A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning

Xiong, Jiang; Yu, Dezheng; Liu, Shuangyin; Shu, Lei; Wang, Xiaochan; Zhao-ke, Liu

doi:10.3390/electronics10010081

Cited by 86 publications

(51 citation statements)

References 53 publications

(65 reference statements)

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“…With the hot development of deep learning, various deep learning models on image recognition have emerged, such as Alex-Net, VGG (Visual Geometry Group), and ResNet (Residual Network) [ 6 – 10 ]. Although these deep learning models avoid the artificial selection of important parts and artificial feature extraction and reduce the impact of artificial factors on the recognition effect, the design of these deep learning models is very dependent on the color and texture information of the picture, which is difficult to be directly used in the recognition of hand-painted sketches lacking color and texture information.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Research on Recognition Effect of DSCN Network Structure in Hand‐Drawn Sketch

2021

Computational Intelligence and Neuroscience

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With the rapid development of image recognition technology, freehand sketch recognition has attracted more and more attention. How to achieve good recognition effect in the absence of color and texture information is the key to the development of freehand sketch recognition. Traditional nonlearning classical models are highly dependent on manual selection features. To solve this problem, a neural network sketch recognition method based on DSCN structure is proposed in this paper. Firstly, the stroke sequence of the sketch is drawn; then, the feature is extracted according to the stroke sequence combined with neural network, and the extracted image features are used as the input of the model to construct the time relationship between different image features. Through the control experiment on TU-Berlin dataset, the results show that, compared with the traditional nonlearning methods, HOG-SVM, SIFT-Fisher Vector, MKL-SVM, and FV-SP, the recognition accuracy of DSCN network is improved by 15.8%, 10.3%, 6.0%, and 2.9%, respectively. Compared with the classical deep learning model, Alex-Net, the recognition accuracy is improved by 5.6%. The above results show that the DSCN network proposed in this paper has strong ability of feature extraction and nonlinear expression and can effectively improve the recognition accuracy of hand-painted sketches after introducing the stroke order.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Research on Recognition Effect of DSCN Network Structure in Hand‐Drawn Sketch

2021

Computational Intelligence and Neuroscience

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“…It is apparent from the literature review that most of the studies have targeted the problem of plant disease classification [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Nevertheless, models were considered as a black box, and it is difficult to trust a model that we can’t explain how it operates.…”

Section: Resultsmentioning

confidence: 99%

“…Many state-of-the-art methods already exist for plant disease classification and detection [ 23 , 24 , 25 , 26 , 27 ] and defect detection in general [ 28 , 29 , 30 ]. However, to the best of our knowledge, there are only a few studies on coffee disease detection and they focus only on classifying healthy and non-healthy leaves using transfer learning [ 31 ] or using an annotated bounding box for detection [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Coffee Disease Visualization and Classification

Yebasse

Shimelis²,

Warku

et al. 2021

Plants

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Deep learning architectures are widely used in state-of-the-art image classification tasks. Deep learning has enhanced the ability to automatically detect and classify plant diseases. However, in practice, disease classification problems are treated as black-box methods. Thus, it is difficult to trust the model that it truly identifies the region of the disease in the image; it may simply use unrelated surroundings for classification. Visualization techniques can help determine important areas for the model by highlighting the region responsible for the classification. In this study, we present a methodology for visualizing coffee diseases using different visualization approaches. Our goal is to visualize aspects of a coffee disease to obtain insight into what the model “sees” as it learns to classify healthy and non-healthy images. In addition, visualization helped us identify misclassifications and led us to propose a guided approach for coffee disease classification. The guided approach achieved a classification accuracy of 98% compared to the 77% of naïve approach on the Robusta coffee leaf image dataset. The visualization methods considered in this study were Grad-CAM, Grad-CAM++, and Score-CAM. We also provided a visual comparison of the visualization methods.

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“…Various artificial intelligence and image-based plant disease detection approaches have been proposed [10]. Many technological approaches have been proposed for the diagnosis of plant diseases [11] [12] [13] and the general diagnosis of the disease [14] [15] [16].…”

Section: Literature Surveymentioning

confidence: 99%

Cardamom Plant Disease Detection Approach Using EfficientNetV2

2022

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Cardamom is a queen of spices. It is indigenously grown in the evergreen forests of Karnataka, Kerala, Tamilnadu, and the northeastern states of India. India is the third largest producer of cardamom. Plant diseases cause a catastrophic influence on food production safety; they reduce the eminence and quantum of agricultural products. Plant diseases may cause significantly high loss or no harvest in dreadful cases. Various diseases and pests affect the growth of cardamom plants at different stages and crop yields. This study concentrated on two diseases of cardamom plants, Colletotrichum Blight and Phyllosticta Leaf Spot of cardamom and three diseases of grape, Black Rot, ESCA, and Isariopsis Leaf Spot. Various methods have been proposed for plant disease detection, and deep learning has become the preferred method because of its spectacular accomplishment. In this study, U 2 -Net was used to remove the unwanted background of an input image by selecting multiscale features. This work proposes a cardamom plant disease detection approach using the EfficientNetV2 model. A comprehensive set of experiments was carried out to ascertain the performance of the proposed approach and compare it with other models such as EfficientNet and Convolutional Neural Network (CNN). The experimental results showed that the proposed approach achieved a detection accuracy of 98.26%.

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A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning

Cited by 86 publications

References 53 publications

[Retracted] Research on Recognition Effect of DSCN Network Structure in Hand‐Drawn Sketch

[Retracted] Research on Recognition Effect of DSCN Network Structure in Hand‐Drawn Sketch

Coffee Disease Visualization and Classification

Cardamom Plant Disease Detection Approach Using EfficientNetV2

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