ISNM-Net: A Deep Synergic CNN Architecture for Accurate Grape Leaf Disease Classification Using Mobilenet and Res-Net

Grape leaf diseases cause serious problems for viticulture around the world by having a substantial impact on the output and quality of ‎grapevine cultivation. Conventional manual diagnosis techniques are laborious, subjective, and frequently ineffectual in extensive field set-‎tings. In this study, we present the Inceptive Synergic Network Model (ISNM), a revolutionary deep learning framework for the precise, ‎effective, and scalable categorization of grape leaf diseases. With a Scale-Invariant Feature Learning (SIFL) module to improve spatial in-‎variance and reduce superfluous background noise, ISNM combines the advantages of Mobile-Net and ResNet-50 as dual backbones for ‎reliable multi-scale feature extraction. We also investigate how wavelet-based sub-band decomposition can enhance feature localization in a ‎variety of illumination and disease-spread scenarios. Lightweight convolutional layers optimize the fused deep features, allowing for de-‎ployment on edge devices for real-time monitoring. The suggested ISNM outperforms baseline CNNs and other conventional designs in ‎terms of precision, recall, and computational efficiency, achieving a state-of-the-art accuracy of 98.75% when tested on the Plant Village ‎grape leaf dataset. With potential uses in precision farming, self-sufficient vineyard monitoring, and sustainable crop management, this ‎work provides a scalable, interpretable, and useful approach to early grapevine disease diagnosis‎.