2019 International Conference on Computer and Drone Applications (IConDA)最新文献_第2页

Development of Multiple Configuration Flying Wing UAV 多构型飞翼无人机的研制

2019 International Conference on Computer and Drone Applications (IConDA) Pub Date : 2019-12-01 DOI: 10.1109/IConDA47345.2019.9034911

Yaakob Mansor, Z. Sahwee, M. H. M. Asri

引用次数: 2

Prediction of Blast Furnace Slag Concrete Compressive Strength Using Artificial Neural Networks and Multiple Regression Analysis 基于人工神经网络和多元回归分析的高炉矿渣混凝土抗压强度预测

2019 International Conference on Computer and Drone Applications (IConDA) Pub Date : 2019-12-01 DOI: 10.1109/IConDA47345.2019.9034920

F. H. Chiew

引用次数: 6

[Title page] (标题页)

2019 International Conference on Computer and Drone Applications (IConDA) Pub Date : 2019-12-01 DOI: 10.1109/iconda47345.2019.9034680

引用次数: 0

Development of Rice Growth Map Using the Advanced Remote Sensing Techniques 利用先进遥感技术开发水稻生长图谱

2019 International Conference on Computer and Drone Applications (IConDA) Pub Date : 2019-12-01 DOI: 10.1109/IConDA47345.2019.9034916

N. Ya, Loong Shern Lee, M. Ismail, S. M. Razali, Nor Athirah Roslin, M. H. Omar

{"title":"Development of Rice Growth Map Using the Advanced Remote Sensing Techniques","authors":"N. Ya, Loong Shern Lee, M. Ismail, S. M. Razali, Nor Athirah Roslin, M. H. Omar","doi":"10.1109/IConDA47345.2019.9034916","DOIUrl":"https://doi.org/10.1109/IConDA47345.2019.9034916","url":null,"abstract":"Rice monitoring is one of the main issues in rice productivity. Farmers face difficulties in monitoring their rice fields due to climate change, soil conditions, age of the farmers and time consumed to monitor the whole area. Remote sensing technology is one of the alternatives to monitor rice field. The advancement of unmanned aerial vehicle (UAV) technology has been rapidly growing and frequently used in the agriculture industries to monitor crop condition. The objectives of this research are creating crop growth map using aerial imagery and object-based image analysis (OBIA) technique, and validating the normalized difference vegetative index (NDVI) value in rice field map using soil plant analysis development (SPAD) and GreenSeeker data. The multispectral image is processed using OBIA to produce crop growth map. The crop growth map produced is embedded with information that is able to indicate the health status of the rice crop using NDVI. This research was carried out at a paddy field planted using PadiU Putra variety in Ladang Merdeka, Ketereh, Kelantan (0.79 ha). The results from this research show that OBIA method can classify vegetation and non-vegetation to produce crop growth map. NDVI map has a strong correlation with Greenseeker data at 0.893 with positive correlation at 0.05 compared to SPAD meter. The crop growth map allows farmers to improve their rice farm monitoring more effectively using remote sensing technique.","PeriodicalId":175668,"journal":{"name":"2019 International Conference on Computer and Drone Applications (IConDA)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134194916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Propulsion System Drag Reduction for Vertical Takeoff and Land (VTOL) Unmanned Aerial Vehicle (UAV) 垂直起降(VTOL)无人机的推进系统减阻

2019 International Conference on Computer and Drone Applications (IConDA) Pub Date : 2019-12-01 DOI: 10.1109/IConDA47345.2019.9034687

M. H. M. Asri, Z. Sahwee, N. Kamal

引用次数: 1

Hyperpartisan News and Articles Detection Using BERT and ELMo 基于BERT和ELMo的超党派新闻和文章检测

2019 International Conference on Computer and Drone Applications (IConDA) Pub Date : 2019-12-01 DOI: 10.1109/IConDA47345.2019.9034917

Gerald Ki Wei Huang, Jun Choi Lee

引用次数: 6

Evaluation of Feature Extraction Methods for Classification of Palm Trees in UAV Images 无人机图像中棕榈树分类特征提取方法评价

2019 International Conference on Computer and Drone Applications (IConDA) Pub Date : 2019-12-01 DOI: 10.1109/IConDA47345.2019.9034913

Z. Chen, I. Liao

{"title":"Evaluation of Feature Extraction Methods for Classification of Palm Trees in UAV Images","authors":"Z. Chen, I. Liao","doi":"10.1109/IConDA47345.2019.9034913","DOIUrl":"https://doi.org/10.1109/IConDA47345.2019.9034913","url":null,"abstract":"Palm tree detection using remote sensing images has received increasing attention in recent years, concerning the issues of sustainability, productivity and profitability. There has been significant progress in the research of using machine learning techniques, especially convolutional neural networks (CNNs) for automatic palm tree detection. However, whether CNNs can actually outperform traditional human-engineered approaches in terms of classification accuracy and detection speed is yet unknown. In the present study, we have compared human-engineered features namely histogram of oriented gradients (HOG), local binary pattern (LBP) and scale-invariant feature transform (SIFT) with features extracted using pre-trained AlexNet model for detecting palm trees in high resolution images obtained via unmanned aerial vehicle (UAV). Support vector machines (SVM) with linear and non-linear kernels were used to classify feature vectors obtained by different feature extractors. Haar-like features used in Viola-Jones framework was also tested in the study. Results showed that features extracted from the fifth convolutional layer of AlexNet achieved the highest accuracy of 96.1% using SVM with RBF kernel as classifier, which surpassed the accuracy obtained by fully-connected CNN, namely 95.6%. The results also suggest that SVM classifier with radial basis function (RBF) kernel using LBP as features is the optimal combination as it achieved comparable accuracy but higher detection speed than CNN approaches.","PeriodicalId":175668,"journal":{"name":"2019 International Conference on Computer and Drone Applications (IConDA)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133710662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Assessing Performance of Aerobic Routines using Background Subtraction and Intersected Image Region 基于背景减法和图像相交区域的有氧动作性能评价

2019 International Conference on Computer and Drone Applications (IConDA) Pub Date : 2018-10-03 DOI: 10.1109/IConDA47345.2019.9034912

F. John, I. Hipiny, Hamimah Ujir, M. Sunar

引用次数: 7