Optimized Grape Leaf Disease Classification using Hybrid Machine Learning Approach with SSA-SMA

Abstract

Introduction: The agricultural crop grapes shows high susceptibility to multiple diseases, which result in negative impacts on both quantity and quality production. A wide range of existing methods exists for grape leaf disease detection but precise identification of diseases proves challenging. The research target improves disease detection accuracy through innovative machine learning approaches.

Objectives: The main aim of this investigation targets improving the accuracy levels for diagnosing grape leaf diseases. The analysis takes place through an examination of different grape leaf diseases while reviewing past research to determine present-day methodological shortcomings. An evaluation of existing datasets and tools takes place to establish their suitability for disease identification methods. The synthesis of obtained research results will yield a thorough literature review while a research paper analyzes gaps within the existing methods. These analyses will help develop feature extraction methods by utilizing various image processing techniques.

Methods: The proposed research utilizes a combined algorithm framework that unites Sparrow Search Algorithm (SSA) and Slime Mould Algorithm (SMA) to enhance disease recognition outcomes. The research begins with a thorough review of previously used methods along with their related difficulties. The evaluation includes reviewing different datasets together with image-based disease identification preprocessing methods. A comprehensive evaluation of extraction algorithms determines ways for the model to accurately identify distinct disease types. The development of a robust model occurs through implementation of the SSA-SMA hybrid technique for optimizing classification performance.

Results: The analysis combined with model creation generates an accurate detection system for grape leaf diseases. The hybrid machine learning model will exceed current methods through improved feature selection processes and classification techniques. The study traces weaknesses and prospective advancement opportunities by providing important findings through its research manuscript about a gap analysis.

Conclusions: Sustainable agricultural practices receive support through this research which strengthens grape leaf disease detection accuracy. The combination of advanced algorithms will create a powerful detection system which boosts yield quality. Research findings will create fundamental knowledge for precision agriculture developments that benefit grape growers alongside the agriculture sector.