Using Deep Learning for Image-Based Potato Tuber Disease Detection

Oppenheim, D.; Shani, Guy; Erlich, Orly; Tsror, Leah

doi:10.1094/phyto-08-18-0288-r

Cited by 111 publications

(44 citation statements)

References 20 publications

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“…Thus, using CNNs to identify early plant diseases has become a research focus of agricultural informatization. In ( Mohanty et al., 2016 ; Zhang and Wang, 2016 ; Lu J. et al., 2017 ; Lu Y. et al., 2017 ; Khan et al., 2018 ; Liu et al., 2018 ; Geetharamani and Pandian, 2019 ; Ji et al., 2019 ; Jiang et al., 2019 ; Liang et al., 2019 ; Oppenheim et al., 2019 ; Pu et al., 2019 ; Ramcharan et al., 2019 ; Wagh et al., 2019 ; Zhang et al., 2019a ; Zhang et al., 2019b ; ), CNNs are extensively studied and applied to the diagnosis of plant diseases. According to these studies, CNNs can learn advanced robust features of diseases directly from original images rather than selecting or extracting features manually, which outperform the traditional feature extraction approaches.…”

Section: Introductionmentioning

confidence: 99%

Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks

et al. 2020

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Anthracnose, brown spot, mites, black rot, downy mildew, and leaf blight are six common grape leaf pests and diseases, which cause severe economic losses to the grape industry. Timely diagnosis and accurate identification of grape leaf diseases are decisive for controlling the spread of disease and ensuring the healthy development of the grape industry. This paper proposes a novel recognition approach that is based on improved convolutional neural networks for the diagnoses of grape leaf diseases. First, based on 4,023 images collected in the field and 3,646 images collected from public data sets, a data set of 107,366 grape leaf images is generated via image enhancement techniques. Afterward, Inception structure is applied for strengthening the performance of multidimensional feature extraction. In addition, a dense connectivity strategy is introduced to encourage feature reuse and strengthen feature propagation. Ultimately, a novel CNNbased model, namely, DICNN, is built and trained from scratch. It realizes an overall accuracy of 97.22% under the hold-out test set. Compared to GoogLeNet and ResNet-34, the recognition accuracy increases by 2.97% and 2.55%, respectively. The experimental results demonstrate that the proposed model can efficiently recognize grape leaf diseases. Meanwhile, this study explores a new approach for the rapid and accurate diagnosis of plant diseases that establishes a theoretical foundation for the application of deep learning in the field of agricultural information.

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Section: Introductionmentioning

confidence: 99%

Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks

et al. 2020

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“…Rather than manually selecting features to feed traditional machine learning classification methods, CNNs provide endto-end pipelines to automatically extract advanced robust features and thus significantly improve the usability of plant leaf identification. In [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16], various kinds of CNN-based models are applied in the plant leaf disease recognition field which demonstrates deep-learning-based models have become the prevailing methods. However, sufficient training images are an important requirement in high generalization capability of CNN-based models.…”

Section: Introductionmentioning

confidence: 99%

A Data Augmentation Method Based on Generative Adversarial Networks for Grape Leaf Disease Identification

et al. 2020

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The identification of grape leaf diseases based on deep learning is critical to controlling the spread of diseases and ensuring the healthy development of the grape industry. Focusing on the lack of training images of grape leaf diseases, this paper proposes a novel model named Leaf GAN, which is based on generative adversarial networks (GANs), to generate images of four different grape leaf diseases for training identification models. A generator model with degressive channels is first designed to generate grape leaf disease images; then, the dense connectivity strategy and instance normalization are fused into an efficient discriminator to identify real and fake disease images by utilizing their excellent feature extraction capability on grape leaf lesions. Finally, the deep regret gradient penalty method is applied to stabilize the training process of the model. Using a total of 4,062 grape leaf disease images, the Leaf GAN model ultimately generates 8,124 grape leaf disease images. The generated grape leaf disease images based on Leaf GAN model can obtain better performance than DCGAN and WGAN in terms of the Fréchet inception distance. The experimental results show that the proposed Leaf GAN model generates sufficient grape leaf disease images with prominent lesions, providing a feasible solution for the data augmentation of grape leaf disease images. For the eight prevailing classification models with the expanded dataset, the identification performance based on CNNs indicated higher accuracies, whereby all the accuracies were better than those of the initial dataset with other data augmentation methods. Among them, Xception achieves a recognition accuracy of 98.70% on the testing set. The results demonstrate that the proposed data augmentation method represents a new approach to overcoming the overfitting problem in disease identification and can effectively improve the identification accuracy.

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“…For the forecast problem, a recurrent type of neural network was used. Neural network training was based on data obtained experimentally in [7]. The size of the training sample was taken in the amount of 20,000 photos.…”

Section: Fig 1 Type Of Genetic Algorithmmentioning

confidence: 99%

The use of machine learning methods in the diagnosis of diseases of crops

et al. 2020

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The approach to solving the problems of diagnosis and prognosis of diseases of agricultural crops using machine learning methods is described. To solve the problem of forecasting diseases of agricultural crops, it is proposed to use a genetic algorithm in the work. The analysis of the effectiveness of the proposed method is carried out depending on the convergence rate of such parameters as the mutation coefficient and population size. To solve the problem of diagnostics of agricultural crops, it is proposed to use a recurrent type of neural network. A software modelling complex has been developed that allows solving the problems of plant diseases diagnostics and making forecasts. The results obtained can reduce the costs of agricultural enterprises by reducing the cost of diagnosing agricultural diseases.

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Using Deep Learning for Image-Based Potato Tuber Disease Detection

Cited by 111 publications

References 20 publications

Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks

Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks

A Data Augmentation Method Based on Generative Adversarial Networks for Grape Leaf Disease Identification

The use of machine learning methods in the diagnosis of diseases of crops

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