請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94581完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林達德 | zh_TW |
| dc.contributor.advisor | Ta-Te Lin | en |
| dc.contributor.author | 鄧喬尹 | zh_TW |
| dc.contributor.author | Chiao-Yin Teng | en |
| dc.date.accessioned | 2024-08-16T16:51:40Z | - |
| dc.date.available | 2024-08-17 | - |
| dc.date.copyright | 2024-08-16 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-13 | - |
| dc.identifier.citation | Abd El-Ghany, N. M., Abd El-Aziz, S. E., & Marei, S. S. (2020). A review: application of remote sensing as a promising strategy for insect pests and diseases management. Environmental Science and Pollution Research, 27(27), 33503-33515. https://doi.org/10.1007/s11356-020-09517-2
Arsenovic, M., Karanovic, M., Sladojevic, S., Anderla, A., & Stefanovic, D. (2019). Solving current limitations of deep learning based approaches for plant disease detection. Symmetry, 11(7), 939. https://doi.org/10.3390/sym11070939 Atallah, R., & Al-Mousa, A. (2019). Heart disease detection using machine learning majority voting ensemble method. In 2019 2nd international conference on new trends in computing sciences. https://doi.org/10.1109/ICTCS.2019.8923053 Ballou, D., Wang, R., Pazer, H., & Tayi, G. K. (1998). Modeling information manufacturing systems to determine information product quality. Management Science, 44(4), 462-484. https://doi.org/10.1287/mnsc.44.4.462 Barzman, M., Bàrberi, P., Birch, A. N. E., Boonekamp, P., Dachbrodt-Saaydeh, S., Graf, B., Hommel, B., Jensen, J. E., Kiss, J., Kudsk, P., Lamichhane, J. R., Messéan, A., Moonen, A.-C., Ratnadass, A., Ricci, P., Sarah, J.-L., & Sattin, M. (2015). Eight principles of integrated pest management. Agronomy for Sustainable Development, 35(4), 1199-1215. https://doi.org/10.1007/s13593-015-0327-9 Batty, M. (2012). Smart cities, big data. Environment and Planning B: Planning and Design, 39(2), 191-193. Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662-679. https://doi.org/10.1080/1369118X.2012.678878 Boyd, D., & Marwick, A. E. (2011). Social privacy in networked publics: Teens’ attitudes, practices, and strategies. In A decade in internet time: Symposium on the dynamics of the internet and society. Cai, L., & Zhu, Y. (2015). The challenges of data quality and data quality assessment in the big data era. Data Science Journal, 14, 2-2. https://doi.org/10.5334/dsj-2015-002 Chen, H., Chen, J., & Ding, J. (2021). Data evaluation and enhancement for quality improvement of machine learning. IEEE Transactions on Reliability, 70(2), 831-847. https://doi.org/10.1109/TR.2021.3070863 Chodey, M. D., & Noorullah Shariff, C. (2022). Hybrid deep learning model for in-field pest detection on real-time field monitoring. Journal of Plant Diseases and Protection, 129(3), 635-650. https://doi.org/10.1007/s41348-022-00584-w Dai, Q., Cheng, X., Qiao, Y., & Zhang, Y. (2020). Agricultural pest super-resolution and identification with attention enhanced residual and dense fusion generative and adversarial network. IEEE Access, 8, 81943-81959. https://doi.org/10.1109/ACCESS.2020.2991552 Dawei, W., Limiao, D., Jiangong, N., Jiyue, G., Hongfei, Z., & Zhongzhi, H. (2019). Recognition pest by image‐based transfer learning. Journal of the Science of Food and Agriculture, 99(10), 4524-4531. https://doi.org/10.1002/jsfa.9689 de Aquino, G. R. C., de Farias, C. M., & Pirmez, L. (2018). Data Quality Assessment and Enhancement on Social and Sensor Data. In BiDu-Posters@ VLDB. Deng, L., Wang, Z., Wang, C., He, Y., Huang, T., Dong, Y., & Zhang, X. (2020). Application