Journal of Scientific Agriculture https://updatepublishing.com/journal/index.php/jsa <p><em>Journal of Scientific Agriculture (JSA)</em>&nbsp;is an international, peer-reviewed, open-access journal, published by the Update Publishing House.</p> Update Publishing House en-US Journal of Scientific Agriculture 2184-0261 Economic and financial viability of wheat production in Cameroon https://updatepublishing.com/journal/index.php/jsa/article/view/9411 <p>This study evaluated the economic and financial profitability of wheat production in Cameroon using data from 300 individuals in Adamawa, North-West, and West regions. Key factors influencing profitability were identified through correlation heatmaps, pair-plot diagrams, and modeling algorithms (Generalized Least Squares, Random Forest, and Least Absolute Shrinkage and Selection Operator). Positive factors included production volume, packaging, and transportation costs, while negative factors included production workforce, experience, and fertilizer costs. The net margin for wheat production was positive at 76,691,000 FCFA, but financial profitability was low, with an import-to-export ratio of 0.16. The study highlights the need to enhance wheat production to reduce importation.</p> Adama Farida Ngonkeu Mangaptche Eddy Leonard Jean Marie Gankou Copyright (c) 2025 Adama Farida, Ngonkeu Mangaptche Eddy Leonard, Jean Marie Gankou http://creativecommons.org/licenses/by-nc-nd/4.0 2025-01-28 2025-01-28 1 15 10.25081/jsa.2025.v9.9411 Leveraging deep learning for plant disease identification: a bibliometric analysis in SCOPUS from 2018 to 2024 https://updatepublishing.com/journal/index.php/jsa/article/view/9412 <p>This work aimed to present a bibliometric analysis of deep learning research for plant disease identification, with a special focus on generative modeling. A thorough analysis of SCOPUS-sourced bibliometric data from 253 documents was performed. Key performance metrics such as accuracy, precision, recall, and F1-score were analyzed for generative modeling. The findings highlighted significant contributions from some authors Too and Arnal Barbedo, whose works had notable citation counts, suggesting their influence on the academic community. Co-authorship networks revealed strong collaborative clusters, while keyword analysis identified emerging research gaps. This study highlights the role of collaboration and citation metrics in shaping research directions and enhancing the impact of scholarly work in applications of deep learning to plant disease identification. Future research should explore the methodologies of highly cited studies to inform best practices and policy-making.</p> Enow Takang Achuo Albert Ngalle Hermine Bille Ngonkeu Mangaptche Eddy Leonard Copyright (c) 2025 Enow Takang Achuo Albert, Ngalle Hermine Bille, Ngonkeu Mangaptche Eddy Leonard http://creativecommons.org/licenses/by-nc-nd/4.0 2025-02-04 2025-02-04 16 39 10.25081/jsa.2025.v9.9412