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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 周呈霙(Cheng-Ying Chou) | |
| dc.contributor.author | Yen-Shuo Chen | en |
| dc.contributor.author | 陳彥碩 | zh_TW |
| dc.date.accessioned | 2023-03-19T22:44:34Z | - |
| dc.date.copyright | 2022-08-29 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-08-10 | |
| dc.identifier.citation | Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, 39(12), 2481-2495. Bianco, S., Cusano, C., Napoletano, P., & Schettini, R. (2017). Improving CNN-based texture classification by color balancing. Journal of Imaging, 3(3), 33. Bolya, D., Zhou, C., Xiao, F., & Lee, Y. J. (2019). Yolact: Real-time instance segmentation. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 9157-9166). Carlson, T. N., & Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote sensing of Environment, 62(3), 241-252. Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., ... & Lin, D. (2019). MMDetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155. Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European conference on computer vision (ECCV) (pp. 801-818). Delon, J. (2004). Midway image equalization. Journal of Mathematical Imaging and Vision, 21(2), 119-134. ESRI. (2021). ArcGIS Pro. Ver. 2.7.2. Redlands, CA: Environmental Systems Research Institute, Inc. Ghiasi, G., Cui, Y., Srinivas, A., Qian, R., Lin, T. Y., Cubuk, E. D., ... & Zoph, B. (2021). Simple copy-paste is a strong data augmentation method for instance segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2918-2928). He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969). He, K., Sun, J., & Tang, X. (2010). Single image haze removal using dark channel prior. IEEE transactions on pattern analysis and machine intelligence, 33(12), 2341-2353. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). IMF, O., & UNCTAD, W. (2011). Price volatility in food and agricultural markets: Policy responses. FAO: Roma, Italy. Kirillov, A., He, K., Girshick, R., Rother, C., & Dollár, P. (2019). Panoptic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9404-9413). Kirillov, A., Wu, Y., He, K., & Girshick, R. (2020). Pointrend: Image segmentation as rendering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 9799-9808). Li, X., Myint, S. W., Zhang, Y., Galletti, C., Zhang, X., & Turner II, B. L. (2014). Object-based land-cover classification for metropolitan Phoenix, Arizona, using aerial photography. International Journal of Applied Earth Observation and Geoinformation, 33, 321-330. Lin, C., Jin, Z., Mulla, D., Ghosh, R., Guan, K., Kumar, V., & Cai, Y. (2021). Toward Large-Scale Mapping of Tree Crops with High-Resolution Satellite Imagery and Deep Learning Algorithms: A Case Study of Olive Orchards in Morocco. Remote Sensing, 13(9), 1740. Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., ... & Zitnick, C. L. (2014, September). Microsoft coco: Common objects in context. In European conference on computer vision (pp. 740-755). Springer, Cham. Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431-3440). Maheswari, P., Raja, P., Apolo-Apolo, O. E., & Pérez-Ruiz, M. (2021). Intelligent fruit yield estimation for orchards using deep learning based semantic segmentation techniques—a review. Frontiers in Plant Science, 12, 684328. Nogueira, K., Dalla Mura, M., Chanussot, J., Schwartz, W. R., & Dos Santos, J. A. (2019). Dynamic multicontext segmentation of remote sensing images based on convolutional networks. IEEE Transactions on Geoscience and Remote Sensing, 57(10), 7503-7520. Osco, L. P., Nogueira, K., Marques Ramos, A. P., Faita Pinheiro, M. M., Furuya, D. E. G., Gonçalves, W. N., ... & dos Santos, J. A. (2021). Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery. Precision Agriculture, 22(4), 1171-1188. Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., & Lin, D. (2019). Libra R-CNN: Towards balanced learning for object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 821-830). Paul Jr, M., Molua, E. L., Nzie, J. R. M., & Fuh, G. L. (2020). Production and supply of tomato in Cameroon: Examination of the comparative effect of price and non-price factors. Scientific African, 10, e00574. Pizer, S. M., Amburn, E. P., Austin, J. D., Cromartie, R., Geselowitz, A., Greer, T., ... & Zuiderveld, K. (1987). Adaptive histogram equalization and its variations. Computer vision, graphics, and image processing, 39(3), 355-368. Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28. Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham. Tsouros, D. C., Bibi, S., & Sarigiannidis, P. G. (2019). A review on UAV-based applications for precision agriculture. Information, 10(11), 349. Vapnik, V. N., & Chervonenkis, A. Y. (1968). The uniform convergence of frequencies of the appearance of events to their probabilities. In Doklady Akademii Nauk (Vol. 181, No. 4, pp. 781-783). Russian Academy of Sciences. Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2017). Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1492-1500). 林怡均。2022。梨危機再起！日媒預警台灣鳳梨供過於求，進口量恐下滑，內銷不振產地價低迷。上下游News&Market。網址：https://www.newsmarket.com.tw/blog/168922/。刊登日期：2022-05-04 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/85115 | - |
| dc.description.abstract | 農作物的產量過剩或產量不足會影響至供需不平衡，進而產生價格波動，因此，使農民或農業相關單位能夠監控農地進而推估產量或控制品質是個重要的課題。然而，目前監控農地的方法為人估監控，這是一種成本高、耗時、費力、且易出現錯誤的方法，故本篇提出自動化的監控系統，此系統能自動分割每塊農地，此系統分成三個部分：轉換地理資訊成訓練模型的資料集、使用深度捲積神經網路模型、和將模型嵌入至常用的地理資訊系統ArcGIS Pro。此篇使用三種神經網路模型： Mask R-CNN、PointRend、和Yolact，期平均分割準確度分別為0.585、0.592、和 0.528，且使用我的模型來判釋一張航照的時間低於1分鐘。此篇的方法能有效率的分割航照圖中的坵塊面積，且可在ArcGIS Pro中使用本篇訓練好的模型，使農業相關產業或使用地理資訊系統的研究者有更有效率的監控面積的方法。 | zh_TW |
| dc.description.abstract | A surplus or shortage of agricultural fruits often leads to a severe imbalance between supply and demand. Therefore, farmers or agricultural entities monitoring orchards in different geographic areas to predict yield and quality was significant. However, manual monitoring is costly, time-consuming, and unstable. My study proposed a method to automatically identify orchards within a geographic area. The method consisted of three parts. Initially, the geographical information data was changed to COCO data format. The rectified aerial images were fed into a deep convolutional neural network (DCNN). The best segmentation mean precision (mAP) of Mask R-CNN, PointRend and Yolact were 0.585, 0.606, and 0.528, respectively. Then I implemented my well-trained models to the ArcGIS Pro. The processing time for an aerial image with my models was lower than 1 minute. As a result, the model predicted the acreage. Open regions are efficiently classified and segmented. The method could help the experts and reduce their loading of the works. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T22:44:34Z (GMT). No. of bitstreams: 1 U0001-0608202212112400.pdf: 3650040 bytes, checksum: da9cebfce96534ddcebf49b5d0633168 (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | Abstract i 摘要 ii Contents iii Figure Contents iv Table Contents v Chapter 1. Introduction 1 1.1. Purpose 1 1.2. Current Method 2 1.3. Objectives 3 Chapter 2. Literature Review 4 2.1. Remote Sensing 4 2.2. Unmanned Aerial Vehicle 5 2.3. Fruit yield estimation application 8 2.4. The direction of my research 9 Chapter 3. Materials and Methods 12 3.1. Datasets 12 3.2. Data Processing 14 3.2.1. Mislabeled Data 14 3.2.2. Flawed Images 14 3.3. Deep Convolutional Neural Networks 19 3.3.1. Mask R-CNN 19 3.3.2. PointRend 21 3.3.3. Yolact 22 3.4. Application System 24 Chapter 4. Results and Discussion 26 4.1. Dataset Building 26 4.2. The Image Recovery 28 4.3. The Performance of The DCNN Model 31 4.4. Application system 39 Chapter 5. Conclusions 43 Chapter 6. Future Works 44 Reference 45 | |
| dc.language.iso | en | |
| dc.subject | 航照圖 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 坵塊分割 | zh_TW |
| dc.subject | 遙測 | zh_TW |
| dc.subject | 果園判釋 | zh_TW |
| dc.subject | Parcel segmentation | en |
| dc.subject | Deep learning | en |
| dc.subject | Orchard classification | en |
| dc.subject | Aerial imagery | en |
| dc.subject | Remote sensing | en |
| dc.title | 深度學習應用於航照影像果園坵塊判釋 | zh_TW |
| dc.title | Orchard Classification and Parcel Segmentation in Aerial Imagery Using Deep Neural Networks | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 周瑞仁(Jui-Jen Chou),鄭克聲(Ke-Sheng Cheng) | |
| dc.subject.keyword | 深度學習,果園判釋,航照圖,遙測,坵塊分割, | zh_TW |
| dc.subject.keyword | Deep learning,Orchard classification,Aerial imagery,Remote sensing,Parcel segmentation, | en |
| dc.relation.page | 48 | |
| dc.identifier.doi | 10.6342/NTU202202109 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2022-08-11 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物機電工程學系 | zh_TW |
| dc.date.embargo-lift | 2022-08-29 | - |
| 顯示於系所單位： | 生物機電工程學系 | |
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