Multi-Condition Training on Deep Convolutional Neural Networks for Robust Plant Diseases Detection

Yuwana, R. Sandra; Suryawati, Endang; Zilvan, Vicky; Ramdan, Ade; Pardede, Hilman F.; Fauziah, Fani

doi:10.1109/ic3ina48034.2019.8949580

Cited by 12 publications

(6 citation statements)

References 17 publications

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“…They evaluated the model on a tea disease dataset with 5632 images. The results showed that MCT improved the robustness of DCNN to some extent [59]. Still, another method is persistently enriching the diversity of datasets, for example through using different geographical locations and cultivation conditions.…”

Section: Nonideal Robustnessmentioning

confidence: 97%

“…We collected some public plant datasets from the two websites Kaggle (https://www.kaggle.com/datasets, accessed on 12 February 2021) and BIFROST (https://datasets.bifrost.ai/, accessed on 15 February 2021), which can be used for detection or classification tasks, as shown in Table 2. In the literature of DL techniques applied to plant disease classification, the most used public datasets are PlantVillage [53][54][55] and Kaggle [56]; notably, many authors also collect their own datasets [57][58][59][60]. For snake gourd leaf disease classification, we need a large number of leaf images of different disease categories.…”

Section: Data Preparation and Preprocessingmentioning

confidence: 99%

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Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification

2021

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Crop production can be greatly reduced due to various diseases, which seriously endangers food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional classification methods, such as naked-eye observation and laboratory tests, have many limitations, such as being time consuming and subjective. Currently, deep learning (DL) methods, especially those based on convolutional neural network (CNN), have gained widespread application in plant disease classification. They have solved or partially solved the problems of traditional classification methods and represent state-of-the-art technology in this field. In this work, we reviewed the latest CNN networks pertinent to plant leaf disease classification. We summarized DL principles involved in plant disease classification. Additionally, we summarized the main problems and corresponding solutions of CNN used for plant disease classification. Furthermore, we discussed the future development direction in plant disease classification.

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Section: Nonideal Robustnessmentioning

confidence: 97%

Section: Data Preparation and Preprocessingmentioning

confidence: 99%

Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification

2021

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“…The data are scaled into 256 × 256 and then rescaled down into 64 × 64 for the experiments. This dataset in an extension of dataset that we published in [28,60]. The sample images of this dataset is shown in Fig.…”

Section: Datasetmentioning

confidence: 99%

“…Most studies also have not evaluated the robustness of the methods when tested on non-ideal conditions where the image data may be blurred, have different orientations and/or resolutions than training data. To improve robustness, multicondition training on CNN for tea diseases detection is proposed in [60].…”

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confidence: 99%

Plant diseases detection with low resolution data using nested skip connections

et al. 2020

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Natural disasters, climate change[54] and plant diseases[51] are among many factors that threaten the food security. Plant diseases in particular, may cause great loss not only for farmers, but also for global economy. For instance, The International Potato Center (CIP) reports that around 15 % loss of potatoes production[24] is due to late blight diseases only. Globally, plant diseases cause more than 20 % crop loss annually[49]. The

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“…Convolutional neural networks (CNNs) [13][14][15] are the dominant deep learning architectures for image data. Studies have shown CNN is better than traditional machine learning methods for many fields of object recognition, such as face recognition [16,17], character recognition [4,18], vehicle number detection [19,20], vehicle surveillance in autonomous driving [21], medical imaging [22], identification of plant varieties [23][24][25][26], quality inspection of agricultural products [27], and detection of plant diseases [28][29][30][31][32][33][34][35][36][37]. This is due to their ability to…”

Section: Introductionmentioning

confidence: 99%

Unsupervised feature learning-based encoder and adversarial networks

et al. 2021

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In this paper, we propose a novel deep learning-based feature learning architecture for object classification. Conventionally, deep learning methods are trained with supervised learning for object classification. But, this would require large amount of training data. Currently there are increasing trends to employ unsupervised learning for deep learning. By doing so, dependency on the availability of large training data could be reduced. One implementation of unsupervised deep learning is for feature learning where the network is designed to “learn” features automatically from data to obtain good representation that then could be used for classification. Autoencoder and generative adversarial networks (GAN) are examples of unsupervised deep learning methods. For GAN however, the trajectories of feature learning may go to unpredicted directions due to random initialization, making it unsuitable for feature learning. To overcome this, a hybrid of encoder and deep convolutional generative adversarial network (DCGAN) architectures, a variant of GAN, are proposed. Encoder is put on top of the Generator networks of GAN to avoid random initialisation. We called our method as EGAN. The output of EGAN is used as features for two deep convolutional neural networks (DCNNs): AlexNet and DenseNet. We evaluate the proposed methods on three types of dataset and the results indicate that better performances are achieved by our proposed method compared to using autoencoder and GAN.

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Multi-Condition Training on Deep Convolutional Neural Networks for Robust Plant Diseases Detection

Cited by 12 publications

References 17 publications

Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification

Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification

Plant diseases detection with low resolution data using nested skip connections

Unsupervised feature learning-based encoder and adversarial networks

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