TY - EJOU AU - Bashir, Komal AU - Rehman, Mariam AU - Bashir, Afnan AU - Kanwal, Faria TI - Designing and Evaluating a Collaborative Knowledge Management Framework for Leaf Disease Detection T2 - Computer Systems Science and Engineering PY - 2022 VL - 42 IS - 2 SN - AB - 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. KW - Collaborative knowledge management; framework; jennex olfman km success model; knowledge management; rice disease detection DO - 10.32604/csse.2022.022247