Agriculture is an essential sector in Tanzania, contributing to the country’s economy and providing livelihoods for the majority of the population, with agriculture accounting for over 75% of employment and contributing to about 30% of the country’s GDP. However, the country faces various challenges in the agricultural sector, including low crop yields, post-harvest losses, and limited access to markets.
Crop yield is affected by various factors such as weather conditions, soil quality, and pest infestation, while food loss occurs due to poor harvesting, storage, and transportation practices. These challenges have contributed to food insecurity and poverty in the country. Addressing these challenges requires innovative solutions that leverage technology and data analysis.
The problem of low crop yield and food loss in Tanzania is complex and multifaceted. Despite the efforts made by the government and other stakeholders to address the issue, the problem persists, and farmers continue to experience low yields and significant food loss. The traditional methods of addressing these challenges have not been adequate, and there is a need for innovative solutions that leverage technology and data analysis.
– Kick-off meeting with the team to discuss project goals and objectives, including the problem statement, the scope of the project, and the desired outcome.
– Assign roles and responsibilities to team members.
– Conduct research on the current state of crop yield and food loss in Tanzania, including data collection and analysis.
– Identify the data sources required for the project, including weather data, soil data, and crop yield data.
– Begin collecting and cleaning the data, including data pre-processing and formatting.
– Conduct exploratory data analysis (EDA) to identify patterns and trends in the data.
– Develop hypotheses on the factors that affect crop yield and food loss.
– Develop machine learning models to predict crop yield and food loss based on the identified factors.
– Train and test the models, and evaluate their performance.
– Optimize the machine learning models to improve their accuracy and performance.
– Conduct sensitivity analysis to identify the most important factors affecting crop yield and food loss.
– Develop a dashboard to visualize the results of the machine learning models and the sensitivity analysis.
– Present the results to the team and stakeholders.
– Identify potential solutions to reduce food loss and improve crop yield based on the results of the machine learning models and the sensitivity analysis.
– Develop a plan to implement the solutions.
– Finalize the project report and present the findings and recommendations to the stakeholders.
– Conduct a retrospective with the team to identify lessons learned and areas for improvement.
– Presenting a report to Omdena local chapters team
The project will have a significant impact on the team participants by providing them with an opportunity to work on a real-world problem and develop their skills in data analysis, machine learning, problem-solving, and communication. The team members will also gain valuable experience in working collaboratively, managing a project, and presenting their findings to stakeholders. Additionally, the project will contribute to the team members’ personal and professional growth by exposing them to new technologies and techniques that can be applied in various domains.