
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12123-8
10.1016/j.heliyon.2024.e36092
e36092
Research Article
Improved FasterViT model for citrus disease diagnosis
Chen Jiyang
Wang Shuai wangs@dgut.edu.cn
⁎
Guo Jianwen
Chen Fengyi
Li Yuchen
Qiu Honghua
School of Mechanical Engineering, Dongguan University of Technology, Dongguan, 523808, China
⁎ Corresponding author. wangs@dgut.edu.cn
10 8 2024
30 8 2024
10 8 2024
10 16 e360921 12 2023
4 8 2024
9 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Despite advances in deep learning for plant leaf disease recognition, accurately distinguishing morphological features under varying environmental conditions continues to pose significant challenges. Traditional deep learning models often fail to effectively merge local and global information, especially in small-scale datasets, impairing performance and elevating training costs. Focusing on citrus diseases, we propose an improved FasterViT Model, an advanced hybrid CNN-ViT framework that builds upon the FasterViT model. The proposed model seamlessly integrates CNN's rapid local learning capabilities with ViT's global information processing strength, thereby effectively extracting complex textures and morphological features from images. Cross-stage alternating Mixup and Cutout methods are strategically employed to enhance model robustness and generalization capabilities, particularly valuable for fast learning on small-scale datasets by simulating a more diverse training environment. Triplet Attention and AdaptiveAvgPool mechanisms are utilized to reduce training costs and optimize training performance. The proposed model is tested on both our specially constructed small-scale citrus disease dataset called in-field small dataset and the comprehensive PlantVillage dataset. The experimental results demonstrated that the model exhibits the capability of fast learning and adaptation to small sample training in plant disease detection tasks, and demonstrates the effectiveness of our improvement approach in improving model accuracy and reducing training costs. Additionally, its exemplary performance in transfer learning scenarios underscores its adaptability and broad applicability. This study not only highlights the efficacy of the improved FasterViT model in addressing the complexities of plant disease image recognition but also pioneers a new paradigm for developing efficient, scalable, and robust classification systems.

Keywords

Citrus
Disease diagnosis
Faster vision Transformer
CNN-ViT
Transfer learning
==== Body
pmc1 Introduction

Plant leaf disease image recognition is a technology that utilizes computer vision to detect and identify diseases in plant leaves [1]. In recent years, deep learning techniques have significantly improved the accuracy of image recognition tasks and provided promising solutions for various domains [2,3,4]. Currently, deep learning has been applied to leaf disease image recognition in crops such as maize, wheat, citrus, and potatoes, targeting a range of diseases including fungal, bacterial, and viral infections [5].

The Attention Mechanism [6] is a mechanism used in models for information selection and weighting. It allows the model to focus on relevant information for the current task by assigning weights to different parts of the input, thereby improving the performance and effectiveness of the model. Transformer [7] is a deep neural network based on the Attention Mechanism. Compared to other network types such as convolutional and recurrent networks, Transformer-based models demonstrate better performance on various visual benchmarks. Vision Transformer (ViT) [8], based on the Transformer architecture, directly applies the pure Transformer model to small patches of image sequences and achieves state-of-the-art performance on multiple image recognition benchmarks. FasterViT [9], a structure based on CNN and Vision Transformer, introduces self-attention mechanism and hierarchical encoding to better capture contextual information and feature relationships in images, while maintaining high accuracy and faster inference speed. However, when applying FasterViT to solve plant pest classification tasks, it has limitations:(1) Plant datasets often contain occlusions and losses, requiring additional attention to the local features of leaves. FasterViT still lacks effective channels for capturing the correlations between local features in the feature extraction network. For instance, in the presence of leaf occlusions or partial damage, FasterViT's existing architecture may struggle to capture the full context of the leaf's condition, necessitating enhancements for more accurate feature extraction and disease classification.

(2) Although FasterViT has improved inference speed compared to ViT, it requires significant computational resources and high training costs, while practical applications demand sufficient inference speed and efficiency.

This study specifically targets the enhancement of the FasterViT model to address the dual challenges of maintaining high accuracy in disease detection while significantly reducing inference times and computational demands, crucial for practical agricultural applications. Building upon the advantages of the FasterViT model, the proposed approach focuses on citrus as the research subject and introduces an improved FasterViT model. The improved FasterViT model is a hybrid architecture combining CNN and ViT, leveraging the strengths of each. The incorporation of Triplet Attention module [10] enhances the model's ability to focus on relevant features within occluded or partially visible leaves, while the strategic application of Mixup [11] and Cutout [12] methods during training increases the model's robustness to variations and imperfections in input images. Our findings reveal that the improved FasterViT model not only surpasses existing benchmarks in accuracy but also demonstrates remarkable efficiency in resource utilization, indicating its potential to significantly advance real-world agricultural disease management practices. Our exploration opens new avenues for further refinement of deep learning models in agriculture, promising new strategies for disease prediction and management.

The subsequent organization of this paper includes: the second section introduces related research; the third section elaborates on the Method; the fourth section presents the experiments and discussions; and the final section provides conclusions and future work.

2 Related work

2.1 Research on disease based on image recognition

In recent years, numerous research works have been conducted by both domestic and international scholars in the field of intelligent diagnosis of plant diseases based on image recognition. To illustrate the breadth of research in this field, several key studies are highlighted below, showcasing a range of methodologies from traditional machine learning to advanced deep learning approaches.

Shuo Zhang et al. [13] leveraged K-means clustering, a method that partitions images into clusters based on texture features, to enhance the specificity of feature extraction. This approach improved the accuracy of disease classification by more precisely isolating areas of interest within plant images. David F. Nettleton et al. [14] compared four models for predicting rice blast disease, including two process-based models (Yoshino and WARM) and two machine learning-based methods (M5Rules and RNN), providing the first comparison between process-based and machine learning modeling approaches for supporting plant disease identification. Suping Ma et al. [15] extracted texture features from the CIELAB color space and color features from the RGB and HSV spaces, and employed Principal Component Analysis (PCA) and K-Nearest Neighbors (KNN) for classification, significantly contributing to the model's high accuracy. Hossain et al. [16] segmented leaf lesions using K-means and fed the extracted color and texture features into KNN for classification, achieving an accuracy of 96.76 %. While the initial studies primarily utilized traditional machine learning techniques for feature extraction and classification, Shashwat Jha et al. [17] represents a notable pivot by integrating the Vision Transformer, a deep learning model, for enhanced feature extraction, alongside traditional SVM classification. They compared several disease classification algorithms with the proposed model and found that the accuracy reached 99.69 % with the combination of feature extraction based on Vision Transformer and additional green chromatic coordinates features with SVM classification (GCCViT-SVM), while the accuracy reached 97.41 % when integrated with IoT devices after information quantization. The progression from SVM classification achieving accuracies up to 92.6 % and 96.76 % in earlier studies to the groundbreaking 99.69 % accuracy achieved by the GCCViT-SVM model illustrates the substantial advancements made in plant disease image recognition accuracy through methodological innovation. The integration of GCCViT-SVM with IoT devices, as investigated by Shashwat Jha et al. marks an innovative step towards real-world applications, enabling not just high-accuracy disease classification but also facilitating the practical deployment of these models in agricultural monitoring systems."