of agricultural insect pest detection and control map based on image processing analysis. Journal of Intelligent & Fuzzy Systems, 38(1), 379-389. https://doi.org/10.3233/JIFS-179413 Deng, L., & Yu, D. (2014). Deep learning: methods and applications. Foundations and trends® in signal processing, 7(3–4), 197-387. https://doi.org/10.1561/2000000039 Deswal, M., & Sharma, N. (2014). A simplified review on fast HSV image color and texture detection and image conversion algorithm. International Journal of Computer Science and Mobile Computing, 3(5), 1216-1222. Dey, A., Bhoumik, D., & Dey, K. N. (2016). Automatic detection of whitefly pest using statistical feature extraction and image classification methods. Int. Res. J. Eng. Technol, 3(06), 1-10. Ebrahimi, M., Khoshtaghaza, M. H., Minaei, S., & Jamshidi, B. (2017). Vision-based pest detection based on SVM classification method. Computers and Electronics in Agriculture, 137, 52-58. https://doi.org/10.1016/j.compag.2017.03.016 Espinoza, K., Valera, D. L., Torres, J. A., López, A., & Molina-Aiz, F. D. (2016). Combination of image processing and artificial neural networks as a novel approach for the identification of Bemisia tabaci and Frankliniella occidentalis on sticky traps in greenhouse agriculture. Computers and Electronics in Agriculture, 127, 495-505. https://doi.org/doi:10.3390/agriculture10050161 Gao, D., Sun, Q., Hu, B., & Zhang, S. (2020). A framework for agricultural pest and disease monitoring based on internet-of-things and unmanned aerial vehicles. Sensors, 20(5), 1487. https://doi.org/10.3390/s20051487 Gevrey, M., & Worner, S. (2006). Prediction of global distribution of insect pest species in relation to climate by using an ecological informatics method. Journal of Economic Entomology, 99(3), 979-986. https://doi.org/10.1603/0022-0493-99.3.979 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. https://doi.org/10.1109/CVPR.2016.90 Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861. https://doi.org/10.48550/arXiv.1704.04861 Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. https://doi.org/10.1109/CVPR.2017.243 Huddar, S. R., Gowri, S., Keerthana, K., Vasanthi, S., & Rupanagudi, S. R. (2012). Novel algorithm for segmentation and automatic identification of pests on plants using image processing. In 2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT'12). https://doi.org/10.1109/ICCCNT.2012.6396012 Jiang, C. F., Peng, H. Y., & Yang, Y. K. (2016). Tail variance of portfolio under generalized Laplace distribution. Applied Mathematics and Computation, 282, 187-203. https://doi.org/10.1016/j.amc.2016.02.005 Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70-90. https://doi.org/10.1016/j.compag.2018.02.016 Karkouch, A., Mousannif, H., Al Moatassime, H., & Noel, T. (2016). Data quality in internet of things: A state-of-the-art survey. Journal of Network and Computer Applications, 73, 57-81. https://doi.org/10.1016/j.jnca.2016.08.002 Kasinathan, T., & Uyyala, S. R. (2021). Machine learning ensemble with image processing for pest identification and classification in field crops. Neural Computing and Applications, 33, 7491-7504. https://doi.org/10.1007/s00521-020-05497-z Kaya, Y., & Kayci, L. (2014). Application of artificial neural network for automatic detection of butterfly species using color and texture features. The visual computer, 30, 71-79. https://doi.org/doi.org/10.1007/s00371-013-0782-8 Khan, S. M., Ali, S., Nawaz, A., Bukhari, S. A. H., Ejaz, S., & Ahmad, S. (2019). Integrated pest and disease management for better