Journal: Procedia Computer Science

Article Title: Grape Leaf Disease Diagnosis System Using Fused Deep Learning Features Based System

doi: 10.1016/j.procs.2024.04.037

Figure Lengend Snippet: Fig. 2. The methods recommended processing flow Third, combine the deep characteristics that were retrieved using either concatenation of canonical correlation analyses, CCA sum, or straight concatenation. The deep-fused characteristics should then be fed into a fine-trained SVM classifier, which may subsequently output the various diseases present in the input grape leaves. A multi-class SVM classifier may be trained using the “appropriate class output codes for error correction. “MATLAB 2020b's (fitcecoc) function is used using its built-in parameterization. The "fitcecoc" function uses a model of binary SVM with a one-to-one encoding architecture to boost the classifier's categorization effectiveness. Table 2 displays some of the default settings used throughout the training phase. Table 2 Several common training-stage parameters. Component Rate

Article Snippet: A MATLAB 2020b software environment is used, which, by installing the Deep Learning toolbox, may support several traditional CNN models, including AlexNet, GooLeNet, and ResNet.

Techniques: