Smart Agriculture
Data-Driven Validation of Plant-Based Organic Liquid Fertilizers and Pesticides: A Field Data Analytics and Decision-Support Framework for Smallholder Farmers
An applied data science project that collects multi-location field trial data on organic liquid inputs and uses statistical modelling and a simple mobile tool to help smallholder farmers choose effective, low-cost doses.
- 0
- Progress
- 6
- Researchers
About this project
This project treats on-farm validation of plant-based organic liquid fertilizers and pesticides as a data and computing problem. Field trial and laboratory data are collected through offline mobile forms and stored in a structured, open dataset. Statistical and machine learning models then relate formulation, dose and environmental conditions to crop yield and pest response, and cost-benefit analytics compare organic and chemical inputs. The results feed a simple mobile or web decision-support tool that recommends dose and application schedules to smallholder farmers in plain language. All data, code and findings will be shared openly.
Objectives
- Build a structured open dataset of field trial and lab analysis results
- Apply statistical and machine learning models to predict crop and pest response
- Compare organic and chemical inputs through cost-benefit analytics
- Develop a simple mobile or web decision-support tool for farmers
- Publish open data, code and findings
Research priorities
- Mobile field data collection and data quality
- Predictive modelling on small agricultural datasets
- Dose-response analysis
- Decision-support tools for low-resource users
Sustainable Development Goals
- SDG 2 · Zero hunger
- SDG 9 · Industry, innovation and infrastructure
- SDG 12 · Responsible consumption and production
- SDG 13 · Climate action
Activities
- Field data collection with mobile forms
- Data cleaning and database design
- Statistical analysis and predictive modelling
- Dashboard and visualization building
- Prototype dose-recommendation tool
- Manuscript writing
How to take part
Help design the data schema, analyse trial data, build models or the mobile tool, create dashboards, or co-author publications.
Skills required
- Python
- R
- statistics
- data visualization
- mobile data collection
Roles needed
- Data scientist
- Statistician
- Mobile app developer
- Data annotator
- Field data collector
Who should apply
Students, researchers and developers in data science, statistics, computing, agriculture or related fields. Practical farming experience is also welcome. No formal degree required.
Expected contribution
Follow the data protocol, document code and analysis, share results openly, meet agreed timelines, and acknowledge all contributors in publications.
