Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning

Wang, Guan; Sun, Yu; Wang, Jianxin

doi:10.1155/2017/2917536

Cited by 522 publications

(261 citation statements)

References 29 publications

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“…As a result, this method is used for the output of every convolutional layer. The ReLU activation function formula is shown in Equation (12).…”

Section: Relu Activation Functionmentioning

confidence: 99%

“…With the popularity of machine learning algorithms in computer vision, in order to improve the accuracy and rapidity of the diagnosis results, researchers have studied automated plant disease diagnosis based on traditional machine learning algorithms, such as random forest, k-nearest neighbor, and Support Vector Machine (SVM) [3][4][5][6][7][8][9][10][11][12]. However, because the classification features are selected and adopted based on human experience, these approaches improved the recognition accuracy, but the recognition rate is still not high enough and is vulnerable to artificial feature selection.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

et al. 2017

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Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases. Using a dataset of 13,689 images of diseased apple leaves, the proposed deep convolutional neural network model is trained to identify the four common apple leaf diseases. Under the hold-out test set, the experimental results show that the proposed disease identification approach based on the convolutional neural network achieves an overall accuracy of 97.62%, the model parameters are reduced by 51,206,928 compared with those in the standard AlexNet model, and the accuracy of the proposed model with generated pathological images obtains an improvement of 10.83%. This research indicates that the proposed deep learning model provides a better solution in disease control for apple leaf diseases with high accuracy and a faster convergence rate, and that the image generation technique proposed in this paper can enhance the robustness of the convolutional neural network model.

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“…As a result, this method is used for the output of every convolutional layer. The ReLU activation function formula is shown in Equation (12).…”

Section: Relu Activation Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

et al. 2017

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“…Johannes et al (2017) Although the problem of plant leaf disease has been addressed in several studies, few have focused on developing systems capable of estimating stress severity. Wang et al (2017) proposed the use of Convolutional Networks to estimate the severity of plant diseases. The images of apple leaves of the dataset affected by black rot were labeled in four degrees of severity.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for classification and severity estimation of coffee leaf biotic stress

Esgario

Krohling

Ventura

2020

Computers and Electronics in Agriculture

222

109

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Biotic stress consists of damage to plants through other living organisms. Efficient control of biotic agents such as pests and pathogens (viruses, fungi, bacteria, etc.) is closely related to the concept of agricultural sustainability. Agricultural sustainability promotes the development of new technologies that allow the reduction of environmental impacts, greater accessibility to farmers and, consequently, increase on productivity. The use of computer vision with deep learning methods allows the early and correct identification of the stress-causing agent. So, corrective measures can be applied as soon as possible to mitigate the problem. The objective of this work is to design an effective and practical system capable of identifying and estimating the stress severity caused by biotic agents on coffee leaves. The proposed approach consists of a multi-task system based on convolutional neural networks. In addition, we have explored the use of data augmentation techniques to make the system more robust and accurate. The experimental results obtained for classification as well as for severity estimation indicate that the proposed system might be a suitable tool to assist both experts and farmers in the identification and quantification of biotic stresses in coffee plantations.

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“…hyperspectral, chlorophyll fluorescence) in combination with machine learning methods (e.g. deep learning) which are already used in plant phenotyping (Barr et al, 2017;Pound et al, 2016) and expanding in phytopathology (Moghadam et al, 2017;Wang et al, 2017). Hence, our whole framework could benefit from a cascade detection (Zhou, 2012) of fruiting bodies with different steps involving different sensors and/or algorithms.…”

Section: Discussionmentioning

confidence: 99%

Automated image processing to support the analysis of between-year transmission of Leptosphaeria maculans in field conditions

Bousset

Palerme

Leclerc

et al. 2018

Preprint

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Understanding the transmission of inoculum between periods where the host plants are present is central for predicting the development of plant diseases and optimising mitigation strategies. However, the production at the end of the growing period, the survival during the intercrop period, and the emergence or emission of inoculum after sowing or planting can be highly variable, difficult to assess and generally inferred indirectly from symptoms data. As a result, there is a lack of large data sets which is a major brake for the study of these epidemiological processes. Here we focus on Leptosphaeria maculans that causes the black leg of oilseed rape. After having infected leaves, at early stages of the plant, and migrating into the stem, it causes a basal stem canker before harvest. It then survives on stubble left in the field from which ascospores are emitted at the beginning of the next growing period. In this study we first developed an image processing framework to estimate the density of fruiting bodies produced on stubble. Then, we used this framework to analyse automatically a large number of stems collected in oilseed rape fields among a cultivated area. Having performed a quality assessment of the processing chain we used the output data to investigate how the potential level of inoculum may change with the source field, the considered year and the stem canker severity at harvest. Besides the insights gain into the blackleg of oilseed rape, this work shows how image-based phenotyping may support epidemiological studies by increasing substantially the precision of high throughput disease data.

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Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning

Cited by 522 publications

References 29 publications

Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

Deep learning for classification and severity estimation of coffee leaf biotic stress

Automated image processing to support the analysis of between-year transmission of Leptosphaeria maculans in field conditions

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