Deep learning models, by automatically learning hierarchical feature representations from data, significantly reduce the dependency on expert-derived features, enabling more robust and scalable solutions for plant disease diagnosis. As a result, intelligent diagnostic models based on deep learning have been continuously emerging. Liang Meng et al. [18] developed a lightweight disease recognition model based on deep convolution, achieving a recognition accuracy of 98.32 % for tomatoes, peppers, and potatoes. Son N. Tran et al. [19] proposed the Generalized Stacked Multi-Output CNN (GSMo-CNN) and achieved state-of-the-art performance by comparing it with AlexNet, VGG16, ResNet101, EfficientNet, MobileNet, and GSMo-CNN on benchmark datasets such as Plant Village, Plant Leaves, and PlantDoc. Babak Azad et al. [20] introduced a novel contextual bridging method that integrates the local representation capability of U-Net into ViT, creating the Efficient Transformer model, which demonstrated effectiveness in 12,000 wheat images inoculated with Fusarium Head Blight (FHB). The novel contextual bridging method significantly enhances the integration of local and global image features, leading to more accurate disease identification, particularly in complex visual scenes. Wenjing Zhang et al. [21] preprocessed images using a weighted average fusion of the MSRCR and Gamma algorithms, followed by training an InceptionV3 model for the recognition of five diseases. Compared to models trained without preprocessing, the recognition rate improved by 29.71 %.

Transfer learning and few-shot learning are particularly effective in overcoming the scarcity of labeled data, enabling models to adapt swiftly to new disease types with minimal additional data requirements. In the field of few-shot learning, Shuo Liu et al. [22] proposed a Contrastive Network for Few-shot Learning (CNFS) to classify grape leaf diseases using a small sample dataset consisting of healthy grape leaves and three types of diseased grape leaves. Experimental comparisons with VGG, ResNet, and the proposed CNFS model achieved a recognition rate of 96 %. Li Jiao et al. [23] presented a small-sample learning approach for apple tree disease classification, which utilized feature vector distances and sample point density to identify outliers in the dataset. The proposed method achieved classification accuracies of 98.01 % for apple leaf rust, 97.32 % for black rot, 96.30 % for mixed diseases, and 98.85 % for healthy leaves. In the field of transfer learning, Yantong Zhang et al. [24] employed transfer learning to retrain and fine-tune the MobileNetV2 model on a dataset of corn disease images, achieving a testing accuracy of 96.83 %. Huan Cao et al. [25] compared four lightweight deep learning models of different scales using transfer learning training strategies for mango disease classification. The MobileViT model yielded the best results, with precision of 96.31 %, recall of 96.12 %, and F1 score of 96.20 %, outperforming other models. Sahil Faizal et al. [26] utilized transfer learning with fine-tuning techniques to maximize the performance of ResNet101 for classifying 50 tree species based on bark textures in the BarkVN-50 dataset.

2.2 ViT related research

The InceptionNeXt model proposed by Weihao Yu et al. [27] decomposes deep convolutions with large kernels into parallel branches, achieving high throughput. The MobileViG model, a hybrid CNN-GNN model introduced by Mustafa Munir et al. [28], outperforms existing ViG models, mobile CNNs, and ViT architectures in terms of accuracy and speed for visual tasks on mobile devices, including image classification, object detection, and instance segmentation. Jiangning Zhang et al. [29] propose a simple and efficient modern inverted residual mobile module for mobile applications, surpassing state-of-the-art (SOTA) models based on CNNs/Transformers with similar computational power. Yunjie Tian et al. [30] introduce a novel pre-training model architecture, called iTPN, which consists of multiple pyramid-shaped Transformer layers. They design a new layer type called the Pyramid layer, which aims to learn multi-granularity representations of the input. This architecture achieves high-precision ApmIoU in COCO object detection when used with Mask RCNN.

Many new models have introduced additional attention mechanisms to capture more contextual information. Bahdanau et al. [31] proposed the visual attention mechanism, which has become an important component in sequence-to-sequence models. Luong et al. [32] introduced the global attention mechanism, enabling models to better capture the correlations between sequences and targets. Hu et al. [33] proposed adaptive re-calibration of channel-wise feature responses by explicitly modeling the interdependencies among channels. Woo et al. [34] proposed a lightweight and versatile attention module for feed-forward convolutional neural networks. Chen et al. [35] introduced Coordinate Attention, which aims to improve the performance of models in computer vision tasks by attending to different coordinate positions of the input. Zhang et al. [36] proposed ECA, which enhances the model's ability to capture correlations across different channels by introducing a channel attention module. Lingxiao Yang et al. [37] introduced SimAM, which incorporates a similarity calculation module to capture the relevance of features by computing the similarity of input features.

Ben-David et al. [38] introduced the concept of domain adaptation in 2007 and proposed using the maximum mean discrepancy method to measure the differences between different domains. BERT, proposed by Jacob Devlin et al. [39], has become an important pre-training model in the field of natural language processing and is widely used as a transfer learning method. Yosinski et al. [40] introduced the concept of transfer learning for deep neural networks and achieved promising results through fine-tuning on pre-trained networks. Ganin et al. [41] proposed domain-adversarial training, which minimizes the domain discrepancy between the source domain and target domain to facilitate transfer learning.

Anish Mail et al. [42] proposed an end-to-end workflow called AMaizeD for automated detection of maize diseases. This workflow utilizes multi-spectral images captured from unmanned aerial vehicles (UAVs) for early detection of diseases in maize crops. Zj et al. [43] improved the VGG16 model using the concept of multi-task learning for the identification of three types of rice leaf diseases and two types of wheat leaf diseases. They employed transfer learning by utilizing pre-trained models on ImageNet. Nisar Ahmed et al. [44] presented a deep neural network-based solution for leaf disease recognition, named AgroPath. They incorporated image quality assessment to select images with the desired quality for recognition. The AgroPath model achieved a 99.42 % accuracy in leaf disease recognition. Tasnim Ahmed et al. [45] proposed a transfer learning-based technique for apple leaf disease recognition. Their system employed the pre-trained EfficientNetV2S architecture for feature extraction, which was then passed to a classifier block for efficient prediction. They addressed the issue of class imbalance by utilizing runtime data augmentation.

However, there are some challenges in terms of the training cost and effectiveness of current visual transformer models. The ViT model lacks the ability to model local information, requiring a large amount of training data to learn robust feature representations, which leads to mediocre performance on small-scale datasets. Even with improved generalization on small datasets, overfitting remains a challenge due to the complexity of the model. Therefore, this paper proposes a CNN-ViT hybrid architecture to address the issues of fast learning and prevention of overfitting in small-scale scenarios, leveraging the strengths of both CNN and ViT models.

3 Method

3.1 Vision Transformer

The ViT network architecture [8], as shown in Fig. 1, consists of three main components: Linear Projection of Flattened Patches (image embedding layer), Transformer Encoder, and MLP head. The input image is initially divided into fixed-sized patches, which are then flattened and transformed into an embedding vector. This step transforms the 2D image patches into a format suitable for the Transformer, allowing it to process images similarly to how it processes sequences of text data. This process is referred to as the Linear Projection of Flattened Patches, resulting in embedded patches. This adaptation enables the Transformer to apply its powerful sequence modeling capabilities to the spatial domain of images. To preserve positional information within the sequence, positional embeddings are added to the embedded patches. Positional embeddings are crucial because, unlike in images processed by CNNs, the Transformer needs explicit information about the position of each patch to maintain the spatial relationship between them, ensuring the model understands the layout of the image.Fig. 1 The architecture of the ViT model used for plant disease recognition.

