RTCB: an integrated deep learning model for garlic leaf disease identification

TL;DR

This paper introduces RTCB, a deep learning model that improves garlic leaf disease detection with high accuracy and efficiency.

Contribution

A novel deep learning model (RTCB) combining ResNet18 with triplet and convolutional block attention mechanisms for garlic leaf disease recognition.

Findings

RTCB achieves 98.90% classification accuracy, outperforming models like Efficient-v2-B0 and MobileOne-S0.

The model offers faster computation speed and stronger generalization for plant leaf disease detection.

RTCB is suitable for edge computing and has potential for intelligent agriculture applications.

Abstract

Garlic is a common ingredient that not only enhances the flavor of dishes but also has various beneficial effects and functions for humans. However, its leaf diseases and pests have a serious impact on the growth and yield. Traditional plant leaf disease detection methods have shortcomings, such as high time consumption and low recognition accuracy.

As a result, we present a deep learning approach based on an upgraded ResNet18, triplet, convolutional block (RTCB) attention mechanism for recognizing garlic leaf diseases. First, we replace the convolutional layers in the residual block with partial convolutions based on the classic ResNet18 architecture to improve computational efficiency. Then, we introduce triplet attention after the first convolutional layer to enhance the model’s ability to focus on key features. Finally, we add a convolutional block attention mechanism after each residual layer to improve the model’s feature perception.

The experimental results demonstrate that the proposed model achieves a classification accuracy of 98.90%, which is superior to outstanding deep learning models such as Efficient-v2-B0, MobileOne-S0, OverLoCK-S, EfficientFormer, and MobileMamba. The proposed RTCB has a faster computation speed, higher recognition precision, and stronger generalization ability.

The proposed approach provides a scalable technical reference for the engineering application of automatic disease monitoring and control in intelligent agriculture. The current strategy is conducive to the deployment of edge computing equipment and has extensive significance and application potential in plant leaf disease detection.

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