KDI-Transformer: A method for identifying kiwifruit leaf disease severity in complex environments

Abstract

The accurate identification of the severity of kiwifruit leaf diseases faces significant challenges due to the high morphological similarity between different disease states and interference from complex environmental factors. To address this issue, we propose a Vision Transformer-based severity grading model for kiwifruit leaf diseases, called KDI-Transformer. This model deeply integrates the global modeling capability of Transformer with the local feature extraction advantages of Convolutional Neural Networks (CNNs). It incorporates three innovative modules: the Multi-Scale Perception Module (MSP), which extracts multi-granularity lesion features using parallel multi-scale convolutional kernels and integrates contextual information at different scales; the Adaptive Feature Transmission Module (AFT), which uses dynamic gating weights to adaptively adjust the inter-layer feature transmission ratio, effectively alleviating the feature attenuation problem in deep networks; and the Local-Global Interaction Module (LGI), which employs an attention mechanism for dynamic calibration of local features under global semantic guidance, significantly enhancing the model’s sensitivity to subtle disease differences. Experimental results demonstrate that KDI-Transformer achieves an accuracy of 89.57 %, significantly outperforming various baseline models, and provides a new solution for precise crop management in smart agriculture.

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