臺灣博碩士論文加值系統

影像分析是一種強大的方法能有效解決多種難題，能有效應用於農業領域。植物影像分析在農業中扮演著重要的角色，適用於植物研究以提高作物產量的研究效率和品質。本研究旨在探索基於植物特徵的演算法能夠快速進行非破壞性取樣 (non-destructive sampling) 進行外表型性狀分析。Otsu為單一通道的影像分割 (image segmentation) 方法，G-Otsu是一種多層次模型，結合了Otsu與多張影像之間的訊息，適合用於整個資料集分析。Otsu適用於雜訊與目標像素值差距大時，若雜訊貼近目標像素值，Otsu方法的影像分割的前景則容易涵蓋雜訊，而G-Otsu適用於解決此問題。G-Otsu透過不同摘要統計量的應用，本篇共有M、V、MV、K和L版本的G-Otsu方法。K和L版本G-Otsu使用了不同的係數調整，結合了影像之間的相似性與變異性進行影像分割。K版本的G-Otsu在擁有5~100張影像的資料集中，成功分割影像的機率較原始Otsu版本高。透過G-Otsu，我們能夠更好地利用影像之間的特徵訊息。未來，我們可以優化G-Otsu，藉由額外的植物特徵或使用多通道分析，結合多閾值進行影像分析，擴大G-Otsu於其他作物的溫室或生長室的應用。同時，也將結合其他影像分析方法和機器學習方法，以進一步提升分析的準確性。期望基於植物影像特徵的分析將在農業領域發揮重要的作用，為作物生產的提升與農業發展做出貢獻。

Image analysis is a powerful method with extensive applications in agriculture that can address various challenges effectively. Plant image analysis plays a crucial role in agriculture, enabling efficient and high-quality research based on plant phenotyping. The aim of this study is to explore algorithms based on plant features to conduct rapid non-destructive sampling for phenotypic trait analysis. The traditional Otsu algorithm is a single-channel image segmentation method. However, when noise pixel values are very close to the target pixel values, Otsu segmentation may inadvertently include noise. The G-Otsu algorithms are developed in this project, which is a multi-level model combining Otsu with additional information from multiple images. G-Otsu efficiently estimates multiple thresholds simultaneously for all images in the dataset. Five different versions of G-Otsu were tested and each employing different summary statistics: M, V, MV, K, and L versions. The K and L versions of G-Otsu employ distinct coefficient adjustments to incorporate both image similarity and variability into the segmentation algorithm. These two models show higher success rates in image segmentation compared to the original Otsu algorithm. In addition, the K-version of G-Otsu also demonstrates a higher success rate of image segmentation than Otsu in multiple datasets containing between 5 to 100 images. Through G-Otsu, we can better utilize feature information across images. In the future, we can further optimize G-Otsu by incorporating additional plant features or employing multi-channel analysis, integrating multiple thresholds for image segmentation, and expanding the application of G-Otsu to other crops in greenhouses or growth chambers. Additionally, combining other image analysis methods and machine learning techniques will further enhance the accuracy of analysis. It is expected that the analysis based on plant image features will play a crucial role in the agricultural field, contributing to crop production improvement and agricultural development.

論文口試委員審定書 i
中文摘要 ii
Abstract iii
目錄 iv
表目錄 viii
圖目錄 ix
第1章 介紹 1
1.1 影像的基本構成 1
1.1.1 L*A*B*色彩空間 2
1.2 影像分析 2
1.2.1 影像分析的目的和用途 3
1.2.2 應用影像分析於植物與影像特徵 4
1.3 影像分析技術 4
1.3.1 影像分割-二值化 5
1.4 二值化方法-Otsu 6
1.4.1 優點與缺點 6
1.4.2 應用 6
1.5 多層次模型 7
1.6 研究目的 8
第2章 材料與方法 10
2.1 Otsu 10
2.2 G-Otsu 11
2.2.1 利用平均值建立G-Otsu 15
2.2.2 利用變異程度的概念建立G-Otsu 17
2.2.3 結合平均值與變異程度建立G-Otsu 19
2.2.4 調整平均值與變異程度的權重建立G-Otsu 20
2.2.4.1 調整平均值的權重建立G-Otsu 20
2.2.4.2 調整變異程度的權重建立G-Otsu 20
2.3 植物資料集 21
2.4 模擬資料 22
2.5 最佳化過程 23
2.6 實現方法 24
2.7 驗證資料 24
第3章 結果 26
3.1 基於平均值的G-Otsu 26
3.1.1 小的植物資料集 26
3.1.2 大的植物資料集 26
3.2 基於變異程度的G-Otsu 28
3.2.1 小的植物資料集 28
3.2.2 大的植物資料集 28
3.3 結合平均數與變異程度的G-Otsu 30
3.3.1 小的植物資料集 30
3.3.2 大的植物資料集 30
3.4 調整平均值與變異程度的權重建立G-Otsu 32
3.4.1 調整平均值的權重建立G-Otsu 32
3.4.1.1 小的植物資料集 32
3.4.1.2 大的植物資料集 34
3.4.2 調整變異程度的權重建立G-Otsu 36
3.4.2.1小的植物資料集 36
3.4.2.2 大的植物資料集 36
3.5 將K版本G-Otsu運用於不同大小資料集 39
第4章 討論 41
4.1 M版本G-Otsu應用於兩個植物資料集上 41
4.2 V版本G-Otsu應用於兩個植物資料集上 42
4.3 MV版本G-Otsu應用於兩個植物資料集上 42
4.4 調整MV版本G-Otsu應用於兩個植物資料集上 43
4.4.1 K版本G-Otsu應用於兩個植物資料集上 43
4.4.2 L版本G-Otsu應用於兩個植物資料集上 43
4.5 將K版本應用於G-Otsu運用於不同資料集 44
第5章 結論 45
5.1 多個版本的應用 45
5.2 G-Otsu與單一影像Otsu比較 46
5.3 未來方向 47
參考文獻 48
附錄 51

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of big Data, 8, 1-74.
