scholarly journals A Framework for Crop Disease Detection Using Feature Fusion Method

2021 ◽  
Vol 11 (3) ◽  
pp. 216-228
Author(s):  
Radhika Bhagwat ◽  
Yogesh Dandawate
Keyword(s):  
Feature Fusion ◽  
Detection Methods ◽  
Disease Detection ◽  
Learning Techniques ◽  
Proposed Model ◽  
Image Patches ◽  
Crop Disease ◽  
Rgb Images ◽  
Cepstral Coefficients ◽  
High Level

Crop disease detection methods vary from traditional machine learning, which uses Hand-Crafted Features (HCF) to the current deep learning techniques that utilize deep features. In this study, a hybrid framework is designed for crop disease detection using feature fusion. Convolutional Neural Network (CNN) is used for high level features that are fused with HCF. Cepstral coefficients of RGB images are presented as one of the features along with the other popular HCF. The proposed hybrid model is tested on the whole leaf images and also on the image patches which have individual lesions. The experimental results give an enhanced performance with a classification accuracy of 99.93% for the whole leaf images and 99.74% for the images with individual lesions. The proposed model also shows a significant improvement in comparison to the state-of-art techniques. The improved results show the prominence of feature fusion and establish cepstral coefficients as a pertinent feature for crop disease detection.

scholarly journals An Ensemble-Based Malware Detection Model Using Minimum Feature Set

MENDEL ◽  
2019 ◽  
Vol 25 (2) ◽  
pp. 1-10 ◽  
Author(s):  
Ivan Zelinka ◽  
Eslam Amer
Keyword(s):  
Machine Learning ◽  
False Positive Rate ◽  
Malware Detection ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Detection Model ◽  
Learning Techniques ◽  
Proposed Model ◽  
Positive Rate ◽  
Minimum Number

Current commercial antivirus detection engines still rely on signature-based methods. However, with the huge increase in the number of new malware, current detection methods become not suitable. In this paper, we introduce a malware detection model based on ensemble learning. The model is trained using the minimum number of signification features that are extracted from the file header. Evaluations show that the ensemble models slightly outperform individual classification models. Experimental evaluations show that our model can predict unseen malware with an accuracy rate of 0.998 and with a false positive rate of 0.002. The paper also includes a comparison between the performance of the proposed model and with different machine learning techniques. We are emphasizing the use of machine learning based approaches to replace conventional signature-based methods.

scholarly journals A Lightweight YOLOv4-Based Forestry Pest Detection Method Using Coordinate Attention and Feature Fusion

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1587
Author(s):  
Mingfeng Zha ◽  
Wenbin Qian ◽  
Wenlong Yi ◽  
Jing Hua
Keyword(s):  
Detection Method ◽  
Feature Fusion ◽  
State Of The Art ◽  
Detection Methods ◽  
Model Parameters ◽  
Symmetric Structure ◽  
Proposed Model ◽  
Pest Detection ◽  
Feature Information ◽  
Small Targets

Traditional pest detection methods are challenging to use in complex forestry environments due to their low accuracy and speed. To address this issue, this paper proposes the YOLOv4_MF model. The YOLOv4_MF model utilizes MobileNetv2 as the feature extraction block and replaces the traditional convolution with depth-wise separated convolution to reduce the model parameters. In addition, the coordinate attention mechanism was embedded in MobileNetv2 to enhance feature information. A symmetric structure consisting of a three-layer spatial pyramid pool is presented, and an improved feature fusion structure was designed to fuse the target information. For the loss function, focal loss was used instead of cross-entropy loss to enhance the network’s learning of small targets. The experimental results showed that the YOLOv4_MF model has 4.24% higher mAP, 4.37% higher precision, and 6.68% higher recall than the YOLOv4 model. The size of the proposed model was reduced to 1/6 of that of YOLOv4. Moreover, the proposed algorithm achieved 38.62% mAP with respect to some state-of-the-art algorithms on the COCO dataset.

scholarly journals Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques

2017 ◽  
Vol 10 (1) ◽  
pp. 199-208 ◽  
Author(s):  
Hsu-Yung Cheng ◽  
Chih-Lung Lin
Keyword(s):  
Supervised Learning ◽  
Detection Methods ◽  
Support Vector ◽  
Detection Accuracy ◽  
Cloud Detection ◽  
Multi Scale ◽  
Cloud Models ◽  
Learning Techniques ◽  
Image Patches ◽  
Local Image

