From leaf to harvest: achieving sustainable agriculture through advanced disease prediction with DBN-EKELM.

BACKGROUND: In the agricultural sector, the early identification of plant diseases presents a pressing challenge. Throughout the growing season, plants remain vulnerable to an array of diseases. Failure to detect these diseases at their early stages can significantly compromise the overall yield, thereby reducing profitability for farmers. To address this issue, several researchers have introduced standard methods that leverage machine learning and deep learning techniques. However, many of these methods offer limited classification accuracy and often necessitate extensive training parameter adjustments. METHOD: The objective of this study is to develop a new deep learning-based technique for detecting and classifying plant diseases at earlier stages. Thus, this paper introduces a novel technique known as the deep belief network-based enhanced kernel extreme learning machine (DBN-EKELM) that identifies a disease automatically and performs effective classification. The initial phase involves data preprocessing to enhance quality of plant leaf images, facilitating the extraction of critical information. With the goal of achieving superior classification accuracy, this paper proposes the use of the DBN-EKELM technique for optimal plant leaf disease detection. Given that KELM parameters are highly sensitive to minor variations, proper parameter tuning is essential and introduces a novel binary gaining sharing knowledge-based optimization algorithm (NBGSK). RESULT: The efficacy of the proposed DBN-EKELM method is evaluated by comparing its performance with other conventional methods, considering various measures like accuracy, precision, specificity, sensitivity and F-measure. CONCLUSION: Experimental analyses demonstrate that the DBN-EKELM technique achieves an impressive rate of approximately 98.2%, 97%, 98.1%, 97.4% as well as 97.8%, surpassing other standard methods. © 2024 Society of Chemical Industry.

Writing for publication: Sharing your clinical knowledge and skills.

Clinical nurses are ideally placed to write for publication in addition to those who work in academia who have this as an accepted part of their role. Nurses generate new evidence from their work in practice by carrying out research and audits and being involved in practice development projects, for example. This resource of knowledge needs to be shared with others, ideally in an international arena so that nurses can learn from each other. Nursing in the United Kingdom is now an all graduate profession and many nurses go on to study at both Masters and PhD level, providing writing from all levels of academic study that can be adapted for publication. It seems wrong to undertake a study and obtain findings and then choose not share this widely. Both a lack of confidence and time are cited as reasons why nurses do not write; however, to share knowledge with others is a duty as part of any nursing role for the improvement of staff working practices and patient care. All nurses need knowledge that is practical, experiential, and scientific; clinical nurses who write for publication can provide this.

Strategies for sharing pedagogical knowledge in clinical education in adapting to the impact of COVID-19.

BACKGROUND: The extent and complexity of knowledge in the field of medicine necessitate modern education systems. Rational performance in the education system depends on the synergy of educators through knowledge sharing as the basis of education. The aim of this study was to investigate the knowledge-sharing strategies in clinical education and its changes during the COVID-19 pandemic. MATERIALS AND METHODS: The content analysis was conducted in 2019 at Birjand University of Medical Sciences. Twenty-seven clinical instructors with enough experience and knowledge in the field of clinical education were chosen based on purposive theoretical sampling. Data collection was done by semi-structured interviews, which continued until data saturation. The interviews were recorded, transcribed, and read several times to obtain a whole understanding. Next, the meaning units and initial codes were identified, and then, they were classified into subcategories and categories. To ensure the trustworthiness of the data, Lincoln and Guba criteria were considered. RESULTS: The results of the study include five pedagogical knowledge-sharing strategies: "peer-helping, clinical education workplace," "use of cyberspace," "student mediation," "working teams," and "scientific communities." Coronavirus pandemic was identified as the "facilitator" and the "culturalization factor" of knowledge sharing. In addition, "lack of shared knowledge management," "lack of compliance with needs," and "dispersion of content" were considered as barriers to the efficiency of pedagogical knowledge sharing during the coronavirus pandemic. CONCLUSIONS: Sharing knowledge in a clinical education setting could continue by various strategies. The results can be used in planning for the professional development of professors.