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Diagnosis of Custard Apple Disease Based on Adaptive Information Entropy Data Augmentation and Multiscale Region Aggregation Interactive Visual Transformers

文献类型： 外文期刊

作者： Cui, Kunpeng 1 ; Huang, Jianbo 1 ; Dai, Guowei 2 ; Fan, Jingchao 3 ; Dewi, Christine 4 ;

作者机构： 1.Zhejiang A&F Univ, Ji Yang Coll, Shaoxing 311800, Peoples R China

2.Sichuan Univ, Coll Comp Sci, Chengdu 610065, Peoples R China

3.CAAS, Natl Agr Sci Data Ctr, Agr Informat Inst, Beijing 100081, Peoples R China

4.Deakin Univ, Sch Informat Technol, 221 Burwood Hwy, Burwood, Vic 3125, Australia

5.Satya Wacana Christian Univ, Dept Informat Technol, 52-60 Diponegoro Rd, Salatiga 50711, Indonesia

关键词： plant disease; convolutional neural network; adaptive data augmentation; feature fusion; visual transformer

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 14 卷 11 期

页码：

收录情况： SCI

摘要： Accurate diagnosis of plant diseases is crucial for crop health. This study introduces the EDA-ViT model, a Vision Transformer (ViT)-based approach that integrates adaptive entropy-based data augmentation for diagnosing custard apple (Annona squamosa) diseases. Traditional models like convolutional neural network and ViT face challenges with local feature extraction and large dataset requirements. EDA-ViT overcomes these by using a multi-scale weighted feature aggregation and a feature interaction module, enhancing both local and global feature extraction. The adaptive data augmentation method refines the training process, boosting accuracy and robustness. With a dataset of 8226 images, EDA-ViT achieved a classification accuracy of 96.58%, an F1 score of 96.10%, and a Matthews Correlation Coefficient (MCC) of 92.24%, outperforming other models. The inclusion of the Deformable Multi-head Self-Attention (DMSA) mechanism further enhanced feature capture. Ablation studies revealed that the adaptive augmentation contributed to a 0.56% accuracy improvement and a 0.34% increase in MCC. In summary, EDA-ViT presents an innovative solution for custard apple disease diagnosis, with potential applications in broader agricultural disease detection, ultimately aiding precision agriculture and crop health management.

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