Abstract. Cloud detection is important for providing necessary information such as cloud cover in many applications. Existing cloud detection methods include red-to-blue ratio thresholding and other classification-based techniques. In this paper, we propose to perform cloud detection using supervised learning techniques with multi-resolution features. One of the major contributions of this work is that the features are extracted from local image patches with different sizes to include local structure and multi-resolution information. The cloud models are learned through the training process. We consider classifiers including random forest, support vector machine, and Bayesian classifier. To take advantage of the clues provided by multiple classifiers and various levels of patch sizes, we employ a voting scheme to combine the results to further increase the detection accuracy. In the experiments, we have shown that the proposed method can distinguish cloud and non-cloud pixels more accurately compared with existing works.

scholarly journals Detection and Classification of Paddy Crop Disease using Deep Learning Techniques

2019 ◽  
Vol 8 (3) ◽  
pp. 4353-4359
Keyword(s):  
Crop Production ◽  
Input Image ◽  
Vital Role ◽  
Disease Detection ◽  
Efficient Detection ◽  
Learning Techniques ◽  
Crop Disease ◽  
Time Basis ◽  
Occurrence Matrix ◽  
Paddy Crop

Agricultural production plays a vital role in Indian economy. The biggest menace for a farmer is the various diseases that infect the crop. Quality and high production of crops is involved with factors like efficient detection of diseases in the crop. The disease detection though Naked-eye observation of expert can be prohibitively expensive and requires meticulous and scrupulous analysis to detect the disease. The existing systems on disease detection is not efficient enough in terms on real time basis. This paper presents an effective method for identification of paddy leaf disease. The proposed approaches involves pre-processing of input image and the paddy plant disease type is recognized using Gray-Level Co-occurrence Matrix (GLCM) technique and classifiers namely Artificial Neural Networks is used for better accuracy of detection. This method will be very useful to farmers to detect paddy diseases beforehand and thus prevent over usage of pesticides which in turn affects the crop production

scholarly journals MFF-Net: Deepfake Detection Network Based on Multi-Feature Fusion

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1692
Author(s):  
Lei Zhao ◽  
Mingcheng Zhang ◽  
Hongwei Ding ◽  
Xiaohui Cui
Keyword(s):  
Feature Extraction ◽  
Semantic Information ◽  
Feature Fusion ◽  
State Of The Art ◽  
Detection Methods ◽  
Textural Features ◽  
Detection Technology ◽  
Rgb Images ◽  
Signal Processing Methods ◽  
Made In

Significant progress has been made in generating counterfeit images and videos. Forged videos generated by deepfaking have been widely spread and have caused severe societal impacts, which stir up public concern about automatic deepfake detection technology. Recently, many deepfake detection methods based on forged features have been proposed. Among the popular forged features, textural features are widely used. However, most of the current texture-based detection methods extract textures directly from RGB images, ignoring the mature spectral analysis methods. Therefore, this research proposes a deepfake detection network fusing RGB features and textural information extracted by neural networks and signal processing methods, namely, MFF-Net. Specifically, it consists of four key components: (1) a feature extraction module to further extract textural and frequency information using the Gabor convolution and residual attention blocks; (2) a texture enhancement module to zoom into the subtle textural features in shallow layers; (3) an attention module to force the classifier to focus on the forged part; (4) two instances of feature fusion to firstly fuse textural features from the shallow RGB branch and feature extraction module and then to fuse the textural features and semantic information. Moreover, we further introduce a new diversity loss to force the feature extraction module to learn features of different scales and directions. The experimental results show that MFF-Net has excellent generalization and has achieved state-of-the-art performance on various deepfake datasets.

scholarly journals A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images

2019 ◽  
Vol 11 (13) ◽  
pp. 1554 ◽  
Author(s):  
Xin Zhang ◽  
Liangxiu Han ◽  
Yingying Dong ◽  
Yue Shi ◽  
Wenjiang Huang ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Spatial Information ◽  
Yellow Rust ◽  
Hyperspectral Images ◽  
Disease Spread ◽  
Spectral Information ◽  
Disease Detection ◽  
Proposed Model ◽  
Crop Disease

