The objective of this project is to apply in practice what we have learnt during lectures with the purpose of becoming familiar with software engineering practices and be able to address new software engineering issues in a rigorous way. The project includes two assignments:
- The preparation of a Requirement Analysis and Specification Document (RASD) for the problem provided.
- The definition of the Design Document (DD) for the system considered in point 1 above.
Data-driven Predictive Farming in Telengana, or DREAM, is an elaboration of an initiative presented to the professors of the class by their colleagues working at UNDP India, a United Nations division and within the Healthsites initiative. The initiative was promoted by Telengana's government.
Agriculture plays a pivotal role in India’s economy as over 58% of rural households depend on it as the principal means of livelihood, 80% of whom are smallholder farmers with less than 2 hectares of farmland. More than a fifth of the smallholder farm households are below poverty. Globally there will be 9.7 billion people in the world by 2050 (as per a recent UN estimate). Food demand is expected to increase anywhere between 59% to 98% by 2050 (source Harvard Business Review). Climate change continues to be a real and potent threat to the agriculture sector, which will impact everything from productivity to livelihoods across food and farm systems and is predicted to result in a 4%-26% loss in net farm income towards the end of the century. This calls for a revamp of the entire mechanism that brings food from farms to our plates. The COVID-19 pandemic has greatly highlighted the massive disruption caused in food supply chains exposing the vulnerabilities of marginalized communities, small holder farmers and the importance of building resilient food systems. It has become even more important now that we develop and adopt innovative methodologies and technologies that can help bolster countries against food supply shocks and challenges.
Telengana’s long-term goal: Telengana is the 11th largest state in India with a geographical area of 112,077 km2 and 35,193,978 residents (data from 2011) (see https://en.wikipedia.org/wiki/Telangana for more details). The goal of Telengana’s government is to design, develop and demonstrate anticipatory governance models for food systems using digital public goods and community-centric approaches to strengthen data-driven policy making in the state. This will require the involvement of multiple stakeholders, from normal citizens to policy makers, farmers, market analysts, agronomists, etc.
In the first place, Telengana wants to partner with IT providers with the aim of acquiring and combining:
- Data concerning meteorological short-term and long-term forecasts. Telengana already collects and makes available such data (see https://www.tsdps.telangana.gov.in/aws.jsp).
- Information provided by the farmers about their production (types of products, produced amount per product).
- Information obtained by the water irrigation system concerning the amount of water used by each farmer.
- Information obtained by sensors deployed on the territory and measuring the humidity of soil.
- Information obtained by the governmental agronomists who periodically visit the farms in their areas.
Acquiring and combining such data, DREAMS will support the work of three types of actors: policy makers, farmers, and agronomists. In the following we describe the needs of each category of actor.
They want to:
- Identify those farmers who are performing well, especially when they demonstrate to be resilient to meteorological adverse events, as these farmers will receive special incentives and will be asked to provide useful best practices to the others.
- Identify those farmers who need to be helped as they are performing particularly badly.
- Understand whether the steering initiatives carried out by agronomists with the help of good farmers produce significant results.
They want to:
- Visualize data relevant to them – for instance, weather forecasts, personalized suggestions concerning specific crops to plant or specific fertilizers to use – based on their location and type of production.
- Insert in the system data about their production and any problem they face.
- Request for help and suggestions by agronomists and other farmers.
- Create discussion forums with the other farmers.
They want to:
- Insert the area they are responsible of.
- Receive information about requests for help and answer to these requests.
- Visualize data concerning weather forecasts in the area and the best performing farmers in the area.
- Visualize and update a daily plan to visit farms in the area, assuming that all farms must be visited at least twice a year, but those that are under-performing should be visited more often, depending on the type of problem they are facing.
- Confirm the execution of the daily plan at the end of each day or specify the deviations from the plan.
Supervisor: Professor Tamburri Damian Andrew