Fig. 1

These embedded patches are then fed into the Transformer Encoder. The output of the Transformer Encoder is obtained through a multi-layer perceptron (MLP) head, which serves as the classification layer. The MLP classification layer is implemented using regular fully connected layers.

The Transformer Encoder is composed of multiple Encoder Blocks, which consist of the following main components:(1) Multi-Head Attention [7]: Multi-Head Attention allows the model to ‘look’ at different parts of an image from multiple ‘perspectives,’ enhancing its ability to detect subtle features indicative of disease. It calculates the relevance between queries and keys and weights the values based on these relevance. The attention function is defined as:

(1) Attention(Q,K,V)=softmax(QKTdk)V

Here, Q, K, and V represent the queries, keys, and values respectively. dk denotes the dimension of the keys and is used for scaling the similarity. The softmax function converts the similarity values into attention weights.(2) Feed-Forward Neural Network (FFN) [7]: The FFN within each Encoder Block acts as a powerful calculator that further processes each patch's information, applying complex transformations to extract and refine features relevant for classification tasks. Each Encoder Block typically consists of two linear transformations and a ReLU activation function [46]. It performs a non-linear transformation on the embedded vector. The FFN function is defined as:

(2) FFN(x)=max(0,xW1+b1)W2+b2

Here, x represents the input sequence, W1 and W2 are the linear transformations, and b1 and b2 are bias vectors used to adjust the linear transformations.(3) Residual Connections and Layer Normalization: Residual connections help preserve information from earlier layers by adding it back to the output of later layers, preventing the loss of important details. Layer normalization (LayerNorm) ensures that the model's training remains stable and efficient by normalizing the data within each layer. It can be represented as:

(3) Output=LayerNorm(Input+Sublayer(Input))

In the Transformer, the Sublayer can be either Multi-Head Attention or FFN, which perform different transformations on the input. In the residual connection, the input and the output of the Sublayer are added together, allowing gradients to flow more easily through the network. LayerNorm normalizes the features along the feature dimension for each sample, ensuring zero mean and unit variance for each feature. The ViT's sophisticated architecture, combining attention mechanisms with neural network processing, offers a promising avenue for advancing the accuracy and efficiency of plant disease detection models.

3.2 FasterViT

The network architecture of FasterViT [9] is depicted in Fig. 2. Compared to the ViT model, FasterViT introduces several key improvements, including:(1) Adoption of a multi-stage structure with Downsample: This allows FasterViT to process images at varying resolutions, enhancing its ability to detect features across different scales. This is particularly beneficial for recognizing plant diseases, where symptoms may vary in size and appearance.

(2) Hybrid Attention Transformer (HAT): This blends the model's focus on both detailed and broader image features, enabling more nuanced feature capture with less computational demand compared to traditional attention mechanisms.

(3) Usage of Conv Block in the Original FasterViT layer: Conv Blocks within FasterViT layers specialize in identifying basic shapes and textures in the early stages of processing, which are crucial for accurately diagnosing plant diseases from images.

Fig. 2 Illustrates the architecture of FasterViT. In the diagram, H, W, and C represent the height, width, and number of channels of the input feature map, respectively.

Fig. 2

The FasterViT network consists of five stages. The Original FasterViT layers in stages 1, 2, and 3 are composed of Conv Blocks with small convolution kernels [47,48] and residual convolution blocks with Downsample [49]. Compared to the Transformer Encoder blocks in ViT, the Conv Blocks in FasterViT improve efficiency by reducing computational costs.

The Conv Block, as defined in Ref. [9], is expressed by the following equations:(4) xˆ=GELU(BN(Conv3x3(x)))

(5) x=BN(Conv3x3(xˆ)+x)

Here, BN [50] represents batch normalization, and GELU [51] is an activation function. Downsample is achieved by applying two-dimensional layer normalization and a 3 × 3 convolution to reduce the spatial resolution of the feature map.

In stages 4 and 5, HAT is utilized for spatial reasoning across the entire feature map. HAT is a novel window-based attention mechanism [9], as depicted in Fig. 3.Fig. 3 Illustrates the achievement of global learning by incorporating Hierarchical Attention.

Fig. 3

Traditional feature maps are divided into local windows, where pixels within each window only attend to other pixels within the same window through local tokens. This constraint limits the scope of attention mechanisms. Therefore, FasterViT incorporates both local window attention and Hierarchical Attention to enable global information propagation. Each window is associated with a unique Carrier Token, and information is propagated across windows using Carrier Tokens (CTs). This allows for global learning and facilitates the dissemination of information throughout the entire feature map.

The entire operation of the HAT block [2] is as follows:

The input feature map x∈ RH×W×d is windowed to obtain X1, where H, W, and d represent the height, width, and depth of the feature map. The feature map is initially divided into n = H2k2 windows of size k × k:(6) xˆ1=Splitk*k(x)

Next, initialize Carrier Tokens:(7) xˆC=Conv3*3(x)

(8) xˆCt=AvgPool(xˆC)

Among them, xˆC is the effective position encoding result, xˆCt and AvgPool represent Carrier Tokens and pooling operations, respectively. In each HAT, CTs undergo the following transformations:(9) xˆCt=xˆCt+γ1*MHSA(LN(xˆCt))

(10) xˆCt=xˆCt+γ2*MLP(LN(xˆCt))

In this process, LN refers to Layer Normalization, MHSA represents Multi-Head Self-Attention, MLP denotes two Multi-Layer Perceptrons with GeLU activation function [51], and γ1 and γ2 are learnable scaling parameters.

To establish short-distance spatial connections, the local features and Carrier Tokens are concatenated. It should be noted that xˆCt,1 represents the unique Carrier Token for the local window. The representation is as follows:(11) xˆw=Concat(xˆ1,xˆCt,1)

xˆw represents the interaction between local tokens and Carrier Tokens. Then, it undergoes another round of attention process:xˆw=xˆw+γ1·MHSA(LN(xˆw)),

(12) xˆw=xˆw+γ2·MLP(LN(xˆw)).

Finally, the tokens are split to be used for subsequent hierarchical attention:(13) xˆ1,xˆCt,1=Split(xˆw)

To facilitate long-range information exchange, FasterViT employs a global information propagation mechanism by upsampling the Carrier Tokens and merging them with the output of the Local window Attention:(14) x=Upsample(xˆCt)+Merge(xˆ1)

The Hierarchical Attention combines the Local window Attention and Carrier Tokens, as indicated by the connections represented by the red arrows in Fig. 3. Carrier Tokens act as messengers, carrying key information across different parts of the image. This allows FasterViT to piece together a holistic view from the detailed analyses conducted on each image segment, ensuring no critical information is overlooked. The Hierarchical Attention mechanism smartly balances the need to understand both the big picture and fine details of an image, doing so in a computationally efficient manner that makes FasterViT suitable for real-time applications, such as monitoring crop health.

3.3 Improved FasterViT

To further improve the performance and efficiency of plant disease recognition, this paper proposes an improved FasterViT model. The improved FasterViT addresses challenges such as high training costs and the inability to adapt to fast learning with small datasets. It introduces a novel hybrid CNN-ViT model, as depicted in Fig. 4. Fig. 4 showcases the streamlined architecture of the improved FasterViT model, highlighting the strategic placement of Mixup and Cutout methods, the reconfigured FasterViT layers with optimized ConvBlocks, and the integration of advanced attention mechanisms to bolster information preservation and feature representation. The improved FasterViT incorporates the following improvements:(1) Cross-stage alternating Mixup and Cutout methods: By employing Mixup and Cutout methods in an alternating fashion, the model simulates a richer dataset by blending images or introducing variations, which accelerates the learning process and enhances the model's ability to generalize from limited data.

