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Improving Badminton Action Recognition Using Spatio-Temporal Analysis and a Weighted Ensemble Learning Model

Computers, Materials & Continua 2024年第11期81卷 3079-3096页

作者： Farida Asriani Azhari Azhari wahyono wahyono Department of Computer Science and Electronics Universitas Gadjah MadaYogyakarta55281Indonesia Electrical Engineering Department Universitas Jenderal SoedirmanPurbalingga53371Indonesia

Incredible progress has been made in human action recognition(HAR),significantly impacting computer vision applications in sports ***,identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion *** learning techniques like convolutional neural networks(CNNs),long short-term memory(LSTM),and graph convolutional networks(GCNs)improve recognition in large datasets,while the traditional machine learning methods like SVM(support vector machines),RF(random forest),and LR(logistic regression),combined with handcrafted features and ensemble approaches,perform well but struggle with the complexity of fast-paced sports like *** proposed an ensemble learning model combining support vector machines(SVM),logistic regression(LR),random forest(RF),and adaptive boosting(AdaBoost)for badminton action *** data in this study consist of video recordings of badminton stroke techniques,which have been extracted into spatiotemporal *** three-dimensional distance between each skeleton point and the right hip represents the spatial *** temporal features are the results of Fast Dynamic Time Warping(FDTW)calculations applied to 15 frames of each video *** weighted ensemble model employs soft voting classifiers from SVM,LR,RF,and AdaBoost to enhance the accuracy of badminton action *** E2 ensemble model,which combines SVM,LR,and AdaBoost,achieves the highest accuracy of 95.38%.

关键词： Weighted ensemble learning badminton action soft voting classifier joint skeleton fast dynamic time warping spatiotemporal

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A comprehensive survey on weed and crop classification using machine learning and deep learning

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Artificial Intelligence in Agriculture 2024年第3期13卷 45-63页

作者： Faisal Dharma Adhinata wahyono Raden Sumiharto Department of Computer Science and Electronics Faculty of Mathematic and Natural SciencesUniversitas Gadjah MadaYogyakartaIndonesia Faculty of Informatics Institut Teknologi Telkom PurwokertoCentral JavaIndonesia

Machine learning and deep learning are subsets of Artificial Intelligence that have revolutionized object detection and classification in images or *** technology plays a crucial role in facilitating the transition from conventional to precision agriculture,particularly in the context of weed *** agriculture,which previously relied on manual efforts,has now embraced the use of smart devices for more efficient weed ***,several challenges are associated with weed detection,including the visual similarity between weed and crop,occlusion and lighting effects,as well as the need for early-stage weed ***,this study aimed to provide a comprehensive review of the application of both traditional machine learning and deep learning,as well as the combination of the two methods,for weed detection across different crop *** results of this review show the advantages and disadvantages of using machine learning and deep ***,deep learning produced superior accuracy compared to machine learning under various *** learning required the selection of the right combination of features to achieve high accuracy in classifyingweed and crop,particularly under conditions consisting of lighting and early growth ***,a precise segmentation stage would be required in cases of *** learning had the advantage of achieving real-time processing by producing smaller models than deep learning,thereby eliminating the need for additional ***,the development of GPU technology is currently rapid,so researchers are more often using deep learning for more accurate weed identification.

关键词： Deep learning Machine learning Precision agriculture Weed control Weed detection

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Feature aggregation for nutrient deficiency identification in chili based on machine learning

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Artificial Intelligence in Agriculture 2023年第2期8卷 77-90页

作者： Deffa Rahadiyan Sri Hartati wahyono Andri Prima Nugroho Department of Computer Science and Electronics Universitas Gadjah Mada(UGM)YogyakartaIndonesia Department of Agricultural and Biosystems Engineering Universitas Gadjah Mada(UGM)YogyakartaIndonesia

Macronutrient deficiency inhibits the growth and development of chili *** of the non-destructive methods that plays a role in processing plant image data based on specific characteristics is computer *** study uses 5166 image data after augmentation process for six plant health *** the analysis of one feature cannot represent plant health ***,a careful combination of features is *** study combines three types of features with HSV and RGB for color,GLCM and LBP for texture,and Hu moments and centroid distance for *** feature and its combination are trained and tested using the same MLP *** combination of RGB,GLCM,Hu moments,and Distance of centroid features results the best *** addition,this study compares the MLP architecture used with previous studies such as SVM,Random Forest Technique,Naive Bayes,and *** produced the best performance,followed by SVM and MLP,with accuracy reaching 97.76%,90.55%and 89.70%,*** MLP has lower accuracy than CNN,the model for identifying plant health conditions has a reasonably good success rate to be applied in a simple agricultural environment.

关键词： Feature Combination Multi-Layer Perceptron Classifier Nutrient deficiency

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