Fused Deep Features-Based Grape Varieties Identification Using Support Vector Machine

Peng, Yun; Zhao, Shiwei; Liu, Jizhan

doi:10.3390/agriculture11090869

Cited by 18 publications

(21 citation statements)

References 34 publications

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“…Among them, CCA can generally grasp the correlation between the two sets of feature sets, and is often used for feature fusion between the two sets of feature sets 32 , 33 .Kiran et al 34 used CCA to fuse the global average pooling (GAP) layer of Resnet-50 and the deep features of fully connected layer(FCL) to achieve Human Action Recognition (HAR). Peng et al 35 used CCA to fuse the deep features of different networks to obtain more discriminative fusion features, thereby improving the recognition accuracy of grape species.…”

Section: Related Workmentioning

confidence: 99%

Research on lung nodule recognition algorithm based on deep feature fusion and MKL-SVM-IPSO

Zheng

Huang

et al. 2022

Sci Rep

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Lung CAD system can provide auxiliary third-party opinions for doctors, improve the accuracy of lung nodule recognition. The selection and fusion of nodule features and the advancement of recognition algorithms are crucial improving lung CAD systems. Based on the HDL model, this paper mainly focuses on the three key algorithms of feature extraction, feature fusion and nodule recognition of lung CAD system. First, CBAM is embedded into VGG16 and VGG19, and feature extraction models AE-VGG16 and AE-VGG19 are constructed, so that the network can pay more attention to the key feature information in nodule description. Then, feature dimensionality reduction based on PCA and feature fusion based on CCA are sequentially performed on the extracted depth features to obtain low-dimensional fusion features. Finally, the fusion features are input into the proposed MKL-SVM-IPSO model based on the improved Particle Swarm Optimization algorithm to speed up the training speed, get the global optimal parameter group. The public dataset LUNA16 was selected for the experiment. The results show that the accuracy of lung nodule recognition of the proposed lung CAD system can reach 99.56%, and the sensitivity and F1-score can reach 99.3% and 0.9965, respectively, which can reduce the possibility of false detection and missed detection of nodules.

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Section: Related Workmentioning

confidence: 99%

Research on lung nodule recognition algorithm based on deep feature fusion and MKL-SVM-IPSO

Zheng

Huang

et al. 2022

Sci Rep

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“…This test is based on testing each crop cluster by comparing it to another cluster that exhibits no crystallization using a special instrument that briefly directs light into the cluster to capture an electron micrograph of the leaves. Meanwhile, in [11], the authors digitized the Screen, a black tangent screen for measuring and classifying calcium oxalate crystals. Moreover, the approach reported in [12] was to measure calcium oxalate crystals using an electron micrograph-based 3D crop position measurement system.…”

Section: Computerized Approaches For the Detection Of Calcium Oxalate...mentioning

confidence: 99%

“…The CNNs used were VGG with 27 deep layers [10], Inception CNN with 79 deep layers [12], and NetV2 (Intel, New York, NY, USA) with 98 deep layers. All of the neural networks were trained with an average of one million electron micrographs with 800 object classes from the dataset Electron Micrograph Net [10][11][12][13][14]. Nevertheless, as the detection task of our research was very different from that of Electron Micrograph Net, we employed transfer learning of the abovementioned CNNs and tuned the networks' weights for the multi-class classification of calcium oxalate crystals.…”

Section: Datasetmentioning

confidence: 99%

“…Calcium oxalate crystals are a common crystallization requiring detection by a professional agronomist. It is also important to measure the degree of accumulation of calcium oxalate crystals [10][11][12][13], which can range from mild cases through the middle ranges to extreme cases. More than 4% of plants are known to suffer from calcium oxalate crystal conditions.…”

Section: Introduction 1calcium Oxalate Crystalsmentioning

confidence: 99%

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Computerized Detection of Calcium Oxalate Crystal Progression

Mahmoud

2022

Crystals

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Calcium oxalate crystals in plants can cause health issues such as kidney stones if ingested in large amounts. Calcium oxalate crystallizations affect approximately 4% of plants. Some of these crystallizations are more common, and human and animal ingestion can be avoided if the degree of severity is detected at an early stage. Therefore, in this paper, we present a computerized method for detecting calcium oxalate crystallizations at an early stage, when chances for avoiding it are higher. In our research, electron micrograph processing techniques are used to extract features and measure the degree of crystallization progression in cases of crystalized plants and normal plants. A new fast search algorithm—ODS: One Direction Search—is proposed to detect calcium oxalate crystal progression. The calcium oxalate crystal progression is detected on the basis of electron micrographs of calcium oxalate crystals by means of a temporal test. We employed deep learning for feature extraction. The deep learning technique uses transfer learning, which allows the proposed detection model to be trained on only a small amount of data regarding calcium oxalate crystals for the determination of the presence of calcium oxalate crystals and the severity of the cases. The experimental results, using electron micrographs of 6900 clusters, demonstrated a success rate of 97.5% when detecting cases of calcium oxalate crystals. The simulation results of the new temporal algorithm show an enhancement of the speed by 70% compared to well-known temporal algorithms, and increased accuracy when computing PRSN against other algorithms.

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“…Grapes are one of the most popular fruits in the world and also the main raw material for the production of wine, thus the yield and quality of grapes are of substantial economic value [1]. However, grape leaves are susceptible to various diseases that are influenced by the weather as well as the environment, and mainly caused by fungi, viruses, and bacteria.…”

Section: Introductionmentioning

confidence: 99%

GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

et al. 2022

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Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually, the differences in symptoms between early crop disease and late crop disease stages include the area of disease and color of disease. This also poses additional difficulties for CNN models. Here, we propose a lightweight CNN model called GrapeNet for the identification of different symptom stages for specific grape diseases. The main components of GrapeNet are residual blocks, residual feature fusion blocks (RFFBs), and convolution block attention modules. The residual blocks are used to deepen the network depth and extract rich features. To alleviate the CNN performance degradation associated with a large number of hidden layers, we designed an RFFB module based on the residual block. It fuses the average pooled feature map before the residual block input and the high-dimensional feature maps after the residual block output by a concatenation operation, thereby achieving feature fusion at different depths. In addition, the convolutional block attention module (CBAM) is introduced after each RFFB module to extract valid disease information. The obtained results show that the identification accuracy was determined as 82.99%, 84.01%, 82.74%, 84.77%, 80.96%, 82.74%, 80.96%, 83.76%, and 86.29% for GoogLeNet, Vgg16, ResNet34, DenseNet121, MobileNetV2, MobileNetV3_large, ShuffleNetV2_×1.0, EfficientNetV2_s, and GrapeNet. The GrapeNet model achieved the best classification performance when compared with other classical models. The total number of parameters of the GrapeNet model only included 2.15 million. Compared with DenseNet121, which has the highest accuracy among classical network models, the number of parameters of GrapeNet was reduced by 4.81 million, thereby reducing the training time of GrapeNet by about two times compared with that of DenseNet121. Moreover, the visualization results of Grad-cam indicate that the introduction of CBAM can emphasize disease information and suppress irrelevant information. The overall results suggest that the GrapeNet model is useful for the automatic identification of grape leaf diseases.

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Fused Deep Features-Based Grape Varieties Identification Using Support Vector Machine

Cited by 18 publications

References 34 publications

Research on lung nodule recognition algorithm based on deep feature fusion and MKL-SVM-IPSO

Research on lung nodule recognition algorithm based on deep feature fusion and MKL-SVM-IPSO

Computerized Detection of Calcium Oxalate Crystal Progression

GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

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