(2) Optimization of feature representation: The optimization involves carefully reducing the number of ConvBlock layers and selecting larger kernel sizes, which streamlines the model by reducing computational demands without compromising its ability to extract and process critical features from plant disease images. In Stages 4 and 5, a significant amount of hierarchical attention is employed, along with an increase in the number of layers. The number of Transformer attention heads is also increased to capture fine-grained image details. Increasing hierarchical attention and the number of Transformer attention heads allows the model to more precisely identify and focus on disease-specific patterns within the images, capturing subtle differences that distinguish various plant diseases.

(3) Information preservation: Utilize the Triplet Attention [10] and Adaptive Average Pooling [52] mechanisms. The incorporation of Triplet Attention enables the model to better understand the relationships between different image features, ensuring critical information is highlighted. Simultaneously, Adaptive Average Pooling adjusts pooling operations dynamically, preserving essential details by adapting to the most informative parts of the feature map. Together, these mechanisms ensure that the model retains vital information throughout the processing stages, crucial for accurate disease classification.

Fig. 4 The architecture of the improved FasterViT model.

Fig. 4

3.3.1 Cross-stage alternating Mixup and Cutout methods

The Cross-stage alternating Mixup and Cutout methods are illustrated in Fig. 5. Firstly, the mean and standard deviation of the RGB channels of the training dataset are computed (Normalization). Normalization, by standardizing the scale of the image data, ensures that the Mixup and Cutout methods operate on a consistent basis across the dataset, aiding in the model's faster convergence and more stable training.Fig. 5 The legend for the Cross-stage alternating Mixup and Cutout methods.

Fig. 5

The computation of image mean is given by Formula (1).(15) μ=1wh∑i=1w∑j=1hp(ij)

Wherein, w and h represent the width and height of the image, respectively, and p(ij) denotes the pixel value at position (i, j) with width i and height j.

The computation of standard deviation is given by Formula (2):(16) σ=1wh∑i=1w∑j=1h(p(ij)−μ)2

Wherein, w, h, p(ij), and μ are the same as in Formula (1).

Subsequently, different transformations are applied to the input data during different stages of training. The input data is initially subjected to the combination of Random Rotation [53], Gaussian Blur [54], and Color Jitter [55] strategies. By blending images and their corresponding labels early in training, Mixup [11] introduces a form of regularization that encourages the model to become more robust to variations in the input data, promoting better generalization from the outset. The Formula of Mixup as shown in Formula (3).(17) {xˆ=λxi+(1−λ)xjyˆ=λyi+(1−λ)yj

wherein, (xi, yi) and (xj,yj) are randomly selected two samples and their corresponding labels from the same batch, and λ is a randomly sampled value from a beta distribution, where λ∈[0,1].

During the mid-stage, the input data undergoes processing using Cutout [12]. This involves randomly occluding a portion of the image, aiming to compel the model to focus on the remaining parts of the image and learn important features. It encourages the model to consider more minor features when making decisions, rather than relying solely on the presence of a few major features.

The Cutout method [12], by occluding parts of the image during the mid-stages of training, forces the model to focus on less dominant features, enhancing its ability to identify diseases from partial or obscured visual information.

In the later stage, a random data augmentation strategy is employed, which may involve using only Mixup, only Cutout, or both simultaneously. This randomness implies that each training sample has the opportunity to be subjected to different augmentation methods, enabling rapid learning and generalization under small sample conditions.

This staged approach to applying Mixup, Cutout, and other augmentations allows the model to gradually adapt to increasingly complex representations of the input data, thereby optimizing the learning process and enhancing the model's performance on challenging plant disease recognition tasks.

3.3.2 New FasterViTlayer

The improved FasterViT model improves the flexibility and perceptual capability of feature representation by employing specific structures of New FasterViT layers at different stages. We describe the details of the New FasterViT layers used in Fig. 6.Fig. 6 The implementation of the New FasterViT layers at each stage.

Fig. 6

The employment of large-sized convolutional blocks (64 × 64 and 128 × 128) in the initial stages is designed to ensure the model can capture wide-ranging spatial contexts from the start, enabling it to detect diverse and subtle features indicative of various plant diseases.

Downsampling operations, applied between the early stages, serve to compact the feature maps, reducing computational load while preserving essential information. This step is crucial for incrementally focusing the model's attention on salient features by reducing the resolution of the input data.

Incorporating the HAT mechanism at multiple points allows the model to perform advanced spatial reasoning by focusing on specific areas within the feature map. This targeted attention is vital for accurately identifying plant diseases from complex image backgrounds.

The combination of Triplet Attention and AdaptiveAvgPool mechanisms in the final stage is a strategic enhancement aimed at refining the model's feature representation, which will be further explained in section 3.3.3. Triplet Attention enables nuanced analysis of the image by assessing channel-wise and spatial interactions, while AdaptiveAvgPool ensures that the most informative features are emphasized, enhancing the model's ability to discern subtle disease indicators.

Finally, expanding the number of Transformer attention heads enables the model to simultaneously focus on multiple facets of the image data, significantly improving its capacity to capture fine-grained details necessary for accurate disease classification, especially in images with complex symptoms.

This multi-scale feature modeling approach not only improves the accuracy of plant disease image recognition but is also particularly suitable for small-sample learning environments due to its efficient computation and balanced resource utilization.

3.3.3 The Triplet Attention and AdaptiveAvgPool mechanism

We employ a combination of Triplet Attention and AdaptiveAvgPool mechanisms to enhance the model's ability to capture long-range dependencies. The implementation principle of this mechanism is illustrated in Fig. 7.Fig. 7 Legend of the principle of Triplet Attention and AdaptiveAvgPool mechanism.

Fig. 7

The Triplet Attention mechanism's three-branch structure is designed to meticulously analyze the image data, where one branch focuses on channel-wise interactions to understand feature importance across different channels, another on spatial-wise interactions to capture the spatial relationships within the image, and the third branch enhances focus on critical areas through a spatial attention map generated by Z-Pool and convolution operations. This comprehensive approach ensures a more nuanced understanding of the image content, vital for detecting subtle plant disease indicators.

Z-Pool, by merging the advantages of MaxPooling and AvgPooling, ensures that both the most prominent and the average features are considered, creating a balanced feature map that highlights essential details while maintaining contextual information. This dual approach aids in forming a robust attention map that directs the model's focus to the most informative parts of the image.

AdaptiveAvgPool dynamically adjusts the pooling window, allowing the model to flexibly accommodate images of varying sizes and complexities by ensuring that the feature representation is consistently optimized for the target output. This adaptability is crucial for maintaining high accuracy across diverse plant disease images.

Triplet Attention captures richer contextual information through its three-branch structure, while AdaptiveAvgPool consolidates this information into a fixed-size feature representation. The actual network architecture of this mechanism is depicted in Fig. 8. The low computational cost of the Triplet Attention mechanism, relative to its effectiveness in encoding detailed channel-wise and spatial information, marks a significant advancement in making sophisticated attention mechanisms more scalable and applicable to larger datasets without incurring prohibitive processing overheads.Fig. 8 Triplet attention and AdaptiveAvgPool mechanism network architecture.

