Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms

Özgüven, Mehmet Metin; Adem, Kemal

doi:10.1016/j.physa.2019.122537

Cited by 220 publications

(79 citation statements)

References 15 publications

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“…The impact of target dataset diversity and choosing a backbone model that is suitable for the target classes is much more important than the number of sample images in the dataset itself (as shown in Table 1 [ 111 , 113 , 114 ]).…”

Section: Discussionmentioning

confidence: 99%

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Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Hasan

Yusuf

Alzubaidi

2020

Plants

151

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Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

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Section: Discussionmentioning

confidence: 99%

“…Most of the recently presented models, including YOLO-v3 and R-CNN family architectures, are supported by the FPN model, which enables small object detection and enhances semantic segmentation and multi-object detection (as shown in Table 1 [ 107 , 108 , 110 , 111 , 113 , 114 , 117 , 118 ]).…”

Section: Discussionmentioning

confidence: 99%

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Hasan

Yusuf

Alzubaidi

2020

Plants

151

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“…However, in the context of this study, works on deep learning for the detection of plant diseases after images have been reviewed. An Updated Faster R-CNN architecture developed by changing the parameters of a CNN model and a Faster R-CNN architecture for automatic detection of leaf spot disease ( Cercospora beticola Sacc ) in sugar beet were proposed by Ozguven and Aden [ 3 ]. Their proposed method for the detection of disease severities by the imaging-based expert systems was trained and tested with 155 images, and according to the test results, the overall correct classification rate was found to be 95.48%.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Oppenheim et al [ 6 ] used deep learning for the detection of potato tuber disease. The basic architecture chosen for this problem was a CNN developed by the Visual Geometry Group (VGG) from the University of Oxford, U.K., named CNN-F due to its faster training time [ 3 ]. In their model, several new dropout layers were added to the VGG architecture to deal with problems of over fitting, especially due to the relatively small dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning

Sibiya

Sumbwanyambe

2021

Pathogens

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Many applications of plant pathology had been enabled by the evolution of artificial intelligence (AI). For instance, many researchers had used pre-trained convolutional neural networks (CNNs) such as the VGG-16, Inception, and Google Net to mention a few, for the classifications of plant diseases. The trend of using AI for plant disease classification has grown to such an extent that some researchers were able to use artificial intelligence to also detect their severities. The purpose of this study is to introduce a novel approach that is reliable in predicting severities of the maize common rust disease by CNN deep learning models. This was achieved by applying threshold-segmentation on images of diseased maize leaves (Common Rust disease) to extract the percentage of the diseased leaf area which was then used to derive fuzzy decision rules for the assignment of Common Rust images to their severity classes. The four severity classes were then used to train a VGG-16 network in order to automatically classify the test images of the Common Rust disease according to their classes of severity. Trained with images developed by using this proposed approach, the VGG-16 network achieved a validation accuracy of 95.63% and a testing accuracy of 89% when tested on images of the Common Rust disease among four classes of disease severity named Early stage, Middle stage, Late Stage and Healthy stage.

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“…(2019) identified 18 banana diseases and pests, Ghoury et al . (2019) differentiated healthy and diseased grape fruits and leaves, and Ozguven and Adem (2019) detected sugar beet leaf spot disease. The present study aimed to use the Faster R‐CNN model to localize and classify three leaf diseases and damage from four pests of tea in images acquired under field conditions.…”

Section: Introductionmentioning

confidence: 99%

Identification of tea foliar diseases and pest damage under practical field conditions using a convolutional neural network

2020

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Lesions of tea (Camellia sinensis) leaves are detrimental to the growth of tea crops. Their adverse effects include further disease of tea leaves and a direct reduction in yield and profit. Therefore, early detection and on‐site monitoring of tea leaf lesions are necessary for effective management to control infections and prevent further yield loss. In this study, 1,822 images of tea leaves with lesions caused by three diseases (brown blight, Colletotrichum camelliae; blister blight, Exobasidium vexans; and algal leaf spot, Cephaleuros virescens) and four pests (leaf miner, Tropicomyia theae; tea thrip, Scirtothrips dorsalis; tea leaf roller, Homona magnanima; and tea mosquito bug, Helopeltis fasciaticollis) were collected from northern and central Taiwan. A faster region‐based convolutional neural network (Faster R‐CNN) was then trained to detect the locations of the lesions on the leaves and to identify the causes of the lesions. The trained Faster R‐CNN detector achieved a precision of 77.5%, recall of 70.6%, an F1 score of 73.91%, and a mean average precision of 66.02%. An overall accuracy of 89.4% was obtained for identification of the seven classes of tea diseases and pests. The developed detector could assist tea farmers in identifying the causes of lesions in real time.

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Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms

Cited by 220 publications

References 15 publications

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning

Identification of tea foliar diseases and pest damage under practical field conditions using a convolutional neural network

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