Open Access

ARTICLE

Designing and Evaluating a Collaborative Knowledge Management Framework for Leaf Disease Detection

by Komal Bashir^1,*, Mariam Rehman², Afnan Bashir³, Faria Kanwal¹

1 Department of Computer Science, Lahore College for Women University, Lahore, 54000, Pakistan
2 Department of Information Technology, Government College, Faisalabad, 54000, Pakistan
3 Engage Research Lab, School of Law and Society, University of the Sunshine Coast, Sippy Downs, QLD 4556, Australia

* Corresponding Author: Komal Bashir. Email:

Computer Systems Science and Engineering 2022, 42(2), 751-777. https://doi.org/10.32604/csse.2022.022247

Received 01 August 2021; Accepted 06 September 2021; Issue published 04 January 2022

Abstract

Knowledge Management (KM) has become a dynamic concept for inquiry in research. The management of knowledge from multiple sources requires a systematic approach that can facilitate capturing all important aspects related to a particular discipline, several KM frameworks have been designed to serve this purpose. This research aims to propose a Collaborative Knowledge Management (CKM) Framework that bridges gaps and overcomes weaknesses in existing frameworks. The paper also validates the framework by evaluating its effectiveness for the agriculture sector of Pakistan. A software LCWU aKMS was developed which serves as a practical implementation of the concepts behind the proposed CKMF framework. LCWU aKMS served as an effective system for rice leaf disease detection and identification. It aimed to enhance CKM through knowledge sharing, lessons learned, feedback on problem resolutions, help from co-workers, collaboration, and helping communities. Data were collected from 300 rice crop farmers by questionnaires based on hypotheses. Jennex Olfman model was used to estimate the effectiveness of CKMF. Various tests were performed including frequency measures of variables, Cronbach’s alpha reliability, and Pearson’s correlation. The research provided a KMS depicting KM and collaborative features. The disease detection module was evaluated using the precision and recall method and found to be 94.16% accurate. The system could replace the work of extension agents, making it a cost and time-effective initiative for farmer betterment.

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Keywords

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