Wheat Stripe Rust Grading by Deep Learning With Attention Mechanism and Images From Mobile Devices

Mi, Zhiwen; Zhang, Xudong; Su, Jinya; Han, Dejun; Su, Baofeng

doi:10.3389/fpls.2020.558126

Cited by 79 publications

(44 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The second method consists of using a LiDE 300 Canon scanner (Canon (China) Ltd., Beijing, China) to scan the disease-damaged leaves and obtain the DR value by image processing. An increasing number of studies have used image processing methods rather than visual interpretation for yellow rust detection due to improvements in image processing technology (Mi, Zhang, Su, Han, & Su, 2020). This method provides more accurate results than the first method.…”

Section: Disease Severity Determinationmentioning

confidence: 99%

A new spectral index for the quantitative identification of yellow rust using fungal spore information

Ren

Huang

et al. 2021

Big Earth Data

View full text Add to dashboard Cite

Yellow rust (Puccinia striiformis f. sp. Tritici) is a frequently occurring fungal disease of winter wheat (Triticum aestivum L.). During yellow rust infestation, fungal spores appear on the surface of the leaves as yellow and narrow stripes parallel to the leaf veins. We analyzed the effect of the fungal spores on the spectra of the diseased leaves to find a band sensitive to yellow rust and established a new vegetation index called the yellow rust spore index (YRSI). The estimation accuracy and stability were evaluated using two years of leaf spectral data, and the results were compared with eight indices commonly used for yellow rust detection. The results showed that the use of the YRSI ranked first for estimating the disease ratio for the 2017 spectral data (R 2 = 0.710, RMSE = 0.097) and outperformed the published indices (R 2 = 0.587, RMSE = 0.120) for the validation using the 2002 spectral data. The random forest (RF), k-nearest neighbor (KNN), and support vector machine (SVM) algorithms were used to test the discrimination ability of the YRSI and the eight commonly used indices using a mixed dataset of yellow-rust-infested, healthy, and aphid-infested wheat spectral data. The YRSI provided the best performance.

show abstract

Section: Disease Severity Determinationmentioning

confidence: 99%

A new spectral index for the quantitative identification of yellow rust using fungal spore information

Ren

Huang

et al. 2021

Big Earth Data

View full text Add to dashboard Cite

show abstract

“…The attention mechanism can assign larger weights to regions of interest and smaller weights to backgrounds and extract information that contributes more to classification to optimize the model and to make judgments that are more accurate. In other studies, attention mechanisms have achieved excellent performance in tasks, such as classification, detection, and segmentation (Hu et al, 2018;Karthik et al, 2020;Mi et al, 2020;Hou et al, 2021…”

Section: Introductionmentioning

confidence: 99%

Identification of Apple Leaf Diseases by Improved Deep Convolutional Neural Networks With an Attention Mechanism

et al. 2021

View full text Add to dashboard Cite

The accurate identification of apple leaf diseases is of great significance for controlling the spread of diseases and ensuring the healthy and stable development of the apple industry. In order to improve detection accuracy and efficiency, a deep learning model, which is called the Coordination Attention EfficientNet (CA-ENet), is proposed to identify different apple diseases. First, a coordinate attention block is integrated into the EfficientNet-B4 network, which embedded the spatial location information of the feature by channel attention to ensure that the model can learn both the channel and spatial location information of important features. Then, a depth-wise separable convolution is applied to the convolution module to reduce the number of parameters, and the h-swish activation function is introduced to achieve the fast and easy to quantify the process. Afterward, 5,170 images are collected in the field environment at the apple planting base of the Northwest A&F University, while 3,000 images are acquired from the PlantVillage public data set. Also, image augmentation techniques are used to generate an Apple Leaf Disease Identification Data set (ALDID), which contains 81,700 images. The experimental results show that the accuracy of the CA-ENet is 98.92% on the ALDID, and the average F1-score reaches .988, which is better than those of common models such as the ResNet-152, DenseNet-264, and ResNeXt-101. The generated test dataset is used to test the anti-interference ability of the model. The results show that the proposed method can achieve competitive performance on the apple disease identification task.

show abstract

“… Li et al (2020) used the GoogleNet model and embedded SENet attention mechanism to enhance information expression of Solanaceae diseases, with an accuracy rate of 95.09%, and the model size is 14.68 MB, which can be applied to the mobile terminal to identify Solanaceae disease. Mi et al (2020) proposed a novel deep learning network, namely, C-DenseNet, which embeds convolutional block attention module (CBAM) in the densely connected convolutional network with an accuracy rate of 97.99%. Wang et al (2021) proposed a novel lightweight model (ECA-SNet) based on Shufflenet-V2 as the backbone network and introduced an effective channel attention strategy to enhance the model’s ability to extract fine-grained lesion features with an accuracy rate of 98.86%.…”

Section: Introductionmentioning

confidence: 99%

Rubber Leaf Disease Recognition Based on Improved Deep Convolutional Neural Networks With a Cross-Scale Attention Mechanism

Zeng

Zhang

et al. 2022

Front. Plant Sci.

View full text Add to dashboard Cite

Natural rubber is an essential raw material for industrial products and plays an important role in social development. A variety of diseases can affect the growth of rubber trees, reducing the production and quality of natural rubber. Therefore, it is of great significance to automatically identify rubber leaf disease. However, in practice, different diseases have complex morphological characteristics of spots and symptoms at different stages and scales, and there are subtle interclass differences and large intraclass variation between the symptoms of diseases. To tackle these challenges, a group multi-scale attention network (GMA-Net) was proposed for rubber leaf disease image recognition. The key idea of our method is to develop a group multi-scale dilated convolution (GMDC) module for multi-scale feature extraction as well as a cross-scale attention feature fusion (CAFF) module for multi-scale attention feature fusion. Specifically, the model uses a group convolution structure to reduce model parameters and provide multiple branches and then embeds multiple dilated convolutions to improve the model’s adaptability to the scale variability of disease spots. Furthermore, the CAFF module is further designed to drive the network to learn the attentional features of multi-scale diseases and strengthen the disease features fusion at different scales. In this article, a dataset of rubber leaf diseases was constructed, including 2,788 images of four rubber leaf diseases and healthy leaves. Experimental results show that the accuracy of the model is 98.06%, which was better than other state-of-the-art approaches. Moreover, the model parameters of GMA-Net are only 0.65 M, and the model size is only 5.62 MB. Compared with MobileNetV1, V2, and ShuffleNetV1, V2 lightweight models, the model parameters and size are reduced by more than half, but the recognition accuracy is also improved by 3.86–6.1%. In addition, to verify the robustness of this model, we have also verified it on the PlantVillage public dataset. The experimental results show that the recognition accuracy of our proposed model is 99.43% on the PlantVillage dataset, which is also better than other state-of-the-art approaches. The effectiveness of the proposed method is verified, and it can be used for plant disease recognition.

show abstract

Wheat Stripe Rust Grading by Deep Learning With Attention Mechanism and Images From Mobile Devices

Cited by 79 publications

References 26 publications

A new spectral index for the quantitative identification of yellow rust using fungal spore information

A new spectral index for the quantitative identification of yellow rust using fungal spore information

Identification of Apple Leaf Diseases by Improved Deep Convolutional Neural Networks With an Attention Mechanism

Rubber Leaf Disease Recognition Based on Improved Deep Convolutional Neural Networks With a Cross-Scale Attention Mechanism

Contact Info

Product

Resources

About