Knowledge Representation and Management for Precision Agriculture: A Case Study

Abstract

Precision Agriculture (PA) means the use of information technology for the management of crop growing procedures in such a way that farming methods implementation is accurate, controlled and done on time so that maximum yield can be obtained while reducing the losses, eliminating health hazards and cutting down the input costs. Despite the significance of PA, its practical implementation is yet scarce in Pakistan. The successful implementation of PA depends on gathering, storing, and sharing knowledge being generated at various levels. The knowledge that is needed to be shared includes best practices at farming level, results of various crop monitoring mechanisms, and the latest research findings at research institutes. An efficient knowledge storing and sharing system ultimately results in better crop plans, high yields and cost reduction. Due to slow knowledge sharing processes, stakeholders especially the farmers get delayed information. Also, the process level integration, that is responsible for calculating agricultural indices, crop health monitoring parameters, and parameter estimation techniques require coupling of different Knowledge Management (KM) technologies. Common KM systems lack such capabilities thus result in overall reduced benefits. This paper proposes a KM framework through which knowledge can be readily stored and shared with all the stakeholders through process automation. The system being proposed has three layered architecture with organizational layer at the top, connected to process layer and resources through a conceptual layer. This fully integrated KM framework has been applied to Rice Research Institute (RRI) at KalaShah Kaku, Lahore. Automation of manual processes done at RRI has been achieved through the application of proposed KM framework and is one of the main contributions of this paper. The RRI study shows that real time analysis can be shared promptly with the stakeholders through efficient knowledge management. The proposed KM model is generic and can be customized for any other organization related to agriculture or otherwise.