Yellow rust in winter wheat is a widespread and serious fungal disease, resulting in significant yield losses globally. Effective monitoring and accurate detection of yellow rust are crucial to ensure stable and reliable wheat production and food security. The existing standard methods often rely on manual inspection of disease symptoms in a small crop area by agronomists or trained surveyors. This is costly, time consuming and prone to error due to the subjectivity of surveyors. Recent advances in unmanned aerial vehicles (UAVs) mounted with hyperspectral image sensors have the potential to address these issues with low cost and high efficiency. This work proposed a new deep convolutional neural network (DCNN) based approach for automated crop disease detection using very high spatial resolution hyperspectral images captured with UAVs. The proposed model introduced multiple Inception-Resnet layers for feature extraction and was optimized to establish the most suitable depth and width of the network. Benefiting from the ability of convolution layers to handle three-dimensional data, the model used both spatial and spectral information for yellow rust detection. The model was calibrated with hyperspectral imagery collected by UAVs in five different dates across a whole crop cycle over a well-controlled field experiment with healthy and rust infected wheat plots. Its performance was compared across sampling dates and with random forest, a representative of traditional classification methods in which only spectral information was used. It was found that the method has high performance across all the growing cycle, particularly at late stages of the disease spread. The overall accuracy of the proposed model (0.85) was higher than that of the random forest classifier (0.77). These results showed that combining both spectral and spatial information is a suitable approach to improving the accuracy of crop disease detection with high resolution UAV hyperspectral images.

Application of Machine Learning Techniques in Crop Disease Classification: A Comprehensive Review

2021 ◽  
Vol 9 (1) ◽  
pp. 89-93
Author(s):  
Khwairakpam Amitab ◽  
◽  
Lal Hmingliana ◽  
Amitabha Nath ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Systems ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Disease Detection ◽  
Comprehensive Review ◽  
Road Map ◽  
Learning Techniques ◽  
Crop Disease

Crop diseases are the main threat to agricultural products. Fast, accurate, and automatic detection of diseases can help to overcome this problem. Literature suggests, machine learning techniques are capable of achieving these goals in near real-time. This article presents a comprehensive review of machine learning techniques for crop disease detection and classification. Existing plant disease classification systems are explored and commonly used processing steps are investigated. Analysis of machine learning techniques, accuracy factor, and current state-of-the-art in this domain have been reviewed and presented through this manuscript. The survey resulted in the identification of the strengths and limitations of existing techniques and provides a road map for future research works. These would help researchers to understand and pursue machine learning applications in the field of disease detection and classification

scholarly journals A BERT-based Hybrid Short Text Classification Model Incorporating CNN and Attention-based BiGRU

2021 ◽  
Vol 33 (6) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Text Classification ◽  
Feature Fusion ◽  
Feature Representation ◽  
Classification Model ◽  
Short Text ◽  
Learning Techniques ◽  
Proposed Model ◽  
Fusion Framework

Short text classification is a research focus for natural language processing (NLP), which is widely used in news classification, sentiment analysis, mail filtering and other fields. In recent years, deep learning techniques are applied to text classification and has made some progress. Different from ordinary text classification, short text has the problem of less vocabulary and feature sparsity, which raise higher request for text semantic feature representation. To address this issue, this paper propose a feature fusion framework based on the Bidirectional Encoder Representations from Transformers (BERT). In this hybrid method, BERT is used to train word vector representation. Convolutional neural network (CNN) capture static features. As a supplement, a bi-gated recurrent neural network (BiGRU) is adopted to capture contextual features. Furthermore, an attention mechanism is introduced to assign the weight of salient words. The experimental results confirmed that the proposed model significantly outperforms the other state-of-the-art baseline methods.

scholarly journals An Enhanced Plant Disease Classifier Model Based on Deep Learning Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 7159-7164 ◽  
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Low Cost ◽  
Disease Diagnosis ◽  
Plant Diseases ◽  
Disease Detection ◽  
Crop Species ◽  
Learning Techniques ◽  
Proposed Model ◽  
Significant Performance

Plant disease detection is used to detect and identify symptoms of plant diseases. Detection of plant diseases through the naked eye is ineffective, especially because there are numerous diseases. Therefore, there is a need to develop low-cost methods to improve rapidity and accuracy of plant disease diagnosis. This paper presents an effective model for plant disease detection by using our developed deep learning approach. Extensive experiments were performed on the PlantVillage dataset, which contains 54,306 images categorized between 38 different classes containing 14 crop species and 26 diseases. Our proposed model demonstrated significant performance improvement in terms of accuracy, recall, precision, and F1-score compared with the existing model used for plant disease detection.

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