2022
DOI: 10.3390/agriengineering4040056
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A VGG-19 Model with Transfer Learning and Image Segmentation for Classification of Tomato Leaf Disease

Abstract: Tomato leaves can have different diseases which can affect harvest performance. Therefore, accurate classification for the early detection of disease for treatment is very important. This article proposes one classification model, in which 16,010 tomato leaf images obtained from the Plant Village database are segmented before being used to train a deep convolutional neural network (DCNN). This means that this classification model will reduce training time compared with that of the model without segmenting the … Show more

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Cited by 43 publications
(30 citation statements)
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References 49 publications
(63 reference statements)
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“…The original data dimension of the workload in the identification experiment is [9,2500,18]. There are 9 groups of different load values, and each group of load experiments produces 2500 × 18 data points.…”
Section: Parameter Analysis Of Prediction Modelmentioning
confidence: 99%
See 1 more Smart Citation
“…The original data dimension of the workload in the identification experiment is [9,2500,18]. There are 9 groups of different load values, and each group of load experiments produces 2500 × 18 data points.…”
Section: Parameter Analysis Of Prediction Modelmentioning
confidence: 99%
“…Deep learning has been widely used in many fields, such as medical analysis [7,8], image recognition [9], structural analysis [4], target optimization [10,11], and so on. Although the process of machine learning is analogous to the learning process of the human brain, the exploration of its learning mechanism and the generalization ability of the model for unknown data still need to be relied on large sample data [12][13][14].…”
Section: Introduction 1motivationsmentioning
confidence: 99%
“…Among the various deep learning models, convolutional neural networks (CNNs) have emerged as the most widely used and effective models for plant disease detection. CNN models, such as Resnet [1], VGG [22], and AlexNet [34], have demonstrated superior performance in accurately classifying plant diseases. These models have been studied and fine-tuned to achieve high accuracy in disease classification tasks.…”
Section: Introductionmentioning
confidence: 99%
“…Many deep learning-based methods have been proposed for crop disease identification [5][6][7][8][9][10][11][12][13][14][15][16][17][18]. For example, li et al [5] proposed the OplusVNet, a 13-layer convolutional neural network that achieved 99% prediction accuracy on a dataset collected from the field using VGG16 network modules for transfer learning.…”
Section: Introductionmentioning
confidence: 99%
“…For example, li et al [5] proposed the OplusVNet, a 13-layer convolutional neural network that achieved 99% prediction accuracy on a dataset collected from the field using VGG16 network modules for transfer learning. Nguyen et al [6] proposed a neural network model that combines image segmentation with transfer learning, segments the image and uses HSV to extract the original leaf area and black background, and feeds it into a VGG-19 model for transfer learning, achieving an accuracy of 99.72%, with a training time of 275000s. These networks have effectively improved the recognition of crop diseases and pests.…”
Section: Introductionmentioning
confidence: 99%