supervision, A.C. and Y.J.J.; funding acquisition, A.C. and Y.J.J. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement This dataset was collected from Kaggle, this can be found at https://www.kaggle.com/datasets/cookiefinder/tomato-disease-multiple-sources (accessed on 2 January 2023). This dataset was collected from Mendeley, this can be found at https://data.mendeley.com/datasets/ngdgg79rzb/1 (accessed on 17 March 2023). Conflicts of Interest The authors declare no conflict of interest. Funding Statement This research was funded in part by the
Open resource ↗Kaggle · cookiefinder/tomato-disease-multiple-sources · lines:335-374Unverified paper record
Aggregating Different Scales of Attention on Feature Variants for Tomato Leaf Disease Diagnosis from Image Data: A Transformer Driven Study.
Sensors (Basel, Switzerland) · 5 Apr 2023 · 10.3390/s23073751
Abstract
Tomato leaf diseases can incur significant financial damage by having adverse impacts on crops and, consequently, they are a major concern for tomato growers all over the world. The diseases may come in a variety of forms, caused by environmental stress and various pathogens. An automated approach to detect leaf disease from images would assist farmers to take effective control measures quickly and affordably. Therefore, the proposed study aims to analyze the effects of transformer-based approaches that aggregate different scales of attention on variants of features for the classification of tomato leaf diseases from image data. Four state-of-the-art transformer-based models, namely, External Attention Transformer (EANet), Multi-Axis Vision Transformer (MaxViT), Compact Convolutional Transformers (CCT), and Pyramid Vision Transformer (PVT), are trained and tested on a multiclass tomato disease dataset. The result analysis showcases that MaxViT comfortably outperforms the other three transformer models with 97% overall accuracy, as opposed to the 89% accuracy achieved by EANet, 91% by CCT, and 93% by PVT. MaxViT also achieves a smoother learning curve compared to the other transformers. Afterwards, we further verified the legitimacy of the results on another relatively smaller dataset. Overall, the exhaustive empirical analysis presented in the paper proves that the MaxViT architecture is the most effective transformer model to classify tomato leaf disease, providing the availability of powerful hardware to incorporate the model.
Plant phenotyping relevance
トマト葉画像から病害状態を分類する画像解析手法を複数のTransformerで比較・検証しており、植物の病害表現型の取得が研究の中心である。
abstractanalyze the effects of transformer-based approaches that aggregate different scales of attention on variants of features for the classification of tomato leaf diseases from image data
abstractFour state-of-the-art transformer-based models, namely, External Attention Transformer (EANet), Multi-Axis Vision Transformer (MaxViT), Compact Convolutional Transformers (CCT), and Pyramid Vision Transformer (PVT), are trained and tested on a multiclass tomato disease dataset.
Code and data availability
The paper used two public plant-image datasets for tomato leaf disease classification, both explicitly linked in the Data Availability Statement: a Kaggle tomato disease dataset (20,000 images, 11 classes) and a Mendeley/PlantVillage-derived dataset (4,972 images, 6 classes). No author analysis code or trained model is
d Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement This dataset was collected from Kaggle, this can be found at https://www.kaggle.com/datasets/cookiefinder/tomato-disease-multiple-sources (accessed on 2 January 2023). This dataset was collected from Mendeley, this can be found at https://data.mendeley.com/datasets/ngdgg79rzb/1 (accessed on 17 March 2023). Conflicts of Interest The authors declare no conflict of interest. Funding Statement This research was funded in part by the National Research Foundation of Korea (grant no. NRF-2020R1A2C1008753). Footnotes Disclaimer/Publisher’s Note: The statements, opinions and data contained in all public
Open resource ↗Mendeley · ngdgg79rzb · lines:335-374This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.