{"id":5286,"date":"2026-08-17T04:44:38","date_gmt":"2026-08-17T04:44:38","guid":{"rendered":"https:\/\/cognitionconferences.com\/plantscience\/?post_type=speaker&p=5286"},"modified":"2026-08-17T04:44:44","modified_gmt":"2026-08-17T04:44:44","slug":"dr-muhammad-farhan","status":"publish","type":"speaker","link":"https:\/\/cognitionconferences.com\/plantscience\/speaker\/dr-muhammad-farhan\/","title":{"rendered":"Dr Muhammad Farhan"},"content":{"rendered":"

Abstract<\/strong>
\nCoffee plants are susceptible to a variety of diseases that threaten the quantity and quality of coffee
\nproduction. Coffee Berry Disease (CBD) poses a significant threat to global coffee production,
\nparticularly in Africa, and creates a potential risk of expansion into coffee-growing regions in Latin
\nAmerica and Asia. This study explores a novel hybrid computational modeling using fractional
\ncalculus and deep learning techniques to model the complex dynamics of CBD. The Caputo
\noperator-based Euler scheme is used to solve the model numerically and to generate synthetic
\ndatasets by considering various values of fractional order. Further, the dynamics of disease
\ntransmission are numerically studied using a deep neural network by comparing solutions across all
\ncompartments through convergence testing, regression metrics and error distribution. To minimize
\nthe mean squared error, we employ a fractional Euler iterative scheme, allocating 70 % of the data
\nfor training and 15% each for validation and testing. Numerical performance is demonstrated under
\nfive fractional-order scenarios (\u03b1 = 0.80,0.85,0.90,0.95,1) using distinct initial conditions. The
\nresults obtain from present approach align precisely with the benchmark data. The best validation
\nperformance is archived to 10\u221210, and with minimum absolute error 10\u22128. The outcomes of the
\nscheme are rigorously presented numerically and tabulated. We believe that the integration of a
\ndeep neural network that implements Tanh and ReLU activations in its hidden layers to study CBD
\nis a novel attempt in the modeling of infectious diseases. This hybrid neuro-computing paradigm
\ndelivers both improved accuracy and computational efficiency, providing a better understanding of
\nCBD and thereby helping to set effective control interventions.
\nKeywords: Coffee berry disease modeling; Fractional calculus; Deep learning approach; Error
\nestimation; Memory-based simulation.<\/p>\n","protected":false},"featured_media":5287,"template":"","meta":{"_acf_changed":false},"schedule":[],"speaker-category":[],"class_list":["post-5286","speaker","type-speaker","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"\nMuhammad Farhan |Yangzhou University |China<\/title>\n<meta name=\"description\" content=\"I completed my PhD at the age of 32 at Abdul Wali Khan University and conducted postdoctoral studies at Yangzhou University, China. 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