Amy Xia, H., Ma, H., & Carlin, B. P. (2011). Bayesian hierarchical modeling for detecting safety signals in clinical trials. Journal of Biopharmaceutical Statistics, 21(5), 1006-1029.
Balduzzi, M., Binder, B. M., Bucksch, A., Chang, C., Hong, L., Iyer-Pascuzzi, A. S., Pradal, C., & Sparks, E. E. (2017). Reshaping plant biology: qualitative and quantitative descriptors for plant morphology. Frontiers in Plant Science, 8, 117.
Bhandari, A. K., Maurya, S., & Meena, A. K. (2018). Social spider optimization based optimally weighted Otsu thresholding for image enhancement. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
Bosker, R., & Snijders, T. A. (2011). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Multilevel analysis, 1-368.
Bouguettaya, A., Zarzour, H., Kechida, A., & Taberkit, A. M. (2022). Deep learning techniques to classify agricultural crops through UAV imagery: A review. Neural Computing and Applications, 34(12), 9511-9536.
Broët, P., Richardson, S., & Radvanyi, F. (2002). Bayesian hierarchical model for identifying changes in gene expression from microarray experiments. Journal of Computational Biology, 9(4), 671-683.
Chipman, H., & Tibshirani, R. (2006). Hybrid hierarchical clustering with applications to microarray data. Biostatistics, 7(2), 286-301.
Cho, J., Choi, J., Qiao, M., Ji, C., Kim, H., Uhm, K., & Chon, T. (2007). Automatic identification of whiteflies, aphids and thrips in greenhouse based on image analysis. Red, 346(246), 244.
Cressie, N., Calder, C. A., Clark, J. S., Hoef, J. M. V., & Wikle, C. K. (2009). Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. Ecological Applications, 19(3), 553-570.
Fahlgren, N., Feldman, M., Gehan, M. A., Wilson, M. S., Shyu, C., Bryant, D. W., Hill, S. T., McEntee, C. J., Warnasooriya, S. N., & Kumar, I. (2015). A versatile phenotyping system and analytics platform reveals diverse temporal responses to water availability in Setaria. Molecular plant, 8(10), 1520-1535.
Fan, H., Xie, F., Li, Y., Jiang, Z., & Liu, J. (2017). Automatic segmentation of dermoscopy images using saliency combined with Otsu threshold. Computers in biology and medicine, 85, 75-85.
Feldman, M. J., Paul, R. E., Banan, D., Barrett, J. F., Sebastian, J., Yee, M.-C., Jiang, H., Lipka, A. E., Brutnell, T. P., & Dinneny, J. R. (2017). Time dependent genetic analysis links field and controlled environment phenotypes in the model C4 grass Setaria. PLoS genetics, 13(6), e1006841.
Fomalont, E. B. (1999). Image analysis. In Synthesis Imaging in Radio Astronomy II (Vol. 180, p. 301).
Fu, K.-S., & Mui, J. (1981). A survey on image segmentation. Pattern recognition, 13(1), 3-16.
Ghaderi-Zefrehei, M., Memari, H. R., & Kadkhodaei, S. (2010). Multilevel modeling in human microarray time course gene expression data. In 2010 17th Iranian Conference of Biomedical Engineering (ICBME) (pp. 1-5). IEEE.
Goh, T. Y., Basah, S. N., Yazid, H., Safar, M. J. A., & Saad, F. S. A. (2018). Performance analysis of image thresholding: Otsu technique. Measurement, 114, 298-307.
Haralick, R. M., & Shapiro, L. G. (1985). Image segmentation techniques. Computer vision, graphics, and image processing, 29(1), 100-132.
Jain, A. K. (1989). Fundamentals of digital image processing. Prentice-Hall, Inc.
Kan, A. (2017). Machine learning applications in cell image analysis. Immunology and cell biology, 95(6), 525-530.
