Research Article
An architectural framework of a Decision Support System (DSS) to increase the returns of small scale farmers in Kanchipuram District, India
@ARTICLE{10.4108/eai.13-7-2018.163978, author={K. Sornalakshmi and S. Sindh and G. Sujatha and D. Hemavathi}, title={An architectural framework of a Decision Support System (DSS) to increase the returns of small scale farmers in Kanchipuram District, India}, journal={EAI Endorsed Transactions on Energy Web}, volume={7}, number={29}, publisher={EAI}, journal_a={EW}, year={2020}, month={4}, keywords={Supply Chain Management, smart agriculture, agri-marketing, Green Massive MIMO Techniques, Green Communication Protocols}, doi={10.4108/eai.13-7-2018.163978} }- K. Sornalakshmi
S. Sindh
G. Sujatha
D. Hemavathi
Year: 2020
An architectural framework of a Decision Support System (DSS) to increase the returns of small scale farmers in Kanchipuram District, India
EW
EAI
DOI: 10.4108/eai.13-7-2018.163978
Abstract
In India, 80% of farmers are small scale and marginal farmers. The sub section of these farmers who cultivate vegetables do not get considerable returns on their investment because their produce has to pass through various layers of agents or distributors before reaching the end users. This process could be regulated to minimize round trip transfer of vegetables for the sake of increased farmer returns. Also, many farmers cultivate the same crop resulting in over production which in turn affects the returns of all farmers cultivating that crop. There are many government schemes like FPOs (Farmer Producer Organizations), PPC (Primary Processing Centers), SFAC (Small Farmers Agri Consortium) to cater this issue. This paper surveys all the existing schemes and mobile platforms. We also propose a framework for a holistic mobile app based decision support system for small scale farmers. This proposed application helps the farmers to increase the returns without middleman and promote community farming.
Copyright © 2020 K.Sornalakshmi et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