Fig. 8

4 Experiments and discussions

4.1 Experimental setup

The dataset used in our project is a citrus image dataset created by our team [56], called In-field small dataset. The In-field small dataset consists of citrus disease-infected leaves collected in the field under adaptive photography mode, which better reflects real-world application scenarios. The adaptive photography mode employed in collecting the citrus dataset ensures that the images more accurately reflect the variability and complexity of real-world conditions, such as differences in lighting, background, and leaf positioning, providing a robust foundation for training our model. The In-field small dataset comprises three categories: Huanglong Disease (HD) leaves (778 images), magnesium deficiency (MD) leaves (759 images), and healthy leaves (1533 images), totaling 3250 images. The images are in the format of 4000 * 3000 * 3. We split the dataset into training, validation, and testing sets in an 8:1:1 ratio. The 8:1:1 split ratio was strategically chosen to maximize the amount of data available for training the model while still reserving enough images for thorough validation and testing, ensuring a comprehensive evaluation of the model's ability to generalize to new data.

Additionally, we utilize the PlantVillage dataset [57] as a pretraining dataset. The PlantVillage dataset consists of 54,303 images of healthy and unhealthy leaves, categorized into 38 classes based on species and diseases. Examples of the two datasets are shown in Fig. 9.Fig. 9 Examples of two datasets.

Fig. 9

The equipment we used is an ASUS FX507ZM (Windows 11 system) with an Intel i7-12700H processor and an NVIDIA RTX 3060 graphics card. The training process is accelerated using GPU. The Python environment is Python 3.9, with Torch 2.0.0, Torchvision 0.15.1, and OpenCV 4.5.5 installed. For data augmentation, we applied the methods described in section 3.3.1. Additionally, we utilized exponential moving average (EMA) to improve performance. During the training process, we employed the AdamW optimizer, mixed-precision training, EMA for weight updates, with an initial learning rate of 0.0001. We used the cosine annealing learning rate adjustment strategy, setting the minimum learning rate to 1E-6. The learning rate was cosine-decayed over 20 epochs. We saved the experimental data during training and performed model evaluation afterwards.

The selection of an NVIDIA RTX 3060 graphics card, coupled with mixed-precision training, significantly enhances the training speed without compromising the model's accuracy, enabling more efficient experimentation and iteration. Data augmentation techniques, such as Mixup and Cutout, introduce variability into the training process, enhancing the model's robustness against overfitting. Simultaneously, the use of EMA for weight updates smoothens the training process, leading to more stable convergence and improved overall performance. Employing the AdamW optimizer, known for its effectiveness in deep learning tasks, in conjunction with a cosine annealing learning rate strategy, ensures gradual and adaptive adjustment of learning rates. This method helps in navigating the optimization landscape more effectively, avoiding local minima and fostering better generalization across epochs.

4.2 Algorithm performance metrics

The performance of the proposed method was evaluated using seven performance metrics: top-1 accuracy, precision, recall, F1 score, F2 score, specificity, and MCC (Matthews correlation coefficient). The performance metrics are described as follows in Table 1.Table 1 Performance metrics.

Table 1Performance Index	Formula	Description	
Accuracy	TP+TNTP+TN+FP+FN	This metric measures the overall correctness of the model across both classes.	
Precision	TPTP+FP	It evaluates the model's accuracy among its positive predictions.	
Recall	TPTP+FN	This measures the model's ability to identify all relevant cases.	
F1 Score	2TP2TP+FN+FP	The F1 Score provides a balance between precision and recall.	
F2 Score	5×Accuracy×Recall4×Accuracy+Recall	The F2 Score is a metric that balances precision and recall, placing more emphasis on recall. It useful for models where missing positive cases is more critical.	
Specificity	TNTN+FP	This metric assesses the model's ability to correctly identify negative cases.	
MCC	TP×TN−FP×FN(TP+FP)(TP+FN)(TN+FP)(TN+FN)	This metric offers a balanced measure of the model's performance across all four confusion matrix categories, useful when classes are imbalanced.	
Note: TP (True Positive): The true class is positive, and the predicted class is also positive. FP (False Positive): The true class is negative, but the predicted class is positive. FN (False Negative): The true class is positive, but the predicted class is negative.TN (True Negative): The true class is negative, and the predicted class is also negative.

We used a confusion matrix [58] as a visualization tool to compare the differences between the predicted results of the classification model and the actual observed results. As shown in Fig. 10, it is a two-dimensional matrix where each row represents the actual class and each column represents the predicted class. The confusion matrix visually represents the model's predictions, with each quadrant corresponding to TP, FP, FN, and TN. This matrix is instrumental in calculating the detailed performance metrics, providing insights into areas where the model excels or needs improvement.Fig. 10 Confusion matrix.

Fig. 10

In addition, we also used ROC curves [59] to evaluate the performance of the model. The ROC curve illustrates the trade-off between sensitivity and specificity at various threshold levels, offering a comprehensive view of the model's performance across all possible classification thresholds. The ROC curve is plotted with the sensitivity (True Positive Rate, TPR) on the y-axis and 1 minus specificity (False Positive Rate, FPR) on the x-axis. The AUC quantifies this performance, with higher values indicating better discrimination between positive and negative classes. By comparing the area under the curve (AUC), we can determine which model more effectively distinguishes between positive and negative samples.

4.3 Experimental and discussion of the classification Model's performance

To verify the feature extraction and fast learning capabilities of the FasterViT model on small sample datasets, as well as whether transfer learning has a positive impact on the model on large datasets, we conducted three sets of algorithm performance experiments, as shown in Fig. 11.Fig. 11 Flow chart of three experiment.

Fig. 11

The three experimental setups are described as follows:Experiment 1 (Baseline Control Group): Training the FasterViT model on the In-field small dataset for 100 epochs without pretraining. This setup establishes baseline performance metrics without the influence of pretraining, focusing on computational efficiency and model's intrinsic learning capabilities

Experiment 2 (Improved Model Performance): Training the improved FasterViT model on the In-field small dataset for 80 epochs without pretraining. Underscoring the enhancements made to the model in terms of speed and resource utilization, and its ability to learn effectively from small datasets

Experiment 3 (Transfer Learning Impact): Pretraining the improved FasterViT model on the PlantVillage dataset for 60 epochs, followed by fine-tuning the model with the optimal parameters for 40 epochs. Fine-tuning is a transfer learning strategy where we transfer the general features learned from a large dataset to our model. We freeze some convolutional layers of the pretrained model and fine-tune the remaining classification layers to achieve the best performance (as shown in Fig. 12). This approach aims to demonstrate the model's enhanced generalization capabilities through transfer learning, highlighting significant performance improvements across all metricsFig. 12 Legend of transfer learning fine-tuning methods.

Fig. 12

The experimental results of three groups of experiments corresponding to different types of diseases in the test set are shown in Table 2, Table 3, Table 4. The transition from Experiment 1 to Experiment 3 showcases a clear trajectory of performance enhancement, with Experiment 2 serving as a pivotal point that emphasizes the improved model's superior feature extraction and learning speed. Experiment 3 further illustrates the substantial impact of transfer learning on model accuracy, precision, and recall.Table 2 Experiment 2 Experimental results of FasterViT.

