請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73111
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 劉力瑜(Li-yu Daisy Liu) | |
dc.contributor.author | Jauru Pei | en |
dc.contributor.author | 裴昭如 | zh_TW |
dc.date.accessioned | 2021-06-17T07:17:59Z | - |
dc.date.available | 2021-08-05 | |
dc.date.copyright | 2019-08-05 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-07-11 | |
dc.identifier.citation | Assefa, Y., Vara Prasad, P. V., Carter, P., Hinds, M., Bhalla, G., Schon, R., ... & Ciampitti, I. A. (2016). Yield responses to planting density for US modern corn hybrids: A synthesis-analysis. Crop Science, 56(5), 2802-2817.
Baatz M. and Schäpe A. (2000). “Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation,” in Angewandte Geographische Informationsverarbeitung XII, Karlsruhe, Germany, pp. 12–23 Baloch, A. W., Soomro, A. M., Javed, M. A., Ahmed, M., Bughio, H. R., Bughio, M. S., & Mastoi, N. N. (2002). Optimum plant density for high yield in rice (Oryza sativa L.). Asian J. Plant Sci, 1(1), 25-27. Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS journal of photogrammetry and remote sensing, 65(1), 2-16. Barmeier, G., & Schmidhalter, U. (2016). High-throughput phenotyping of wheat and barley plants grown in single or few rows in small plots using active and passive spectral proximal sensing. Sensors, 16(11), 1860. Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., & Heynen, M. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of photogrammetry and remote sensing, 58(3-4), 239-258. Berge, T. W., Rene cederkvist, H., Aastveit, A. H., & Fykse, H. (2008). Simulating the effects of mapping and spraying resolution and threshold level on accuracy of patch spraying decisions and herbicide use based on mapped weed data. Acta Agriculturae Scandinavica Section B–Soil and Plant Science, 58(3), 216-229. Bivand, R., Keitt, T., Rowlingson, B., Pebesma, E., Sumner, M., Hijmans, R., ... & Bivand, M. R. (2015). Package ‘rgdal’. Bindings for the Geospatial Data Abstraction Library. Available online: https://cran. r-project. org/web/packages/rgdal/index. html (accessed on 15 October 2017). Bozorgi, H. R., Faraji, A., Danesh, R. K., Keshavarz, A., Azarpour, E., & Tarighi, F. (2011). Effect of plant density on yield and yield components of rice. World Applied Sciences Journal, 12(11), 2053-2057. Burgos-Artizzu, X. P., Ribeiro, A., Guijarro, M., & Pajares, G. (2011). Real-time image processing for crop/weed discrimination in maize fields. Computers and Electronics in Agriculture, 75(2), 337-346. Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., & Gattelli, M. (2015). Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote sensing, 7(4), 4026-4047. Chaki, N., Cortesi, A., & Devarakonda, N. (Eds.). (2017). Proceedings of International Conference on Computational Intelligence and Data Engineering: ICCIDE 2017 (Vol. 9). Springer. Cohen, Y., & Shoshany, M. (2002). A national knowledge-based crop recognition in Mediterranean environment. International Journal of Applied Earth Observation and Geoinformation, 4(1), 75-87. Dhanachandra, N., & Chanu, Y. J. (2017). A survey on image segmentation methods using clustering techniques. European Journal of Engineering Research and Science, 2(1), 15-20. Dhanachandra, N., Manglem, K., & Chanu, Y. J. (2015). Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Computer Science, 54, 764-771. Dharani, T., Aroquiaraj, I. L., & Mageshwari, V. (2016, August). Diverse image investigation using image metrics for content based image retrieval system. In 2016 International Conference on Inventive Computation Technologies (ICICT) (Vol. 2, pp. 1-8). IEEE. Dubey, S. R., Dixit, P., Singh, N., & Gupta, J. P. (2013). Infected fruit part detection using K-means clustering segmentation technique. Ijimai, 2(2), 65-72. Duro, D. C., Franklin, S. E., & Dubé, M. G. (2012). A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote sensing of environment, 118, 259-272. Elbatta, M. T., & Ashour, W. M. (2013). A dynamic method for discovering density varied clusters. A dynamic method for discovering density varied clusters, 6(1). Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996, August). A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd (Vol. 96, No. 34, pp. 226-231). Flanders, D., Hall-Beyer, M., & Pereverzoff, J. (2003). Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction. Canadian Journal of Remote Sensing, 29(4), 441-452. Floreano, D., & Wood, R. J. (2015). Science, technology and the future of small autonomous drones. Nature, 521(7553), 460. Gaonkar, M. N., & Sawant, K. (2013). AutoEpsDBSCAN: DBSCAN with Eps automatic for large dataset. International Journal on Advanced Computer Theory and Engineering, 2(2), 11-16. Gautam, R. K., & Panigrahi, S. (2007). Leaf nitrogen determination of corn plant using aerial images and artificial neural networks. Canadian Biosystems Engineering, 49, 7. Gnädinger, F., & Schmidhalter, U. (2017). Digital counts of maize plants by unmanned aerial vehicles (UAVs). Remote Sensing, 9(6), 544. Gupta, N., & Bhadauria, H. S. (2014). Object based information extraction from high resolution satellite imagery using eCognition. International Journal of Computer Science Issues (IJCSI), 11(3), 139. Huber, P. J. (1981). Robust statistics (pp. 1248-1251). Springer Berlin Heidelberg. Huang, M., Yang, C., Ji, Q., Jiang, L., Tan, J., & Li, Y. (2013). Tillering responses of rice to plant density and nitrogen rate in a subtropical environment of southern China. Field Crops Research, 149, 187-192. Iglewicz, B., & Hoaglin, D. C. (1993). How to Detect and Handle Outliers (The ASQC Basic Reference in Quality Control). Jin, X., Liu, S., Baret, F., Hemerlé, M., & Comar, A. (2017). Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment, 198, 105-114. Kassambara, A. (2017). Practical guide to cluster analysis in R: unsupervised machine learning (Vol. 1). STHDA. Khan, K., Rehman, S. U., Aziz, K., Fong, S., & Sarasvady, S. (2014, February). DBSCAN: Past, present and future. In The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014) (pp. 232-238). IEEE. Kodinariya, T. M., & Makwana, P. R. (2013). Review on determining number of Cluster in K-Means Clustering. International Journal, 1(6), 90-95. Lelong, C., Burger, P., Jubelin, G., Roux, B., Labbé, S., & Baret, F. (2008). Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors, 8(5), 3557-3585. Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764-766. Li, Y., Cao, Z., Lu, H., Xiao, Y., Zhu, Y., & Cremers, A. B. (2016). In-field cotton detection via region-based semantic image segmentation. Computers and Electronics in Agriculture, 127, 475-486. MacQueen, J. (1967, June). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (Vol. 1, No. 14, pp. 281-297). Marino, S., & Alvino, A. (2018). Detection of homogeneous wheat areas using multi-temporal UAS images and ground truth data analyzed by cluster analysis. European Journal of Remote Sensing, 51(1), 266-275. Miller, J. (1991). Reaction time analysis with outlier exclusion: Bias varies with sample size. The quarterly journal of experimental psychology, 43(4), 907-912. Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote sensing of environment, 115(5), 1145-1161. Mitra, S., & Nandy, J. (2011, June). KDDclus: A simple method for multi-density clustering. In Proceedings of International Workshop on Soft Computing Applications and Knowledge Discovery (SCAKD 2011), Moscow, Russia (pp. 72-76). Peña, J. M., Torres-Sánchez, J., de Castro, A. I., Kelly, M., & López-Granados, F. (2013). Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PloS one, 8(10), e77151. Rahmah, N., & Sitanggang, I. S. (2016, January). Determination of optimal epsilon (eps) value on dbscan algorithm to clustering data on peatland hotspots in sumatra. In IOP Conference Series: Earth and Environmental Science (Vol. 31, No. 1, p. 012012). IOP Publishing. Rahman, M., Hossain, M., & Bell, R. W. (2011). Plant density effects on growth, yield and yield components of two soybean varieties under equidistant planting arrangement. Asian Journal of Plant Sciences, 10(5), 278-286. Rahmani, M. K. I., Pal, N., & Arora, K. (2014). Clustering of image data using K-means and fuzzy K-means. International Journal of Advanced Computer Science and Applications, 5(7), 160-163. Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Transactions on Database Systems (TODS), 42(3), 19. Shrivastava, N., & Rai, P. K. (2015, June). An object based building extraction method and classification using high resolution remote sensing data/O metoda de extragere a cladirilor orientata obiect si clasificare folosind date de înalta rezolutie furnizate de teledetectie. In Forum Geografic (Vol. 14, No. 1, p. 14). University of Craiova, Department of Geography. Sitanggang, I. S., Risal, A. A. N., & Syaufina, L. (2018, November). Incremental Clustering on Hotspot Data as Forest and Land Fires Indicator in Sumatra. In IOP Conference Series: Earth and Environmental Science (Vol. 187, No. 1, p. 012043). IOP Publishing. Song, X., Wang, J., Huang, W., Liu, L., Yan, G., & Pu, R. (2009). The delineation of agricultural management zones with high resolution remotely sensed data. Precision agriculture, 10(6), 471-487. Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423. Torres-Sánchez, J., López-Granados, F., De Castro, A. I., & Peña-Barragán, J. M. (2013). Configuration and specifications of an unmanned aerial vehicle (UAV) for early site specific weed management. PloS one, 8(3), e58210. Trimble, D. (2014). Ecognition Developer Reference Book 9.0. Trimble Documentation München, Germany. Tripicchio, P., Satler, M., Dabisias, G., Ruffaldi, E., & Avizzano, C. A. (2015, July). Towards smart farming and sustainable agriculture with drones. In 2015 International Conference on Intelligent Environments (pp. 140-143). IEEE. Usman, M., Sitanggang, I. S., & Syaufina, L. (2015). Hotspot distribution analyses based on peat characteristics using density-based spatial clustering. Procedia Environmental Sciences, 24, 132-140. van Aardt, J. A., & Wynne, R. H. (2004, May). A multi-resolution approach to forest segmentation as a precursor to estimation of volume and biomass by species. In Proceedings of the American Society for Photogrammetric Engineering and Remote Sensing Annual Conference (pp. 24-28). Wang, M. (2008). A multiresolution remotely sensed image segmentation method combining rainfalling watershed algorithm and fast region merging. Remote Sensing & Spatial Information Sciences, XXXVII (Part B4). Beijing. Wang, T. C. (2018). Developing a field crop phenotyping and management decision support system with unmanned aerial vehicle-derived multispectral images (Unpublished master’s thesis). National Taiwan University, Taipei, Taiwan. Wang, P., Liu, S., Liu, M., Wang, Q., Wang, J., & Zhang, C. (2011, October). The improved dbscan algorithm study on maize purity identification. In International Conference on Computer and Computing Technologies in Agriculture (pp. 648-656). Springer, Berlin, Heidelberg. Yan, M., & Ye, K. (2007). Determining the number of clusters using the weighted gap statistic. Biometrics, 63(4), 1031-1037. Ye, Q., Gao, W., & Zeng, W. (2003, July). Color image segmentation using density-based clustering. In 2003 International Conference on Multimedia and Expo. ICME'03. Proceedings (Cat. No. 03TH8698) (Vol. 2, pp. II-401). IEEE. Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision agriculture, 13(6), 693-712. Zhang, Z. (2016). Introduction to machine learning: k-nearest neighbors. Annals of translational medicine, 4(11). | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73111 | - |
dc.description.abstract | 植物密度對於作物生長是一重要的影響因子,其可影響作物產量、水分吸收、肥料需求,甚至是對病蟲害的敏感度。前人研究指出,決定最佳的植物密度及行株距對作物而言是一重要的管理決策。而在傳統上,植物密度可由人力至田區中直接以目視進行植株個數計算進而得知,但以人力直接進行田間調查是耗時且費力的。隨著科技發展,空拍影像也是幫助獲取植物密度的一個新方法。相較於更為傳統的衛星影像,無人機影像的解析度提升,並且可依使用者需求訂定飛行高度及路線。本研究對桃園農業改良場四月初期之水稻田區多光譜影像,分別利用像素影像分析及物件影像分析,透過常態化差值植生指標計算田區中的植株位置及個數,藉以推估田區之植物密度。在本文的方法中,像素影像分析主要是以分群法來進行;而物件影像分析,則利用eCognition 中的多尺度影像切割及回歸線來輔助進行。研究結果顯示,像素影像分析以分群法估計田區影像的植株位置之數量而言,可達到85.56 % 的準確率,並且可看出田區中植株初期生長較不佳的作物位置;而物件影像分析準確率則可達96.01 %,並從中可得知缺株位置個數。雖然兩種方法仍有部分需人為操作,但我們的結果提供了對於無人機空拍影像分析之部分自動化的一些可能性,亦有機會用於計算植物密度。 | zh_TW |
