Deep neural network features fusion and selection based on PLS regression with an application for crops diseases classification

Saeed, Farah; Khan, Muhammad Attique; Sharif, Muhammad; Mittal, Mamta; Goyal, Lalit Mohan; Roy, Sudipta

doi:10.1016/j.asoc.2021.107164

Cited by 89 publications

(34 citation statements)

References 38 publications

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“…Three different image datasets were used for the experimental process and achieved an accuracy of 96.5%. Saeed et al [15] presented a deep learning and PLS based features fusion framework for plant diseases recognition. Features are extracted from the two fully connected layers such as FC6 and FC7 of the VGG19 deep model, later fused using the PLS approach.…”

Section: Related Workmentioning

confidence: 99%

“…A CNN model is a more powerful form of deep learning which performed better for high dimensional datasets. A simple CNN model includes several hidden layers such as a convolutional layer, pooling layer, activation layer, normalization layer, fully connected layer, and output layer [15]. Many pre-trained deep models are publically available for feature extraction, such as GoogleNet [16], AlexNet, and ResNet.…”

Section: Introductionmentioning

confidence: 99%

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Citrus Diseases Recognition Using Deep Improved Genetic Algorithm

Yasmeen¹,

Khan²,

Tariq³

et al. 2022

Computers, Materials &Amp; Continua

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Agriculture is the backbone of each country, and almost 50% of the population is directly involved in farming. In Pakistan, several kinds of fruits are produced and exported the other countries. Citrus is an important fruit, and its production in Pakistan is higher than the other fruits. However, the diseases of citrus fruits such as canker, citrus scab, blight, and a few more impact the quality and quantity of this Fruit. The manual diagnosis of these diseases required an expert person who is always a time-consuming and costly procedure. In the agriculture sector, deep learning showing significant success in the last five years. This research work proposes an automated framework using deep learning and best feature selection for citrus diseases classification. In the proposed framework, the augmentation technique is applied initially by creating more training data from existing samples. They were then modifying the two pre-trained models named Resnet18 and Inception V3. The modified models are trained using an augmented dataset through transfer learning. Features are extracted for each model, which is further selected using Improved Genetic Algorithm (ImGA). The selected features of both models are fused using an array-based approach that is finally classified using supervised learning classifiers such as Support Vector Machine (SVM) and name a few more. The experimental process is conducted on three different datasets-Citrus Hybrid, Citrus Leaf, and Citrus Fruits. On these datasets, the best-achieved accuracy is 99.5%, 94%, and 97.7%, respectively. The proposed framework is evaluated on each step and compared with some recent techniques, showing that the proposed method shows improved performance.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Citrus Diseases Recognition Using Deep Improved Genetic Algorithm

Yasmeen¹,

Khan²,

Tariq³

et al. 2022

Computers, Materials &Amp; Continua

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“…Dubey et al [9]used K-means clustering to extract the region of interest, then segmented the disease according to color, texture and shape, combined different features, and finally used SVM to classify, the accuracy was 95.94%. Farah Saeed et.al [10] used pretained VGG19 to extract deep features, and combined the features from the fully connected layers 6 and 7 with a PLS-based parallel fusion method. Then, the best features were selected by PLS project method and classified by the ensemble baggage tree.…”

Section: Introductionmentioning

confidence: 99%

Apple Leaf Disease Recognition and Sub-Class Categorization Based on Improved Multi-Scale Feature Fusion Network

et al. 2021

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Apple diseases cause a lot of economic losses to fruit growers in China. Early diagnosis and accurate recognition of apple diseases can control the spread of disease and reduce production costs. However, the significance of disease characteristic of apple leaves in complex environment is relatively weak, and the fine-grain among different diseases of apple leaves is high, and the conventional feature extraction methods will lose the discrimination information. To solve these problems, an apple disease classification model based on multi-scale feature fusion is proposed in this paper. Firstly, the information flow of conventional residual network (ResNet) was improved to achieve efficient information circulation through changing the position of batch normalization and rectified linear unit (ReLU). Secondly, in order to solve the problem of serious loss of information in ResNet downsample, the channel projection and spatial projection of downsample were separated. Lastly, the 3×3 conv in ResBlocks was replaced by pyramid convolution, and the dilated convolution with different dilation rate was introduced into pyramid convolution to enhance the output scale of feature maps and improve the robustness of the model. The optimized model was verified on the dataset of this paper, and the optimized model had stronger anti-noise ability and better robustness, excellent learning effect and fast convergence speed. The classification accuracy on the original dataset is 94.24%, and that on the preprocessed dataset is 94.99%. The results demonstrate that the optimal model has a high accuracy, which can provide a reference for the prevention and control of apple leaf diseases.INDEX TERMS Apple disease, multi-scale feature fusion, deep learning, ResNet, classification.

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“…Hence, there is a need for efficient and robust techniques to identify mango diseases automatically, accurately, and efficiently [4,[13][14][15]. For this purpose, the images used as a baseline can be captured by digital and mobile cameras [16,17].…”

Section: Introductionmentioning

confidence: 99%

Mango Leaf Disease Recognition and Classification Using Novel Segmentation and Vein Pattern Technique

et al. 2021

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Mango fruit is in high demand. So, the timely control of mango plant diseases is necessary to gain high returns. Automated recognition of mango plant leaf diseases is still a challenge as manual disease detection is not a feasible choice in this computerized era due to its high cost and the non-availability of mango experts and the variations in the symptoms. Amongst all the challenges, the segmentation of diseased parts is a big issue, being the pre-requisite for correct recognition and identification. For this purpose, a novel segmentation approach is proposed in this study to segment the diseased part by considering the vein pattern of the leaf. This leaf vein-seg approach segments the vein pattern of the leaf. Afterward, features are extracted and fused using canonical correlation analysis (CCA)-based fusion. As a final identification step, a cubic support vector machine (SVM) is implemented to validate the results. The highest accuracy achieved by this proposed model is 95.5%, which proves that the proposed model is very helpful to mango plant growers for the timely recognition and identification of diseases.

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Deep neural network features fusion and selection based on PLS regression with an application for crops diseases classification

Cited by 89 publications

References 38 publications

Citrus Diseases Recognition Using Deep Improved Genetic Algorithm

Citrus Diseases Recognition Using Deep Improved Genetic Algorithm

Apple Leaf Disease Recognition and Sub-Class Categorization Based on Improved Multi-Scale Feature Fusion Network

Mango Leaf Disease Recognition and Classification Using Novel Segmentation and Vein Pattern Technique

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