Table 2Data Type	Huanglong Disease	Magnesium deficiency	Normal	
Accuracy	0.963	0.969	0.988	
Precision	0.971	0.941	0.966	
Recall	0.919	0.960	1.00	
F1 Score	0.944	0.950	0.983	
F2 Score	0.964	0.946	0.978	
Specificity	0.986	0.973	0.981	
MCC	0.935	0.930	0.964	

Table 3 Experiment 1 Experimental results of improved FasterViT.

Table 3Data Type	Huanglong Disease	Magnesium deficiency	Normal	
Accuracy	0.988	0.997	0.991	
Precision	0.982	1.00	0.983	
Recall	0.982	0.990	0.991	
F1 Score	0.982	0.995	0.987	
F2 Score	0.987	1.00	0.987	
Specificity	0.991	1.00	0.991	
MCC	0.979	0.994	0.986	

Table 4 Experiment 3 Experimental results of pre training + improved FasterViT.

Table 4Data Type	Huanglong Disease	Magnesium deficiency	Normal	
Accuracy	0.991	0.994	0.997	
Precision	0.982	0.990	1.00	
Recall	0.991	0.990	0.991	
F1 Score	0.987	0.990	0.996	
F2 Score	0.991	0.990	0.995	
Specificity	0.991	0.996	1.00	
MCC	0.978	0.978	0.998	

Table 3 highlight the improved FasterViT model's exceptional accuracy and precision, notably achieving near-perfect performance in identifying magnesium deficiency. This underscores the model's capability to extract distinctive features even from smaller datasets. Comparing Table 2, Table 3 reveals that the improved FasterViT model significantly outperforms the FasterViT model across all metrics, particularly in recall and MCC for Huanglong Disease, illustrating the advancements in accurately identifying diseased samples without misclassifying healthy leaves. The results from Experiment 3, detailed in Table 4, demonstrate the profound impact of transfer learning on the improved FasterViT model's performance. Notably, the model achieved a remarkable MCC of 0.998 for normal leaves, indicating an almost perfect correlation between predictions and actual classifications. This suggests that pretraining on the diverse PlantVillage dataset significantly enriches the model's feature recognition capabilities, enabling it to generalize effectively across different types of citrus leaf diseases.

The confusion matrix and ROC curve obtained from three experiments are shown in Fig. 13. The confusion matrices in Fig. 13 offer a granular view of the model's classification accuracy, with the improved FasterViT model (Fig. 13(b) and (c)) showing fewer false positives and negatives compared to the FasterViT model (Fig. 13 (a)). Additionally, the ROC curves underscore the improved model's superior true positive rate across all disease categories, highlighting its strong discriminative power. The progressive improvement in performance metrics from Experiment 1 through Experiment 3 not only validates the enhanced FasterViT model's ability to effectively learn from limited data but also, through the application of transfer learning, demonstrates its resilience against overfitting. The fine-tuning process, which tailors the model to the citrus dataset after pretraining on PlantVillage, ensures that the model retains its generalization capabilities without compromising specificity or recall.Fig. 13 Confusion matrix and ROC.

Note: HD represents Huanglong Disease, and MD represents Magnesium-efficacy.

Fig. 13

4.4 Algorithm performance comparison experiment and discussion

We conducted Experiment 4 and Experiment 5 to compare the improved FasterViT [9] with other state-of-the-art classification algorithms such as UniRepLKNet [60], Swin_transformer [61], TransXNet [62], Hiera [63], and RevColV2 [64].Experiment 4 (Training without Pretraining): The goal is to evaluate the raw ability of each model to learn directly from the In field small dataset, and observe its adaptation and learning effectiveness without transfer learning assistance. All six algorithm models were trained on an In field small dataset without using any pre trained models. The improved FasterViT model is trained 80 times, while the other models are trained 100 times. The reason for choosing 80 training sessions is because preliminary tests have shown that the model converges faster, thereby demonstrating its computational efficiency.

Experiment 5 (Training with Pretraining): This experiment showcases show the model's ability to improve classification performance using large-scale and diverse datasets (such as ImageNet-21K), and validate the effectiveness of transfer learning strategies. Each of the six algorithm models utilized pretrained models from the ImageNet-21K dataset [65] and performed fine-tuning on our In-field small dataset. This method is similar to the approach used in Experiment 3.

The experimental results are shown in Table 5. Compared to traditional CNN and Transformer models, the improved FasterViT model demonstrates higher computational performance. This is achieved through the improved FasterViT layer and the introduction of the Triplet Attention mechanism, allowing the model to process each position in the input sequence in parallel and enhance feature extraction capability.Table 5 Validation accuracy of different models in Experiment4 and Experiment5.

Table 5Methods	Experiment4	Experiment5	
Improved FasterViT	0.992	0.996	
UniRepLKNet	0.985	0.992	
Swin_transformer	0.990	0.998	
TansXNet	0.971	0.986	
Hiera	0.973	0.989	
RevColV2	0.988	0.993	

Despite using a small sample dataset, the improved FasterViT outperforms other models in Experiment 4 and continues to deliver impressive performance in Experiment 5, where it is fine-tuned using pretrained models from the ImageNet-21K dataset. These results indicate the model's ability to adapt well to small-scale image classification tasks and exhibit superior scalability. The experiments validate that our model outperforms others in terms of its suitability for small-scale image classification tasks and demonstrates excellent generalization capabilities. The success of transfer learning, as evidenced by the performance improvements in Experiment 5, underscores the model's versatility and its potential for application beyond citrus disease identification, including other agricultural tasks and even unrelated image classification challenges.

In addition, we compared the performance of the aforementioned classification models on the In-field small dataset and measured various metrics such as the number of parameters, FLOPs (Floating Point Operations), and throughput. Table 6 below presents a comparison of performance metrics across models, highlighting the improved FasterViT's efficiency.Table 6 Performance testing of different models.

Table 6	ImageSize	Param(M)	FLOPs(G)	Throughput(Img/Sec)	
Improved FasterViT	2242	15.17	3.3	2470	
UniRepLKNet	2242	10.7	1.6	3949	
Swin_transformer	2242	28.30	4.4	756	
TansXNet	2242	22.40	18.7	3196	
Hiera	2242	35.55	6.1	301	
RevColV2	2242	88.36	19.5	221	
Note: Image Size represents the input size of the data, Param represents the number of learnable parameters in the model, FLOPs represents the number of floating point operations required for inference, and Throughput represents the number of images the model can process per unit time.

Table 6 demonstrates that the relatively compact design of the improved FasterViT model, coupled with its lower FLOPs, makes it an ideal candidate for deployment in resource-constrained environments. This model effectively balances efficiency with high throughput, thereby facilitating real-time inference tasks. The throughput of the improved FasterViT model is markedly higher compared to most models, with the exception of ViT-tiny. The elevated throughput, as evidenced by the improved FasterViT model, ensures rapid execution of image classification tasks, thereby enhancing the responsiveness of applications that depend on this technology, such as automated disease detection systems in agriculture.

While ViT-tiny exhibits superior throughput, it is crucial to consider the equilibrium between throughput, accuracy, and model complexity. The architecture of the improved FasterViT, incorporating the Triplet Attention mechanism, provides a robust framework for handling complex image classification tasks without significantly compromising throughput. This makes it a versatile choice for a range of applications. The lower FLOPs of the improved FasterViT signify that it requires fewer computational resources for inference, translating to lower operational costs. This characteristic makes it particularly suitable for continuous, large-scale deployments, such as automated monitoring systems in agriculture.

