Attention embedded residual CNN for disease detection in tomato leaves

Karthik, R.; Hariharan, M.; Anand, Sundar; Mathikshara, Priyanka; Johnson, Annie; Menaka, R.

doi:10.1016/j.asoc.2019.105933

Cited by 297 publications

(100 citation statements)

References 32 publications

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“…The original AlexNet and VGG16 models have a sophisticated structure and many parameters, which greatly limit the practical application and deployment of the model. Karthik et al [34] proposed an attentionbased deep residual network to detect the infection type of tomato leaves. The experiment used the PlantVillage dataset, amongst which 95,999 images were used as training models and 24,001 images were used for validation.…”

Section: Research Progress Of Tomato Disease Image Recognitionmentioning

confidence: 99%

Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model

Wang

2020

Plant Methods

164

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Background: Tomato gray leaf spot is a worldwide disease, especially in warm and humid areas. The continuous expansion of greenhouse tomato cultivation area and the frequent introduction of foreign varieties in recent years have increased the severity of the epidemic hazards of this disease in some tomato planting bases annually. This disease is a newly developed one. Thus, farmers generally lack prevention and control experience and measures in production; the disease is often misdiagnosed or not prevented and controlled timely; this condition results in tomato production reduction or crop failure, which causes severe economic losses to farmers. Therefore, tomato gray leaf spot disease should be identified in the early stage, which will be important in avoiding or reducing the economic loss caused by the disease. The advent of the era of big data has facilitated the use of machine learning method in disease identification. Therefore, deep learning method is proposed to realise the early recognition of tomato gray leaf spot. Tomato growers need to develop the app of image detection mobile terminal of tomato gray leaf spot disease to realise real-time detection of this disease. Results: This study proposes an early recognition method of tomato leaf spot based on MobileNetv2-YOLOv3 model to achieve a good balance between the accuracy and real-time detection of tomato gray leaf spot. This method improves the accuracy of the regression box of tomato gray leaf spot recognition by introducing the GIoU bounding box regression loss function. A MobileNetv2-YOLOv3 lightweight network model, which uses MobileNetv2 as the backbone network of the model, is proposed to facilitate the migration to the mobile terminal. The pre-training method combining mixup training and transfer learning is used to improve the generalisation ability of the model. The images captured under four different conditions are statistically analysed. The recognition effect of the models is evaluated by the F1 score and the AP value, and the experiment is compared with Faster-RCNN and SSD models. Experimental results show that the recognition effect of the proposed model is significantly improved. In the test dataset of images captured under the background of sufficient light without leaf shelter, the F1 score and AP value are 94.13% and 92.53%, and the average IOU value is 89.92%. In all the test sets, the F1 score and AP value are 93.24% and 91.32%, and the average IOU value is 86.98%. The object detection speed can reach 246 frames/s on GPU, the extrapolation speed for a single 416 × 416 picture is 16.9 ms, the detection speed on CPU can reach 22 frames/s, the extrapolation speed is 80.9 ms and the memory occupied by the model is 28 MB. Conclusions: The proposed recognition method has the advantages of low memory consumption, high recognition accuracy and fast recognition speed. This method is a new solution for the early prediction of tomato leaf spot and a new idea for the intelligent diagnosis of tomato leaf spot.

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Section: Research Progress Of Tomato Disease Image Recognitionmentioning

confidence: 99%

Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model

Wang

2020

Plant Methods

164

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“…In the field of crop disease classification, most researchers have tended to use transfer learning technology. There has also been some research on crop disease identification based on the attention mechanism (Nie et al, 2019;Karthik et al, 2020). These previous studies have focused on a certain crop, and so the disease category and scale of the dataset are limited.…”

Section: Attention Mechanismmentioning

confidence: 99%

Fine-Grained Image Classification for Crop Disease Based on Attention Mechanism

Yang

et al. 2020

Front. Plant Sci.

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Fine-grained image classification is a challenging task because of the difficulty in identifying discriminant features, it is not easy to find the subtle features that fully represent the object. In the fine-grained classification of crop disease, visual disturbances such as light, fog, overlap, and jitter are frequently encountered. To explore the influence of the features of crop leaf images on the classification results, a classification model should focus on the more discriminative regions of the image while improving the classification accuracy of the model in complex scenes. This paper proposes a novel attention mechanism that effectively utilizes the informative regions of an image, and describes the use of transfer learning to quickly construct several fine-grained image classification models of crop disease based on this attention mechanism. This study uses 58,200 crop leaf images as a dataset, including 14 different crops and 37 different categories of healthy/diseased crops. Among them, different diseases of the same crop have strong similarities. The NASNetLarge fine-grained classification model based on the proposed attention mechanism achieves the best classification effect, with an F1 score of up to 93.05%. The results show that the proposed attention mechanism effectively improves the fine-grained classification of crop disease images.

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“…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

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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.

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Attention embedded residual CNN for disease detection in tomato leaves

Cited by 297 publications

References 32 publications

Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model

Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model

Fine-Grained Image Classification for Crop Disease Based on Attention Mechanism

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

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