Kaplan, D. R. (2001). The science of plant morphology: definition, history, and role in modern biology. American Journal of Botany, 88(10), 1711-1741.
Karakos, D. G., & Trahanias, P. E. (1997). Generalized multichannel image-filtering structures. IEEE Transactions on image processing, 6(7), 1038-1045.
Kimchi, R. (1992). Primacy of wholistic processing and global/local paradigm: a critical review. Psychological bulletin, 112(1), 24.
King, G., Rosen, O., & Tanner, M. A. (1999). Binomial-beta hierarchical models for ecological inference. Sociological Methods & Research, 28(1), 61-90.
Kumah, C., Zhang, N., Raji, R. K., & Pan, R. (2019). Color measurement of segmented printed fabric patterns in lab color space from RGB digital images. Journal of Textile Science and Technology, 5(1), 1-18.
Kumar, P., & Miklavcic, S. J. (2018). Analytical study of colour spaces for plant pixel detection. Journal of Imaging, 4(2), 42.
Lavania, S., & Matey, P. S. (2015). Novel method for weed classification in maize field using Otsu and PCA implementation. In 2015 IEEE International Conference on Computational Intelligence & Communication Technology (pp. 534-537). IEEE.
Malarvel, M., Sethumadhavan, G., Bhagi, P. C. R., Kar, S., & Thangavel, S. (2017). An improved version of Otsu's method for segmentation of weld defects on X-radiography images. Optik, 142, 109-118.
Mao, K., & Sun, C. (1991). A refined global‐local finite element analysis method. International Journal for Numerical Methods in Engineering, 32(1), 29-43.
McGavin, D., Stukenborg, B., & Witkowski, M. (2005). Color figures in BJ: RGB versus CMYK. Biophysical journal, 88(2), 761-762.
Navon, D. (1977). Forest before trees: The precedence of global features in visual perception. Cognitive psychology, 9(3), 353-383.
Oberholzer, M., Östreicher, M., Christen, H., & Brühlmann, M. (1996). Methods in quantitative image analysis. Histochemistry and cell biology, 105, 333-355.
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66.
Pal, N. R., & Pal, S. K. (1993). A review on image segmentation techniques. Pattern recognition, 26(9), 1277-1294.
Pourdarbani, R., & Rezaei, B. (2011). Automatic detection of greenhouse plants pests by image analysis. Tarım Makinaları Bilimi Dergisi, 7(2), 171-174.
Pratikakis, I., Gatos, B., & Ntirogiannis, K. (2013). ICDAR 2013 document image binarization contest (DIBCO 2013). In 2013 12th International Conference on Document Analysis and Recognition (pp. 1471-1476). IEEE.
Raudenbush, S., & Bryk, A. S. (1986). A hierarchical model for studying school effects. Sociology of education, 1-17.
Sahoo, P. K., Soltani, S., & Wong, A. K. (1988). A survey of thresholding techniques. Computer vision, graphics, and image processing, 41(2), 233-260.
Saikumar, K., & Rajesh, V. (2020). A novel implementation heart diagnosis system based on random forest machine learning technique. International Journal of Pharmaceutical Research (09752366).
Service, P. (2013). Defining and Communicating Color: The CIELAB System. In: Sappi Fine Paper North America.
Solomon, C., & Breckon, T. (2011). Fundamentals of Digital Image Processing: A practical approach with examples in Matlab. John Wiley & Sons.
Tian, H., Wang, T., Liu, Y., Qiao, X., & Li, Y. (2020). Computer vision technology in agricultural automation—A review. Information Processing in Agriculture, 7(1), 1-19.
Wang, S., & Haralick, R. M. (1984). Automatic multithreshold selection. Computer vision, graphics, and image processing, 25(1), 46-67.
Yu, Y., Bao, Y., Wang, J., Chu, H., Zhao, N., He, Y., & Liu, Y. (2021). Crop row segmentation and detection in paddy fields based on treble-classification otsu and double-dimensional clustering method. Remote Sensing, 13(5), 901.
Zaborowicz, M., Boniecki, P., Koszela, K., Przybylak, A., & Przybył, J. (2017). Application of neural image analysis in evaluating the quality of greenhouse tomatoes. Scientia Horticulturae, 218, 222-229.
Zaborowicz, M., Przybył, J., Koszela, K., Boniecki, P., Mueller, W., Raba, B., Lewicki, A., & Przybył, K. (2014). Computer image analysis in obtaining characteristics of images: greenhouse tomatoes in the process of generating learning sets of artificial neural networks. In Sixth International Conference on Digital Image Processing (ICDIP 2014) (Vol. 9159, pp. 63-68). SPIE.
Zhan, Y., & Zhang, G. (2019). An improved OTSU algorithm using histogram accumulation moment for ore segmentation. Symmetry, 11(3), 431.
Zhu, L., & Yang, Y. (2020). Actbert: Learning global-local video-text representations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 8746-8755).