L2MXception: an improved Xception network for classification of peach diseases

Yao, Na; Ni, Fuchuan; Wang, Ziyan; Luo, Jun; Sung, Wing‐Kin; Luo, Chaoxi; Li, Guoliang

doi:10.1186/s13007-021-00736-3

Cited by 23 publications

(10 citation statements)

References 31 publications

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“…The original images of peach diseases (see Figure 1 , Yao et al, 2021 for detail) were collected to form the Peach Disease Image Dataset (PDID). The numbers of images acquired for brown rot disease, anthracnose disease, scab disease, bacterial shot hole disease, gummosis disease, powdery mildew disease, and leaf curl disease were 94, 157, 654, 427, 91, 50, and 87, respectively (see Table 1 for detail).…”

Section: Methodsmentioning

confidence: 99%

“… Major plant diseases of peach. (A) Brown rot of fruit, (B) brown rot of fruit, (C) brown rot of leaf, (D) anthracnose of fruit, (E) anthracnose of leaf, (F) scab of fruit, (G) scab of leaf, (H) bacterial shot hole of fruit, (I) powdery mildew of fruit, (J) powdery mildew of leaf, (K) leaf curl of a leaf, and (L) gummosis of a branch ( Yao et al, 2021 ). …”

Section: Methodsmentioning

confidence: 99%

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Deep Learning-Based Segmentation of Peach Diseases Using Convolutional Neural Network

Yao

et al. 2022

Front. Plant Sci.

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Peach diseases seriously affect peach yield and people’s health. The precise identification of peach diseases and the segmentation of the diseased areas can provide the basis for disease control and treatment. However, the complex background and imbalanced samples bring certain challenges to the segmentation and recognition of lesion area, and the hard samples and imbalance samples can lead to a decline in classification of foreground class and background class. In this paper we applied deep network models (Mask R-CNN and Mask Scoring R-CNN) for segmentation and recognition of peach diseases. Mask R-CNN and Mask Scoring R-CNN are classic instance segmentation models. Using instance segmentation model can obtain the disease names, disease location and disease segmentation, and the foreground area is the basic feature for next segmentation. Focal Loss can solve the problems caused by difficult samples and imbalance samples, and was used for this dataset to improve segmentation accuracy. Experimental results show that Mask Scoring R-CNN with Focal Loss function can improve recognition rate and segmentation accuracy comparing to Mask Scoring R-CNN with CE loss or comparing to Mask R-CNN. When ResNet50 is used as the backbone network based on Mask R-CNN, the segmentation accuracy of segm_mAP_50 increased from 0.236 to 0.254. When ResNetx101 is used as the backbone network, the segmentation accuracy of segm_mAP_50 increased from 0.452 to 0.463. In summary, this paper used Focal Loss on Mask R-CNN and Mask Scoring R-CNN to generate better mAP of segmentation and output more detailed information about peach diseases.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Deep Learning-Based Segmentation of Peach Diseases Using Convolutional Neural Network

Yao

et al. 2022

Front. Plant Sci.

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“…Additionally, the Xception model outperforms Inception V3 in terms of mean accuracy prediction (mAP) when evaluated against the FastEval14k dataset, containing 14,000 images classified into 6000 classes. In another report, Yao et al [27] conducted a study on the classification of peach disease using the traditional Xception and the proposed improved Xception model. The proposed improved Xception network was based on ensembles of regularization terms of the L2-norm and mean.…”

Section: Xceptionmentioning

confidence: 99%

Indigenous Food Recognition Model Based on Various Convolutional Neural Network Architectures for Gastronomic Tourism Business Analytics

et al. 2021

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In gastronomic tourism, food is viewed as the central tourist attraction. Specifically, indigenous food is known to represent the expression of local culture and identity. To promote gastronomic tourism, it is critical to have a model for the food business analytics system. This research undertakes an empirical evaluation of recent transfer learning models for deep learning feature extraction for a food recognition model. The VIREO-Food172 Dataset and a newly established Sabah Food Dataset are used to evaluate the food recognition model. Afterwards, the model is implemented into a web application system as an attempt to automate food recognition. In this model, a fully connected layer with 11 and 10 Softmax neurons is used as the classifier for food categories in both datasets. Six pre-trained Convolutional Neural Network (CNN) models are evaluated as the feature extractors to extract essential features from food images. From the evaluation, the research found that the EfficientNet feature extractor-based and CNN classifier achieved the highest classification accuracy of 94.01% on the Sabah Food Dataset and 86.57% on VIREO-Food172 Dataset. EFFNet as a feature representation outperformed Xception in terms of overall performance. However, Xception can be considered despite some accuracy performance drawback if computational speed and memory space usage are more important than performance.

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“…Figure 4 shows the steps involve in the pre-processing. [17], VGG16 [18], VGG19 [19], InceptionV3 [20], and Xception [21]will be used. In order to get the best result to detect glaucoma, it is needed to fine-tune the required layers of the pre-trained models and also needed to adjust the epochs.…”

Section: B Image Pre-processingmentioning

confidence: 99%

Detection of Glucoma and Diabetes through Image Processing and Machine Learning Approaches

Bandyopadhyay¹,

Bose²,

Dutta³

et al. 2021

Preprint

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In the last few decades, glaucoma became the second biggest leading cause of irreversible vision loss. Because of its asymptotic growth, it is not properly diagnosed until the relatively late stage. To stop the severe damage by glaucoma it is needed to detect glaucoma in its early stages. Surprisingly diabetes also be the greatest cause of glaucoma. In the modern era, artificial intelligence makes great progress in the medical image processing field. Image analysis based on machine learning gives a huge success in diagnosis glaucoma without any misdiagnosis. The aim of this proposed paper is to create an automated process that can detect glaucoma and diabetic retinopathy. Here various Machine Learning models are used and results of these methods are presented.

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L2MXception: an improved Xception network for classification of peach diseases

Cited by 23 publications

References 31 publications

Deep Learning-Based Segmentation of Peach Diseases Using Convolutional Neural Network

Deep Learning-Based Segmentation of Peach Diseases Using Convolutional Neural Network

Indigenous Food Recognition Model Based on Various Convolutional Neural Network Architectures for Gastronomic Tourism Business Analytics

Detection of Glucoma and Diabetes through Image Processing and Machine Learning Approaches

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