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

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.

Analysis The Success of Knowledge Management in Agile Team Study Case: Ecommerce in Indonesia

“…According to JOMKSM, UserSat is positively associated with IU and NB. These findings are also supported by previous research by Bashir et al [17] and Al-Hattami & Kabra [16]. Research done by Wang and Yang [15] also supports the finding about UserSat is positively associated with NB.…”

“…This finding is also supported by research done by Al-Hattami & Kabra [16]. Aside from NB, research done by Bashir et al [17], Halawi et al [18], and Mohammed [13] found that the IU dimension also positively affects UserSat dimension.…”

“…Based on another previous research done by Al Kabra [16], SysQual directly influences intents to use (IU), UserSat, and net benefits (NB). Research by Bashir et al [17], Halawi et al [18] and Wang and Yang [15] supported the hypothesis that SysQual directly influences IU and NB. The next variable is Knowledge quality (KnowQual) in KMS ensures the proper knowledge is captured and available for the right users at the right time and constructed by three factors, three factors, KM strategy/process, knowledge richness, and linkages between knowledge [25].…”

“…User KM support itself is focused on support by the organization and IS KM support is support by the IS provider to the KM user. On the SvcQual dimension, previous research [13] [15] [17] found that SvcQual positively affects IU and UserSat dimension.…”