Adegoke Timothy.
This website serves as the primary digital presence for Badejo Adegoke Timothy, combining a professional resume, project showcase, and blog into a single fast, accessible, and SEO-optimised platform. Built with TanStack Start for server-side rendering and deployed on Netlify for global CDN delivery.
Technical highlights:
- Structured data (JSON-LD) for rich Google search results
- Content Collections for type-safe markdown content management
- AI-powered resume assistant for interactive exploration of experience
- Netlify Forms integration for direct contact