Future research could delve into techniques such as model pruning and quantization to further reduce the size and computational demands of the improved FasterViT model. These optimizations could broaden its applicability, particularly in embedded systems or IoT devices tailored for smart agriculture solutions.

5 Conclusions and future work

In this study, we proposed the improved FasterViT model and explored its application in plant disease detection tasks. Through comparative experiments and result analysis, we have drawn the following conclusions:(1) Through the comparison experiments with FasterViT models, we found that the improved FasterViT model exhibits the capability of fast learning and adaptation to small sample training in plant disease detection tasks. This demonstrates the effectiveness of our improvement approach in improving model accuracy and reducing training costs. Our improved FasterViT model introduces the Triplet Attention mechanism, a novel contribution that significantly enhances feature extraction capabilities, particularly in complex plant disease imagery. This advancement directly contributes to the model's exceptional performance in fast learning and accurate classification on small datasets.

(2) We conducted pretraining and fine-tuning experiments and found that the pretraining process plays a crucial role in improving model performance. This indicates that our cross-stage alternating Mixup and Cutout methods help the model learn richer feature representations, thereby enhancing the accuracy of plant disease detection. Pretraining on the extensive ImageNet-21K dataset equips the model with a broad and diverse feature understanding, which, when fine-tuned with our In-field small dataset, results in notably improved accuracy in plant disease detection.

The improved FasterViT model further reveals the potential of hybrid models in plant pest recognition and provides an efficient and accurate solution. Future research directions could include further optimizing the model architecture, exploring more effective pretraining strategies, and applying it to other plant disease detection tasks. In conclusion, this study provides a new approach and solution for deep learning methods in plant disease detection. Future work could explore the integration of additional attention mechanisms or the development of more efficient training strategies to further boost the model's performance and reduce computational requirements, making it even more suitable for deployment in resource-constrained environments. We believe that the improved FasterViT model will play a significant role in plant protection and agricultural production, providing effective support for the detection and precise control of crop diseases.

Data availability statement

Data will be made available on request.

CRediT authorship contribution statement

Jiyang Chen: Writing – original draft, Validation, Investigation, Data curation. Shuai Wang: Writing – review & editing, Validation, Project administration, Methodology, Investigation, Data curation, Conceptualization. Jianwen Guo: Project administration, Funding acquisition, Conceptualization. Fengyi Chen: Visualization, Validation, Formal analysis. Yuchen Li: Writing – original draft, Software. Honghua Qiu: Validation, Data curation.

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work the authors used ChatGPT in order to improve language and readability. After using this tool/service, the authors reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Jianwen Guo reports financial support was provided by Science and Technology Bureau of Dongguan City, Guangdong Province, China. Jianwen Guo reports was provided by Department of Education of Guangdong Province, China. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work is supported in part by the Dongguan Science and Technology of Social Development Program (20221800905102 ), and Project of Education Department of Guangdong Province (2022ZDZX4053 , pdjh2022b0512 ).
==== Refs
References

