An Intelligent Technique for Grape Fanleaf Virus Detection

Mohammadpoor, Mojtaba; Nooghabi, Mohadese Gerami; Ahmedi, Zahra

doi:10.9781/ijimai.2020.02.001

Cited by 13 publications

(12 citation statements)

References 17 publications

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“…Finally, the extracted texture pattern was fed to a multiclass SVM. In (Mohammadpoor et al, 2020), Mohammadpoor et al proposed an intelligent technique for grape fanleaf virus detection. Based on Fuzzy C-mean algorithm, the area of diseased parts of each leaf was highlighted, and then it was classified using SVM.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, the extracted texture pattern was fed to a multiclass SVM. In ( Mohammadpoor et al., 2020 ), Mohammadpoor et al. proposed an intelligent technique for grape fanleaf virus detection.…”

Section: Related Workmentioning

confidence: 99%

“…However, the requirement of bulky sensors and precise instruments leads to low efficiency and high cost ( Mahlein et al., 2013 ; Lin et al., 2014 ). With the development of computer vision technique, researchers have proposed some plant disease recognition algorithms based on machine learning methods ( Waghmare et al., 2016 ; Ali et al., 2017 ; Hamuda et al., 2017 ; Akbarzadeh et al., 2018 ; Griffel et al., 2018 ; Sharif et al., 2018 ; Kaur et al., 2019 ; Khan et al., 2019 ; Kour and Arora, 2019 ; Liu et al., 2019 ; Wang et al., 2019 ; Zhu et al., 2019 ; Mohammadpoor et al., 2020 ). However, the classification features in these approaches are selected based on human experience, which limits the generalizability of the models and the accuracies of these models are still not satisfy the recognition requirement.…”

Section: Introductionmentioning

confidence: 99%

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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: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks

et al. 2020

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“…Afterward, Mohammadpoor et al (2020) proposed a Fuzzy C-Means and SVM-based hybridised technique to predict Grapevine Fanleaf Virus disease in grape plants. Their prediction system was 98.6% accurate.…”

Section: Related Workmentioning

confidence: 99%

Statistical analysis of machine learning techniques for predicting powdery mildew disease in tomato plants

Chug¹,

Bhatia²,

Singh³

2021

IJIEI

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Powdery mildew is a dangerous disease that reduces the quality and the yield of tomato fruit rapidly. Its early prediction is a prior requirement for obtaining good quality fruit. Therefore, in this study, the best classifier amongst various classifiers has been discovered using different machine learning algorithms. This classifier can precisely classify whether the meteorological conditions of a particular day are conducive to the development of powdery mildew disease or not. Tomato powdery mildew disease dataset has been tested using various performance measures and the results computed for all the classifiers are promising. Friedman test has been used to rank multiple classifiers and post hoc analysis has also been done using the Nemenyi test. It has been observed in comparison that 62.05% of the total pairs of classifiers perform significantly different from each other, and medium Gaussian support vector machine (MGSVM) is the best classifier with 94.74% accuracy.

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“…With the development of computer vision, machine learning techniques have been widely used in the agricultural field in recent years, and a series of approaches have been achieved in crop disease identification (Aravind et al, 2018 ; Kour and Arora, 2019 ; Mohammadpoor et al, 2020 ). In recent years, the main techniques, which are widely used in crop disease identification include artificial neural network (ANN) (Sheikhan et al, 2012 ), the K Nearest Neighbors (KNN) algorithm (Guettari et al, 2016 ), random forests (RF) (Kodovsky et al, 2012 ), and so on.…”

Section: Introductionmentioning

confidence: 99%

Identification of Apple Leaf Diseases by Improved Deep Convolutional Neural Networks With an Attention Mechanism

et al. 2021

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The accurate identification of apple leaf diseases is of great significance for controlling the spread of diseases and ensuring the healthy and stable development of the apple industry. In order to improve detection accuracy and efficiency, a deep learning model, which is called the Coordination Attention EfficientNet (CA-ENet), is proposed to identify different apple diseases. First, a coordinate attention block is integrated into the EfficientNet-B4 network, which embedded the spatial location information of the feature by channel attention to ensure that the model can learn both the channel and spatial location information of important features. Then, a depth-wise separable convolution is applied to the convolution module to reduce the number of parameters, and the h-swish activation function is introduced to achieve the fast and easy to quantify the process. Afterward, 5,170 images are collected in the field environment at the apple planting base of the Northwest A&F University, while 3,000 images are acquired from the PlantVillage public data set. Also, image augmentation techniques are used to generate an Apple Leaf Disease Identification Data set (ALDID), which contains 81,700 images. The experimental results show that the accuracy of the CA-ENet is 98.92% on the ALDID, and the average F1-score reaches .988, which is better than those of common models such as the ResNet-152, DenseNet-264, and ResNeXt-101. The generated test dataset is used to test the anti-interference ability of the model. The results show that the proposed method can achieve competitive performance on the apple disease identification task.

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An Intelligent Technique for Grape Fanleaf Virus Detection

Cited by 13 publications

References 17 publications

Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks

Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks

Statistical analysis of machine learning techniques for predicting powdery mildew disease in tomato plants

Identification of Apple Leaf Diseases by Improved Deep Convolutional Neural Networks With an Attention Mechanism

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