agronomic crop production. Agronomic Crops: Volume 2: Management Practices, 385-428. https://doi.org/10.1007/978-981-32-9783-8_19 Kogan, M. (1998). Integrated pest management: historical perspectives and contemporary developments. Annual Review of Entomology, 43(1), 243-270. https://doi.org/10.1146/annurev.ento.43.1.243 Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25. https://doi.org/10.1145/3065386 Kumari, S., Kumar, D., & Mittal, M. (2021). An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier. International Journal of Cognitive Computing in Engineering, 2, 40-46. https://doi.org/10.1016/j.ijcce.2021.01.001 Lamichhane, J. R., Aubertot, J.-N., Begg, G., Birch, A. N. E., Boonekamp, P., Dachbrodt-Saaydeh, S., … & Messéan, A. (2016). Networking of integrated pest management: A powerful approach to address common challenges in agriculture. Crop Protection, 89, 139-151. https://doi.org/10.1016/j.cropro.2016.07.011 Lathey, A., & Atrey, P. K. (2015). Image enhancement in encrypted domain over cloud. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 11(3), 1-24. https://doi.org/10.1145/2656205 LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539 LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. https://doi.org/10.1109/5.726791 Lewis, W. J., Van Lenteren, J., Phatak, S. C., & Tumlinson Iii, J. (1997). A total system approach to sustainable pest management. Proceedings of the National Academy of Sciences, 94(23), 12243-12248. https://doi.org/10.1073/pnas.94.23.12243 Li, F., & Xiong, Y. (2018). Automatic identification of butterfly species based on HoMSC and GLCMoIB. The visual computer, 34(11), 1525-1533. https://doi.org/10.1007/s00371-017-1426-1 Li, W., Huang, R., Li, J., Liao, Y., Chen, Z., He, G., Yan, R., & Gryllias, K. (2022). A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges. Mechanical Systems and Signal Processing, 167, 108487. https://doi.org/10.1016/j.ymssp.2021.108487 Lima, M. C. F., de Almeida Leandro, M. E. D., Valero, C., Coronel, L. C. P., & Bazzo, C. O. G. (2020). Automatic detection and monitoring of insect pests—A review. Agriculture, 10(5), 161. https://doi.org/10.3390/agriculture10050161 Lin, M., Chen, Q., & Yan, S. (2013). Network in network. arXiv preprint arXiv:1312.4400. https://doi.org/10.48550/arXiv.1312.4400 Liu, B., Hu, Z., Zhao, Y., Bai, Y., & Wang, Y. (2019). Recognition of pyralidae insects using intelligent monitoring autonomous robot vehicle in natural farm scene. arXiv preprint arXiv:1903.10827. https://doi.org/10.48550/arXiv.1903.10827 Liu, B., Zhang, Y., He, D., & Li, Y. (2017). Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry, 10(1), 11. https://doi.org/10.3390/sym10010011 Liu, J., Li, J., Li, W., & Wu, J. (2016). Rethinking big data: A review on the data quality and usage issues. ISPRS journal of photogrammetry and remote sensing, 115, 134-142. https://doi.org/10.1016/j.isprsjprs.2015.11.006 Liu, J., & Wang, X. (2021). Plant diseases and pests detection based on deep learning: a review. Plant Methods, 17(1), 22. https://doi.org/10.1186/s13007-021-00722-9 Miranda, J. L., Gerardo, B. D., & Tanguilig III, B. T. (2014). Pest detection and extraction using image processing techniques. International Journal of Computer and Communication Engineering, 3(3), 189. https://doi.org/10.3390/agriculture10050161 Mohapatra, S., Patra, D., & Satpathy, S. (2014). An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images. Neural Computing and Applications, 24, 1887-1904. https://doi.org/10.1007/s00521-013-1438-3 Nasri, M., Baratchi, M., Tsou, Y. T., Giest, S., Koutamanis, A., & Rieffe, C. (2023). A novel metric to measure spatio-temporal proximity: a case study analyzing children’s social network in schoolyards. Applied Network Science, 8(1), 50. https://doi.org/10.1007/s41109-023-00571-6 Oerke, E.