Intelligent framework for automated rice leaf disease diagnosis using a Simplicial Finite-Element-Informed Neural Network optimised by the MCA

Abstract

Rice, a staple food for more than half of the global population, faces significant yield and quality losses due to various diseases and environmental stresses. This paper presents a new Simplicial Finite-Element-Informed Neural Network based on the Musical Chairs Optimisation Algorithm (SFEINN-MCOA) to detect rice leaf disease precisely and efficiently. The first step involves the acquisition of the RGB image from the Rice Leaf Disease dataset. The Iterative Robust Peak-Aware Guided Filter (IRPAGF) is used to remove noise and enhance contrast, thus improving the image. The Graph-Based Soft-Balanced Fuzzy Clustering (GSBFC) method is used to separate diseased regions and then analyse them. The Self-Distillated Masked Autoencoder (SMA) is used to perform feature extraction and capture important attributes of leaves. The SFEINN classifies data under the category of healthy and diseased leaves, and the MCOA optimises the model parameters to achieve maximum accuracy and minimum error. Experimental findings reveal that the SFEINN-MCOA model has an accuracy of 99.9% and an F1-score of 98.9%, which is better and stronger. This smart system offers a secure, automatic, and effective system for early disease identification of rice and helps farmers to enhance crop health and yield sustainability.

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