Tenacious Fish Swarm Optimization Based Hidden Markov Model (TFSO-HMM) for Augmented Accurate Cotton Leaf Disease Identification and Yield Prediction

Abstract

Abstract This research presents an innovative approach called Tenacious Fish Swarm Optimization based Hidden Markov Model (TFSO-HMM) for augmented accurate cotton leaf disease identification and yield prediction. Cotton leaf diseases significantly threaten crop productivity, requiring timely detection and precise prediction for effective disease management. The proposed TFSO-HMM framework combines the strengths of Tenacious Fish Swarm Optimization (TFSO) and the Hidden Markov Model (HMM) to address the challenges associated with disease identification and yield prediction in cotton plants. TFSO, a nature-inspired optimization algorithm, optimizes the classification process, enhancing the accuracy of disease identification. By harnessing the collective intelligence of fish swarms, TFSO intelligently explores the search space to identify the optimal solution. The selected information is then incorporated into the HMM framework, which captures the temporal dependencies in disease progression and yield prediction. HMM's sequential modelling approach facilitates understanding the dynamic behaviour of cotton leaf diseases over time, leading to more accurate predictions. Experimental results on a comprehensive dataset demonstrate the superior performance of the TFSO-HMM method over existing approaches in terms of accuracy and predictive capability. The augmented accuracy achieved through TFSO-HMM enables early detection and precise prediction of cotton leaf diseases, enabling timely interventions for disease management and maximizing crop yield.