ECVNet: A Fusion Network of Efficient Convolutional Neural Networks and Visual Transformers for Tomato Leaf Disease Identification DOI Creative Commons

Fendong Zou,

Jing Hua, Yuanhao Zhu

и другие.

Agronomy, Год журнала: 2024, Номер 14(12), С. 2985 - 2985

Опубликована: Дек. 15, 2024

Tomato leaf diseases pose a significant threat to plant growth and productivity, necessitating the accurate identification timely management of these issues. Existing models for tomato disease recognition can primarily be categorized into Convolutional Neural Networks (CNNs) Visual Transformers (VTs). While CNNs excel in local feature extraction, they struggle with global recognition; conversely, VTs are advantageous extraction but less effective at capturing features. This discrepancy hampers performance improvement both model types task identification. Currently, fusion that combine still relatively scarce. We developed an efficient network named ECVNet recognition. Specifically, we first designed Channel Attention Residual module (CAR module) focus on channel features enhance model’s sensitivity importance channels. Next, created Fusion (CAF effectively extract integrate features, thereby improving spatial capabilities. conducted extensive experiments using Plant Village dataset AI Challenger 2018 dataset, achieving state-of-the-art cases. Under condition 100 epochs, achieved accuracy 98.88% 86.04% dataset. The introduction provides solution diseases.

Язык: Английский

Ultra-lightweight tomatoes disease recognition method based on efficient attention mechanism in complex environment DOI Creative Commons
Wenbin Sun,

Zhilong Xu,

Kang Xu

и другие.

Frontiers in Plant Science, Год журнала: 2025, Номер 15

Опубликована: Фев. 13, 2025

A variety of diseased leaves and background noise types are present in images tomatoes captured real-world environments. However, existing tomato leaf disease recognition models limited to recognizing only a single leaf, rendering them unsuitable for practical applications scenarios. Additionally, these consume significant hardware resources, making their implementation challenging agricultural production promotion. To address issues, this study proposes framework that integrates detection with recognition. This includes model designed diverse complex environments, along an ultra-lightweight diseases. minimize resource consumption, we developed five inverted residual modules coupled efficient attention mechanism, resulting effectively balances complexity accuracy. The proposed network was trained on dataset collected from real 14 contrasting experiments were conducted under varying conditions. results indicate the accuracy model, which utilizes is 97.84%, 0.418 million parameters. Compared traditional image models, presented not achieves enhanced across noisy environments but also significantly reduces number required parameters, thereby overcoming limitation can recognize images.

Язык: Английский

Процитировано

1
Privacy-Preserving Neural Network Cloud Service System Based on CKKS Homomorphic Encryption DOI
Yanru Zhang, Rui Zhang,

Zhaochong Wu

и другие.

Lecture notes in electrical engineering, Год журнала: 2025, Номер unknown, С. 585 - 592

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
ECVNet: A Fusion Network of Efficient Convolutional Neural Networks and Visual Transformers for Tomato Leaf Disease Identification DOI Creative Commons

Fendong Zou,

Jing Hua, Yuanhao Zhu

и другие.

Agronomy, Год журнала: 2024, Номер 14(12), С. 2985 - 2985

Опубликована: Дек. 15, 2024

Tomato leaf diseases pose a significant threat to plant growth and productivity, necessitating the accurate identification timely management of these issues. Existing models for tomato disease recognition can primarily be categorized into Convolutional Neural Networks (CNNs) Visual Transformers (VTs). While CNNs excel in local feature extraction, they struggle with global recognition; conversely, VTs are advantageous extraction but less effective at capturing features. This discrepancy hampers performance improvement both model types task identification. Currently, fusion that combine still relatively scarce. We developed an efficient network named ECVNet recognition. Specifically, we first designed Channel Attention Residual module (CAR module) focus on channel features enhance model’s sensitivity importance channels. Next, created Fusion (CAF effectively extract integrate features, thereby improving spatial capabilities. conducted extensive experiments using Plant Village dataset AI Challenger 2018 dataset, achieving state-of-the-art cases. Under condition 100 epochs, achieved accuracy 98.88% 86.04% dataset. The introduction provides solution diseases.

Язык: Английский

Процитировано

1