Detection of Weed Using Visual Attention Model and SVM Classifier

Aparna, M.; Radha, D.

doi:10.1007/978-3-030-00665-5_25

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…SVM maps training examples to points in space to maximize the margin between classes. When the samples are not linearly separable, a kernel function can be used to map the samples in a low-dimensional space to a high-dimensional space for classification [ 41 ].…”

Section: Resultsmentioning

confidence: 99%

Multi-Level Attention Recognition of EEG Based on Feature Selection

Zhang

et al. 2023

IJERPH

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In view of the fact that current attention-recognition studies are mostly single-level-based, this paper proposes a multi-level attention-recognition method based on feature selection. Four experimental scenarios are designed to induce high, medium, low, and non-externally directed attention states. A total of 10 features are extracted from 10 electroencephalogram (EEG) channels, respectively, including time-domain measurements, sample entropy, and frequency band energy ratios. Based on all extracted features, an 88.7% recognition accuracy is achieved when classifying the four different attention states using the support vector machine (SVM) classifier. Afterwards, the sequence-forward-selection method is employed to select the optimal feature subset with high discriminating power from the original feature set. Experimental results show that the classification accuracy can be improved to 94.1% using the filtered feature subsets. In addition, the average recognition accuracy based on single subject classification is improved from 90.03% to 92.00%. The promising results indicate the effectiveness of feature selection in improving the performance of multi-level attention-recognition tasks.

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Section: Resultsmentioning

confidence: 99%

Multi-Level Attention Recognition of EEG Based on Feature Selection

Zhang

et al. 2023

IJERPH

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“…The principal component analysis (PCA) was also used as a necessary step for reducing the dimensions of input image. Multilayer Perceptron (MP) neural network as classifier was used in this work which resulted an overall classification accuracy of 89.3% In [5], the categorization type visual saliency model was used to address classification issues with different leaves shapes of crop and weed. Visual attention categorization model along with SVM was used for the classification of broadleaf crop and narrow leaves weed.…”

Section: A Related Work 1) Supervised Learningmentioning

confidence: 99%

Saliency-Based Semantic Weeds Detection and Classification Using UAV Multispectral Imaging

et al. 2023

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Weeds infestation causes damage to crops and limits the agricultural production. The traditional weeds controlling methods rely on agrochemicals which demand labour-intensive practices. Various methods are proposed for the pursuit of weeds detection using multispectral images. The machine vision-based weeds detection methods require the extraction of a large number of multispectral texture features which in turn increases the computational cost. Deep neural networks are used for pixel-based weeds classification, but a drawback of these deep neural network-based weeds detection methods is that they require a large size of images dataset for network training which is time-consuming and expensive to collect particularly for multispectral images. These methods also require a Graphics Processing Unit (GPU) based system because of having high computational cost. In this article, we propose a novel weeds detection model which addresses these issues, as it does not require any kind of supervised training using labelled images and multispectral texture features extraction. The proposed model can execute on a Central Processing Unit (CPU) based system as a result its computational cost reduces. The Predictive Coding/Biased Competition-Divisive Input Modulation (PC/BC-DIM) neural network is used to determine multispectral fused saliency map which is further used to predict salient crops and detect weeds. The proposed model has achieved 94.38% mean accuracy, 0.086 mean square error, and 0.291 root mean square error.

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“…The Support Vector Machine (SVM) is widely used in machine learning models [24] and is a supervised learning algorithm for classification and regression analysis. It performed well in various classification applications [25][26][27][28][29]. Medical diagnosis is an essential application for the SVM classifier because it is crucial in diagnosing specific disorders.…”

Section: Introductionmentioning

confidence: 99%

Hybrid whale algorithm with evolutionary strategies and filtering for high-dimensional optimization: Application to microarray cancer data

Hafiz,

Saeed

2024

PLoS ONE

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The standard whale algorithm is prone to suboptimal results and inefficiencies in high-dimensional search spaces. Therefore, examining the whale optimization algorithm components is critical. The computer-generated initial populations often exhibit an uneven distribution in the solution space, leading to low diversity. We propose a fusion of this algorithm with a discrete recombinant evolutionary strategy to enhance initialization diversity. We conduct simulation experiments and compare the proposed algorithm with the original WOA on thirteen benchmark test functions. Simulation experiments on unimodal or multimodal benchmarks verified the better performance of the proposed RESHWOA, such as accuracy, minimum mean, and low standard deviation rate. Furthermore, we performed two data reduction techniques, Bhattacharya distance and signal-to-noise ratio. Support Vector Machine (SVM) excels in dealing with high-dimensional datasets and numerical features. When users optimize the parameters, they can significantly improve the SVM’s performance, even though it already works well with its default settings. We applied RESHWOA and WOA methods on six microarray cancer datasets to optimize the SVM parameters. The exhaustive examination and detailed results demonstrate that the new structure has addressed WOA’s main shortcomings. We conclude that the proposed RESHWOA performed significantly better than the WOA.

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Detection of Weed Using Visual Attention Model and SVM Classifier

Cited by 6 publications

References 8 publications

Multi-Level Attention Recognition of EEG Based on Feature Selection

Multi-Level Attention Recognition of EEG Based on Feature Selection

Saliency-Based Semantic Weeds Detection and Classification Using UAV Multispectral Imaging

Hybrid whale algorithm with evolutionary strategies and filtering for high-dimensional optimization: Application to microarray cancer data

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