Using Deep Learning for Image-Based Different Degrees of Ginkgo Leaf Disease Classification

Li, Kaizhou; Lin, Jianhui; Liu, Jinrong; Zhao, Yandong

doi:10.3390/info11020095

Cited by 43 publications

(21 citation statements)

References 25 publications

(21 reference statements)

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“…Data augmentation involves the process of creating new data points by manipulating the original data. This process increases the number of training images in DL without the need to add new photos [5,6], in this work the augmentation is done by: -Random reflection in the left-right direction.…”

Section: Materials and Methods 21 Datasetmentioning

confidence: 99%

“…Today, DL is becoming one of the most relevant identification techniques. Convolution neural network (CNN) is DL's basic method, it increases accuracy by programming a large amount of data for extracting features and multiple hidden layers using an ML model [5]. In [6] Krizhevsky implemented a deep CNN to identify 1.2 million images with ImageNet and for the first time achieved the top-1 and top-5 error rate in the Image Recognition Competition, after which the researchers caught the interest of this field.…”

Section: Introductionmentioning

confidence: 99%

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Detection of citrus leaf diseases using a deep learning technique

Luaibi

Salman

Miry

2021

IJECE

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The food security major threats are the diseases affected in plants such as citrus so that the identification in an earlier time is very important. Convenient malady recognition can assist the client with responding immediately and sketch for some guarded activities. This recognition can be completed without a human by utilizing plant leaf pictures. There are many methods employed for the classification and detection in machine learning (ML) models, but the combination of increasing advances in computer vision appears the deep learning (DL) area research to achieve a great potential in terms of increasing accuracy. In this paper, two ways of conventional neural networks are used named Alex Net and Res Net models with and without data augmentation involves the process of creating new data points by manipulating the original data. This process increases the number of training images in DL without the need to add new photos, it will appropriate in the case of small datasets. A self-dataset of 200 images of diseases and healthy citrus leaves are collected. The trained models with data augmentation give the best results with 95.83% and 97.92% for Res Net and Alex Net respectively.

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Section: Materials and Methods 21 Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of citrus leaf diseases using a deep learning technique

Luaibi

Salman

Miry

2021

IJECE

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“…Li et al [62] used VGG16 and Inception V3 models to identify different degrees of Ginkgo biloba diseases, the accuracy of the VGG16 was 98.44% in the laboratory dataset and 92.19% in the field dataset. The accuracy of the Inception V3 model was 92.3% and 93.2%, respectively.…”

Section: A Leaf Disease Detection By Well-known Deep Learning Architectures 1) Classic Deep Learning Architectures For Leaf-disease Detecmentioning

confidence: 99%

Plant Disease Detection and Classification by Deep Learning—A Review

Zhang

2021

IEEE Access

432

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Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this paper, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this work will be a valuable resource for researchers who study the detection of plant diseases and insect pests. At the same time, we also discussed some of the current challenges and problems that need to be resolved. INDEX TERMSDeep learning, plant leaf disease detection, visualization, small sample, hyperspectral imaging.

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“…Finally, 91.83% validation accuracy is achieved during disease classification. Li et al [12] proposed Convolutional Neural Network (CNN) for Ginkgo leaf disease detection. They combined and used Inception V3 and VGGNet-16 models.…”

Section: Related Workmentioning

confidence: 99%

Soybean Plant Disease Classification using Archimedes Optimization Algorithm based Hybrid Deep Learning Model

Annrose

Rufus

Rex

et al. 2021

Preprint

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Bean which is botanically called Phaseolus vulgaris L belongs to the Fabaceae family.During bean disease identification, unnecessary economical losses occur due to the delay of the treatment period, incorrect treatment, and lack of knowledge. The existing deep learning and machine learning techniques met few issues such as high computational complexity, higher cost associated with the training data, more execution time, noise, feature dimensionality, lower accuracy, low speed, etc. To tackle these problems, we have proposed a hybrid deep learning model with an Archimedes optimization algorithm (HDL-AOA) for bean disease classification. In this work, there are five bean classes of which one is a healthy class whereas the remaining four classes indicate different diseases such as Bean halo blight, Pythium diseases, Rhizoctonia root rot, and Anthracnose abnormalities acquired from the Soybean (Large) Data Set.The hybrid deep learning technique is the combination of wavelet packet decomposition (WPD) and long short term memory (LSTM). Initially, the WPD decomposes the input images into four sub-series. For these sub-series, four LSTM networks were developed. During bean disease classification, an Archimedes optimization algorithm (AOA) enhances the classification accuracy for multiple single LSTM networks. MATLAB software implements the HDL-AOA model for bean disease classification. The proposed model accomplishes lower MAPE than other exiting methods. Finally, the proposed HDL-AOA model outperforms excellent classification results using different evaluation measures such as accuracy, specificity, sensitivity, precision, recall, and F-score.

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Using Deep Learning for Image-Based Different Degrees of Ginkgo Leaf Disease Classification

Cited by 43 publications

References 25 publications

Detection of citrus leaf diseases using a deep learning technique

Detection of citrus leaf diseases using a deep learning technique

Plant Disease Detection and Classification by Deep Learning—A Review

Soybean Plant Disease Classification using Archimedes Optimization Algorithm based Hybrid Deep Learning Model

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