The Plant Pathology Challenge 2020 data set to classify foliar disease of apples

Thapa, Ranjita; Zhang, Kai; Snavely, Noah; Belongie, Serge; Khan, Awais

doi:10.1002/aps3.11390

Cited by 128 publications

(82 citation statements)

References 29 publications

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“…The dataset were obtained from FGVC7 [30] (https://www.kaggle.com/c/plant-pathology-2020-fgvc7) and Baidu AI Studio (https://aistudio.baidu.com/aistudio-/datasetdetail/11591). All images were collected in natural environment, with uneven illumination, small disease spot and big background noise.…”

Section: A Dataset and Data Preprocessingmentioning

confidence: 99%

Apple Leaf Disease Recognition and Sub-Class Categorization Based on Improved Multi-Scale Feature Fusion Network

et al. 2021

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Apple diseases cause a lot of economic losses to fruit growers in China. Early diagnosis and accurate recognition of apple diseases can control the spread of disease and reduce production costs. However, the significance of disease characteristic of apple leaves in complex environment is relatively weak, and the fine-grain among different diseases of apple leaves is high, and the conventional feature extraction methods will lose the discrimination information. To solve these problems, an apple disease classification model based on multi-scale feature fusion is proposed in this paper. Firstly, the information flow of conventional residual network (ResNet) was improved to achieve efficient information circulation through changing the position of batch normalization and rectified linear unit (ReLU). Secondly, in order to solve the problem of serious loss of information in ResNet downsample, the channel projection and spatial projection of downsample were separated. Lastly, the 3×3 conv in ResBlocks was replaced by pyramid convolution, and the dilated convolution with different dilation rate was introduced into pyramid convolution to enhance the output scale of feature maps and improve the robustness of the model. The optimized model was verified on the dataset of this paper, and the optimized model had stronger anti-noise ability and better robustness, excellent learning effect and fast convergence speed. The classification accuracy on the original dataset is 94.24%, and that on the preprocessed dataset is 94.99%. The results demonstrate that the optimal model has a high accuracy, which can provide a reference for the prevention and control of apple leaf diseases.INDEX TERMS Apple disease, multi-scale feature fusion, deep learning, ResNet, classification.

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Section: A Dataset and Data Preprocessingmentioning

confidence: 99%

Apple Leaf Disease Recognition and Sub-Class Categorization Based on Improved Multi-Scale Feature Fusion Network

et al. 2021

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“…Then the performance of VGG16 model is evaluated by modifying the number of images and super parameters, and the conclusion that VGG16 model is superior to AlexNet model is drawn. Thapa et al (2020) [22] use CNN to apply functional neural network to 3,651 high-quality images of real symptoms of apple leaf disease, and the accuracy reaches 97%, which improves the efficiency and accuracy of disease monitoring. Chao et al (2020) [23] propose a DCNN model, which uses ATLDs (apple tree leaf diseases) recognition combined with DenseNet and Xcption, and replaces the fully-connected layer with a global average pooling layer.…”

Section: B Recognition and Classification Of Imagesmentioning

confidence: 99%

An Improved CNN-Based Apple Appearance Quality Classification Method With Small Samples

et al. 2021

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Apple quality classification is an important means to refine apple sales market and promote apple sales. At present, most of classification methods based on a convolutional neural network (CNN) depend on the quantity of training samples to get good performance. But due to the lack of large-scale public apple appearance dataset, it is a big challenge to obtain high accuracy of apple appearance quality classification with small samples. Therefore, we propose an improved method based on CNN for apple appearance, quality classification with small samples. Firstly, support vector machine (SVM) is used for image segmentation to avoid the decrease of recognition accuracy caused by environmental noise. Secondly, the segmented image data are input into deep convolutional generative adversarial networks (DCGAN) model, which is used for data expansion. Thirdly, the improved ResNet50 (Imp-ResNet50) is proposed as follows: Replace the fully-connected layer with global average pooling layer; Add the dropout algorithm and batch normalization algorithm at the fully-connected layer; Replace the activation function ReLU with Swish. Through comparative experiments with 360 apple images, we verify the performance of the proposed method including the training image quality, the running time, and classification accuracy. The result shows that the proposed method can obtain high quality training samples and reduce the running time of the method effectively. At the same time, it can realize higher classification accuracy that is up to 96.5%, which is higher than the previous classification method.INDEX TERMS Apple quality classification, SVM, DCGAN, ResNet50.

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“…Other Agriculture Datasets: There also exists different agriculture based datasets designed for the CV community. Ranjita et al [16] proposed a large volume database which has real-life symptom images of multiple apple foliar diseases. This database is used to identify the category of foliar diseases in multiple apple leaves.…”

Section: Related Workmentioning

confidence: 99%

FGrade: A Large Volume Dataset for Grading Tomato Freshness Quality

Das

Kar

Sekh

2021

Communications in Computer and Information Science

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Quality-based grouping or grading of fruits and vegetables played an important role in many stages of agricultural production. The method is used for sorting items based on the size, color, presence of damage etc. Manual grading is laborious and time consuming. Hence, not suitable for processing massive amounts. Grading different fruits and vegetables based on size or damages is popular in literature. More complex problems such as freshness based grading got less attention.Here, we introduce a new freshness grading dataset namely FGrade. The dataset is a collection of (∼6K) high-quality tomato images collected in a form of day-by-day degradation. The dataset is labelled in 2, 4, 6 and 10 classes using a structured manner. We have benchmarked the dataset using state-of-the-art image classification methods. We believe that the dataset is challenging and will attract computer vision researchers in the future. The dataset is available publicly (https://github.com/skarifahmed/FGrade).

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The Plant Pathology Challenge 2020 data set to classify foliar disease of apples

Cited by 128 publications

References 29 publications

Apple Leaf Disease Recognition and Sub-Class Categorization Based on Improved Multi-Scale Feature Fusion Network

Apple Leaf Disease Recognition and Sub-Class Categorization Based on Improved Multi-Scale Feature Fusion Network

An Improved CNN-Based Apple Appearance Quality Classification Method With Small Samples

FGrade: A Large Volume Dataset for Grading Tomato Freshness Quality

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