1 Sankaran S. Mishra A. Ehsani R. Davis C. A review of advanced techniques for detecting plant diseases Comput. Electron. Agric. 72 2010 1 13
2 LeCun Y. Bengio Y. Hinton G. Deep learning Nature 521 2015 436 444 26017442
3 Alsahafi Y.S. Kassem M.A. Hosny K.M. Skin-Net: a novel deep residual network for skin lesions classification using multilevel feature extraction and cross-channel correlation with detection of outlier J. Big Data 10 2023 105
4 Hosny K.M. Kassem M.A. Foaud M.M. Skin melanoma classification using ROI and data augmentation with deep convolutional neural networks Multimed. Tool. Appl. 79 2020 24029 24055
5 Mohanty S.P. Hughes D.P. Salathé M. Using deep learning for image-based plant disease detection Front. Plant Sci. 7 2016 1419 27713752
6 Bahdanau D. Cho K. Bengio Y. Aharoni R. Neural machine translation by jointly learning to align and translate arXiv preprint arXiv:1409.0473 2014
7 Vaswani A. Shazeer N. Parmar N. Uszkoreit J. Jones L. Gomez A.N. Kaiser L. Polosukhin I. Attention is all you need Adv. Neural Inf. Process. Syst. 2017 5998 6008
8 Dosovitskiy A., Beyer L., Kolesnikov A., Weissenborn D., Zhai X., Unterthiner T., Dehghani M., Minderer M., Heigold G., Gelly S., Uszkoreit J., Houlsby N., An image is worth 16x16 words: transformers for image recognition at scale,arXiv preprint arXiv:2010.11929.
9 Hatamizadeh A. Heinrich G. Yin H. Tao A. Alvarez J.M. Kautz J. Molchanov P. FasterViT: fast vision transformers with hierarchical attention arXiv preprint arXiv:2306.06189 2023
10 Misra D. Nalamada T. Arasanipalai A.U. Hou Q. Triplet attention: lightweight attention mechanism for computer vision tasks 2021 IEEE Winter Conf. Appl. Comput. Vision (WACV) 2021 259 268
11 Zhang H., Cisse M., Dauphin Y.N., Lopez-Paz D., Mixup: beyond empirical risk minimization, arXiv preprint arXiv:1710.09412.
12 Devries T., Taylor G.W., Improved regularization of convolutional neural networks with Cutout, arXiv preprint arXiv:1708.04552.
13 Zhang S. Xie Y. Dong J. Research on plant leaf disease recognition based on image processing Mod. Comput. 27 2021 112 116
14 Nettleton D.F. Katsantonis D. Kalaitzidis A. Sarafijanovic-Djukic N. Puigdollers P. Confalonieri R. Predicting rice blast disease: machine learning versus process-based models BMC Bioinf. 20 2019 1 16
15 Ma S. Ju Z. Wang G. Fruit and vegetable classification method based on principal component analysis and ensemble distance. Electron Sci. Technol. 32 2019 1 6
16 Hossain E. Hossain M.F. Rahaman M.A. A color and texture-based approach for the detection and classification of plant leaf disease using KNN classifier 2019 Int. Conf. Electr., Comput. Commun. Eng. (ECCE) 2019 IEEE 1 6
17 Jha S., Luhach V., Gupta G.S., Singh B., Crop disease classification using support vector machines with green chromatic coordinate (GCC) and attention based feature extraction for IoT based smart agricultural applications, arXiv preprint arXiv:2311.00429.
18 Meng L. Guo X. Du J. A lightweight CNN model for crop disease image recognition Jiangsu J. Agric. Sci. 37 2021 1143 1150
19 Yao J. Tran S.N. Garg S. Sawyer S. Deep learning for plant identification and disease classification from leaf images: multi-prediction approaches ACM Computing Surveys 56 6 2024 1 37
20 Azad B. Abdalla A. Won K. Nafchi A.M. Improving FHB screening in wheat breeding using an efficient transformer model 2023 ASABE Annu. Int. Meet. 2023 Am. Soc. Agric. Biol. Eng. 1
21 Zhang W. Sun X. Qiao Y. Identification of tobacco diseases based on InceptionV3 Chin. J. Tob. Sci. 27 2021 61 70
22 Liu S. Improvement of small sample classification method based on contrastive learning and its application in grape leaf disease classification Chin. Agric. Sci. Technol. Inf. 25 2023 69 75
23 Li J. Wang Z. Fan L. Zhao H. Small sample learning method for apple leaf disease image classification J. Jilin Univ. (Lib. Inf. Sci. Ed.) 41 2023 1 7
24 Zhang Y. Su Q. Corn disease image recognition based on transfer learning J. Shanghai Univ. Eng. Sci. 39 2022 1 6
25 Cao H. Fang R. Classification and recognition of mango diseases and pests based on deep learning J. Chengdu Univ. Inf. Technol. (Nat. Sci. Ed.) 40 2023 1 6
26 Faizal S. Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks. arXiv Preprint arXiv:2210.09290 2022
27 Yu W. Zhou P. Yan S. Wang X. InceptionNeXt: when Inception Meets ConvNeXt 2023 arXiv preprint arXiv:2303.16900
28 Munir M. Avery W. Marculescu R. MobileViG: graph-based sparse attention for mobile vision applications Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. 2023 2210 2218
29 Zhang J. Rethinking Mobile Block for Efficient Neural Models. arXiv Preprint arXiv:2301.01146 2023
30 Tian Y. Xie L. Wang Z. Wei L. Zhang X. Jiao J. …Ye Q. Integrally pre-trained transformer pyramid networks Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit 2023 18610 18620
31 Bahdanau D. Cho K. Bengio Y. Neural Machine Translation by Jointly Learning to Align and Translate. arXiv Preprint arXiv:1409.0473 2014
32 Luong M.T. Pham H. Manning C.D. Effective approaches to attention-based neural machine translation Proc. 2015 Conf. Empir. Methods Nat. Lang. Process. (EMNLP) 2015 1412 1421
33 Hu J. Shen L. Sun G. Squeeze-and-excitation networks Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) 2018 7132 7141
34 Woo S. Park J. Lee J.Y. Kweon I.S. CBAM: Convolutional Block Attention Module 2018 Proc. Eur. Conf. Comput. Vis. (ECCV) 3 19
35 Chen Y. Kalantidis Y. Li J. Yan S. Feng J. A2-Nets: double attention networks Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit 2020 CVPR) 212 221
36 Zhang H. Wu C. Zhang Z. Zhu Y. Zhang Z. Lin H. Sun Y. ECA-net: Efficient Channel Attention for Deep Convolutional Neural Networks 2020 Proc. Eur. Conf. Comput. Vis. (ECCV) 3 19
37 Yang L. Zhang R.-Y. Li L. Xie X. SimAM: a simple, parameter-free attention module for convolutional neural networks Proc. 38th Int. Conf. Mach. Learn. (PMLR) 139 2021 11863 11874
38 Ben-David S. Blitzer J. Crammer K. Kulesza A. Pereira F. Vaughan J.W. Analysis of representations for domain adaptation Adv. Neural Inf. Process. Syst. 2007 137 144
39 Devlin J. Chang M.-W. Lee K. Toutanova K. Bert: pre-training of deep bidirectional transformers for language understanding Proc. NAACL-HLT 1 2019
40 Yosinski J. Clune J. Bengio Y. Lipson H. How transferable are features in deep neural networks? Adv. Neural Inf. Process. Syst. 2014 3320 3328
41 Ganin Y. Lempitsky V. Srikumar V. Unsupervised domain adaptation by backpropagation Proc. Int. Conf. Mach. Learn. 2016 1180 1189
42 Mall A. Kabra S. Lhila A. Ajmera P. AMaizeD: an End to End Pipeline for Automatic Maize Disease Detection. arXiv Preprint arXiv:2308.03766 2023
43 Jiang Z. Dong Z. Jiang W. Yang Y. Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning Comput. Electron. Agric. 186 2021 106184
44 Ahmed N. Image quality assessment for foliar disease identification (agropath) J. Agric. Res. 59 2021 177 186
45 Ashmafee M.H. Ahmed T. Ahmed S. Hasan M.B. Jahan M.N. Rahman A.A. An efficient transfer learning-based approach for apple leaf disease classification 2023 Int. Conf. Electr., Comput. Commun. Eng. (ECCE) 2023 1 6
46 Nair V. Hinton G.E. Rectified linear units improve restricted Boltzmann machines Proc. 27th Int. Conf. Mach. Learn. (ICML) 2010 807 814
47 Krizhevsky A. Sutskever I. Hinton G.E. ImageNet classification with deep convolutional neural networks Adv. Neural Inf. Process. Syst. 2012 1097 1105
48 Simonyan K. Zisserman A. Very deep convolutional networks for large-scale image recognition 2014 arXiv:1409.1556
49 LeCun Y. Bottou L. Bengio Y. Haffner P. Gradient-based learning applied to document recognition Proc. IEEE 86 1998 2278 2324 10.1109/5.726791
50 Ioffe S. Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift Proc. Int. Conf. Mach. Learn. 2015 448 456
51 Hendrycks D. Gimpel K. Gaussian Error Linear Units (GELUs) 2016 arXiv preprint arXiv:1606.08415
52 Lin M. Chen Q. Yan S. Network in Network 2014 arXiv preprint arXiv:1312.4400
53 Simard P.Y. Steinkraus D. Platt J.C. Best practices for convolutional neural networks applied to visual document analysis Proc. 7th Int. Conf. Doc. Anal. Recognit. (ICDAR) 2 2003 958 963
54 Gedraite E.S. Hadad M. Investigation on the effect of a Gaussian Blur in image filtering and segmentation Proceedings ELMAR-2011 2011, September IEEE 393 396
55 Wang J. Perez L. The effectiveness of data augmentation in image classification using deep learning Convolut. Neural Networks Vis. Recognit. 11 2017 1 8
56 Li Y. Guo J. Qiu H. Chen F. Zhang J. Denoising diffusion probabilistic models and transfer learning for citrus disease diagnosis Front. Plant Sci. 14 2023 1267810
57 PlantVillage. [Online]. Available:.https://plantvillage.psu.edu/projects.
58 Görtler J. Hohman F. Moritz D. Neo: generalizing confusion matrix visualization to hierarchical and multi-output labels Proc. 2022 CHI Conf 2022 Hum. Factors Comput. Syst. 1 13
59 Shannon C. Weaver W. The mathematical theory of communication Bell Syst. Tech. J. 27 1949 379 423
60 Ding X. Zhang Y. Ge Y. Zhao S. Song L. Yue X. Shan Y. UniRepLKNet: a universal perception large-kernel ConvNet for audio, video, point cloud Time-Series and Image Recognition 2023 arXiv preprint arXiv:2311.15599
61 Liu Z. Lin Y. Cao Y. Hu H. Wei Y. Zhang Z. Huang T. Swin Transformer: hierarchical vision transformer using shifted windows Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. 2021 10215 10224
62 Lou M. Zhou H. Yang S. Yu Y. TransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Visual Recognition 2023 arXiv preprint arXiv:2310.19380
63 Ryali C. Hu Y. Bolya D. Wei C. Fan H. Huang P. Feichtenhofer C. Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles 2023 arXiv preprint arXiv:2306.00989
64 Han Q. Cai Y. Zhang X. RevColV2: exploring disentangled representations in masked image modeling Adv. Neural Inf. Process. Syst. 36 2024
65 Deng J. Dong W. Socher R. Li L.J. Li K. Fei-Fei L. Imagenet: a large-scale hierarchical image database Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2009 248 255