dc.description.abstract | Plant density is a crucial factor influencing crop yields, water assimilation, and fertilizer requirement as well as sensitivity to pathogens. Previous studies presented that determining optimal plant density and row spacing is a critical management decision for crop production. Traditionally, plant density can be obtained from labors by visual counting of crops in field directly, whereas it is time-consuming and laborious. With the advance of technology, aerial images become a new approach to assist in precision agriculture and acquisition of plant density. Compared to the conventional satellite images, images of unmanned aerial vehicles (UAVs) offer higher resolution, and the flight height and route can be defined by the request of users. Several previous studies have investigated the crop growth in the field by UAV images. This study utilized the pixel-based image analysis and object-based image analysis to analyze the multispectral rice field images of Taoyuan District Agricultural Improvement Station. With the Normalized Difference Vegetation Index, the number of position of rice in the field could be computed, and the plant density of the field could be known. There were two primary methods in our study, pixel-based image analysis was mainly conducted by density-based clustering, while object-based image analysis exploited the multiresolution segmentation algorithm in eCognition and the assistance of regression lines. According to the number of field crop positions, results presented that the accuracy of clustering method reached 85.56 %, and this method could previously detect the crop positions requiring the attention from managers. In terms of regression line method, the accuracy achieved 96.01 %, and number of the miss-planted positions could be found. Although manual operation was demanded in this work, our results still provided possibilities of partially automated analysis of UAV images, and it also had opportunity to evaluate plant density. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T07:17:59Z (GMT). No. of bitstreams: 1 ntu-108-R06621201-1.pdf: 2487120 bytes, checksum: 49865f3d415a0008d2773f609fcdf65a (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 謝辭 i
摘要 ii Abstract iii Table of Contents v List of Figures vii List of Tables ix Abbreviation table x Introduction 1 Materials and methods 7 2.1 Aerial image acquisition 8 2.2 Image preprocessing 11 2.3 Crop central positions finding 12 2.3.1 Clustering method 12 2.3.2 Regression line method 19 2.4 Calculation of accuracy 24 Results 26 3.1 Clustering method 27 3.2 Regression line method 35 Discussions 41 4.1 Comparison of clustering method and regression line method 41 4.2 Improvement of manual operation 42 4.3 Estimating number of cluster via gap statistic 42 4.4 Application of Eps in multi-density level k-distance plot 44 4.5 Results of different indices 45 Conclusions 46 Reference 47 Appendix I - Image preprocessing in ArcGIS (version 10.5) 54 Appendix II - Image segmentation in eCognition (version 9.4) 57 Appendix III - Find central points of objects by ArcGIS (version 10.5) 59 | |
dc.language.iso | en | |
dc.title | 透過無人機早期多光譜影像判斷水稻植株位置 | zh_TW |
dc.title | Plant Position Determination of Rice by Early-Stage Multispectral Images Obtained from Unmanned Aerial Vehicles | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 黃文達(Wen-Dar Huang),蔡育彰(Yu-Chang Tsai),黃乾綱(Chien-Kang Huang) | |
dc.subject.keyword | 無人機,多光譜影像,植物密度,植生植標,影像分析, | zh_TW |
dc.subject.keyword | unmanned aerial vehicle(UAV),multispectral images,plant density,vegetative index,image analysis, | en |
dc.relation.page | 60 | |
dc.identifier.doi | 10.6342/NTU201901190 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-07-11 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 農藝學研究所 | zh_TW |
顯示於系所單位: | 農藝學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-108-1.pdf 目前未授權公開取用 | 2.43 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。