GrapeLeafNet: A Dual-Track Feature Fusion Network With Inception-ResNet and Shuffle-Transformer for Accurate Grape Leaf Disease Identification

残差神经网络 计算机科学 鉴定(生物学) 变压器 对偶(语法数字) 人工智能 模式识别(心理学) 人工神经网络 工程类 电气工程 艺术 植物 文学类 电压 生物
作者
R. Karthik,R. Menaka,S. Ompirakash,Pragadeesh Murugan,M. Meenakashi,Sindhia Lingaswamy,Daehan Won
出处
期刊:IEEE Access [Institute of Electrical and Electronics Engineers]
卷期号:12: 19612-19624 被引量:4
标识
DOI:10.1109/access.2024.3361044
摘要

Grapes are a widely cultivated crop in the horticultural industry, renowned for their unique flavor and nutritional benefits. However, this crop is highly susceptible to various diseases that can cause significant reductions in yield and quality, resulting in considerable financial losses. Therefore, it is imperative to identify these diseases to effectively manage their spread. Traditionally, the identification of grape leaf diseases has relied on scientific expertise and observational skills. However, with the advent of deep learning methods, it is now feasible to recognize disease patterns from images of infected leaves. In this research, we propose a novel dual-track feature fusion network titled 'GrapeLeafNet' for detecting grape leaf disease. It employs a dual-track feature fusion approach, combining Inception-ResNet blocks with CBAM for local feature extraction and Shuffle-Transformer for global feature extraction. The first track uses Inception-ResNet blocks to represent features at multiple scales and map significant features, and CBAM captures significant spatial and channel dependencies. The second track employs Shuffle-Transformer to extract long-term dependencies and complex global features in images. The extracted features are then fused using Coordinate attention, enabling the network to capture both local and global contextual information. Experimental results on the Grape leaf disease dataset from Plant Village demonstrate the effectiveness of the proposed network, achieving an accuracy of 99.56%.
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