Professor Athula Ginige
Over production of vegetables such as leeks, tomatoes, onions, potatoes etc have been a recurrent news item in Sri Lanka for the last few years. This has resulted in farmers not been able to sell their harvest leading into financial difficulties. To minimise this over production situation farmers will need to know how much of a particular crop has already been planted by others at the time a farmer is deciding what crop to grow. The challenge is first to gather and then to provide this information to farmers at the right time in a form that is easy to access and comprehend.
At present mostly manual methods are used to collect crop production data as well as to communicate useful information back to farmers. These manual methods are incapable of collecting current production volumes on an ongoing basis and inform farmers how much of a particular crop has been cultivated at the time a farmer is planning what crop to grow next.
Over 80% of the population in Sri Lanka now have a mobile phone. Most present day mobile phones have a range of in build sensors. Making use of the high mobile penetration and sensor capabilities the project Social Life Network for Framers in Sri Lanka aims to develop next generation of Social Networks called Social Life Networks (SLN) to support livelihood activities of farmers. This support also includes providing information necessary for farmers to decide what crop they should grow next.
This is an international collaborative research project involving researchers from Australia (University of Western Sydney, Macquarie University, Australian Catholic University), University of California, Irvine in USA, University of Salerno in Italy and Sri Lanka (University of Colombo, University of Ruhuna).
SLN uses advanced information aggregation and knowledge management techniques to derive useful information in real time from user inputs provided via mobile phones. Some examples of information that would be derived in real-time are demand and supply information for commodities and services, market prices, prediction of crop yield etc. Users are provided with easy to use interfaces on their mobile phones to access this aggregated information as well as other information that they need to make decisions related to their livelihood activities (farming techniques, pest control methods, livestock diseases and vaccination etc). This domain specific knowledge is extracted from published authoritative online data sources with the help of domain ontologies.
The first version of this system has now been developed. It will be tested with a group of framers in December 2012. In this talk I will describe the research methodology that was adapted, conceptual model, the process used to develop the system and demonstrate the system that was developed.