-C. (2006). Crop losses to pests. The Journal of Agricultural Science, 144(1), 31-43. https://doi.org/10.1017/s0021859605005708 Otsu, N. (1979). A threshold selection method from gray-level histograms. Automatica, 11(285-296), 23-27. Parsons, L., Ross, R., & Robert, K. (2019). A survey on wireless sensor network technologies in pest management applications. SN Applied Sciences, 2(1), 28. https://doi.org/10.1007/s42452-019-1834-0 Pipino, L. L., Lee, Y. W., & Wang, R. Y. (2002). Data quality assessment. Communications of the ACM, 45(4), 211-218. Potamitis, I., Eliopoulos, P., & Rigakis, I. (2017). Automated remote insect surveillance at a global scale and the internet of things. Robotics, 6(3), 19. https://doi.org/10.3390/robotics6030019 Puljak, L. (2016). Using social media for knowledge translation, promotion of evidence‐based medicine and high‐quality information on health. Journal of Evidence‐Based Medicine, 9(1), 4-7. https://doi.org/10.1111/jebm.12175 Rustia, D. J. A., Lin, C. E., Chung, J.-Y., Zhuang, Y.-J., Hsu, J.-C., & Lin, T.-T. (2020). Application of an image and environmental sensor network for automated greenhouse insect pest monitoring. Journal of Asia-Pacific Entomology, 23(1), 17-28. https://doi.org/10.1016/j.aspen.2019.11.006 Rustia, D. J. A., Chiu, L. Y., Lu, C. Y., Wu, Y. F., Chen, S. K., Chung, J. Y., ... & Lin, T. T. (2022). Towards intelligent and integrated pest management through an AIoT‐based monitoring system. Pest Management Science, 78(10), 4288-4302. https://doi.org/10.1002/ps.7048 Sara, U., Akter, M., & Uddin, M. S. (2019). Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study. Journal of Computer and Communications, 7(3), 8-18. https://doi.org/10.4236/jcc.2019.73002 Silveira, M., & Monteiro, A. (2009). Automatic recognition and measurement of butterfly eyespot patterns. Biosystems, 95(2), 130-136. https://doi.org/10.1016/j.biosystems.2008.09.004 Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. https://doi.org/10.48550/arXiv.1409.1556 Skendžić, S., Zovko, M., Živković, I. P., Lešić, V., & Lemić, D. (2021). The impact of climate change on agricultural insect pests. Insects, 12(5), 440. https://doi.org/10.3390/insects12050440 Susanto, A., Dewantoro, Z. H., Sari, C. A., Setiadi, D. R. I. M., Rachmawanto, E. H., & Mulyono, I. U. W. (2020, July). Shallot quality classification using HSV color models and size identification based on Naive Bayes classifier. In Journal of Physics: Conference Series (Vol. 1577, No. 1, p. 012020). IOP Publishing. https://doi.org/10.1088/1742-6596/1577/1/012020 Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition. https://doi.org/10.1109/CVPR.2015.7298594 Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018). A survey on deep transfer learning. Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27. Tan, M., & Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning. Thenmozhi, K., & Reddy, U. S. (2019). Crop pest classification based on deep convolutional neural network and transfer learning. Computers and Electronics in Agriculture, 164, 104906. https://doi.org/10.1016/j.compag.2019.104906 Torrey, L., & Shavlik, J. (2010). Transfer learning. In Handbook of research on machine learning applications and trends: algorithms, methods, and techniques (pp. 242-264). IGI global. https://doi.org/10.4018/978-1-60566-766-9.ch011 Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018). Deep learning for computer vision: A brief review. Computational intelligence and neuroscience, 2018. https://doi.org/10.1155/2018/7068349 Wang, J., Lin, C., Ji, L., & Liang, A. (2012). A new automatic identification system of insect images at the order level. Knowledge-Based Systems, 33, 102-110. https://doi.org/10.1016/j.knosys.2012.03.014 Wang, X., Wang, C., Yao, J., Fan, H., Wang, Q., Ren, Y., & Gao, Q. (2022). Comparisons of deep learning and machine learning while using text mining methods to identify suicide attempts of patients with mood disorders. Journal of affective disorders, 317, 107-113. https://doi.org/10.1016/j.jad.2022.08.054 Wang, C. Y., Bochkovskiy, A., & Liao, H. Y. M. (2023). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 7464-7475). https://doi.org/10.48550/arXiv.2207.02696 Wen, C., Wu, D., Hu, H., & Pan, W. (2015). Pose estimation-dependent identification method for field moth images using deep learning architecture. biosystems engineering, 136, 117-128. https://doi.org/10.1016/j.biosystemseng.2015.06.002 Xin, M., & Wang, Y. (2021). Image recognition of crop diseases and insect pests based on deep learning. Wireless Communications and Mobile Computing, 2021, 1-15. https://doi.org/10.1155/2021/5511676 Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., & Yang, M.-H. (2022). Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. https://doi.org/10.1109/CVPR52688.2022.00564 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94581 | - |
| dc.description.abstract | 在農作物生產中，蟲害被認為是對農業生產的最大威脅之一。它們危害農作物的生長，降低作物產量，對農業經濟收益造成重大損失。精確獲取作物栽培環境中害蟲種類與數量的資訊對於蟲害整合管理（IPM）至關重要。在之前的研究中，我們開發出一套基於深度學習方法的AIoT影像設備，專門用於通過黏蟲紙獲取的影像，利用深度學習辨識害蟲種類與數量。儘管先前已取得初步研究成果，但在灰塵和水分干擾、光照條件變化及影像模糊等挑戰下，仍可能導致害蟲的錯誤分類和數量計算不準確。為確保蟲害整合管理的有效性，本研究的目標是開發一個整體框架，用於提升害蟲檢測、分類和數據分析。我們採用YOLOv7模型來替代之前的YOLOv3-Tiny模型，專門用於黏蟲紙影像的物體檢測。結果顯示，YOLOv7模型在各個實驗場域中均達到0.95以上的mAP@.5表現。後續使用ResNet-18模型進行害蟲分類，達到整體害蟲分類模型的F1-score為0.988。此外，利用時空間分析對一段時間內蒐集的黏蟲紙影像進行害蟲種類和數量的調整。創立一個動態的二維數組來記錄在黏蟲紙影像中檢測到害蟲的位置和時間。通過多數決投票算法與對應座標的時空間分析，修正每個時間點害蟲的種類與數量，從而最大限度地減少害蟲的錯誤分類和數量計算不準確。相比於人工統計的真實數據，MAPE誤差僅約為5%。分析結果顯示，本研究所提出的框架能有效提升害蟲辨識模型效能和整體資料品質。 | zh_TW |
| dc.description.abstract | Effective integrated pest management (IPM) in crop production depends on precise information about the quantity and species of insect pests in the crop cultivation environment. In our prior work, we developed AIoT imaging devices employing a deep learning approach tailored for the automated counting and classification of insect pests through acquired images from sticky paper traps. Notwithstanding the previous progress achieved, challenges persist in mitigating issues such as interference from dust and water, variations in lighting conditions, and blurred images, ultimately resulting in the misclassification and miscounting of insect pests. To ensure the effectiveness of integrated pest management, securing accurate data on the types and quantities of insect pests present within the field is crucial. Thus, the objective of this research aims to develop a holistic framework for enhancement of insect pest detection, classification, and data analysis. This involves adopting the YOLOv7 model for object detection models specifically applied to images obtained from sticky paper traps, as a replacement for the prior approach employing the YOLOv3-tiny model. The results demonstrated that the proposed method achieved an average mAP exceeding 0.95, and employing ResNet-18 for insect pest classification, achieving an overall F1-score of 0.988. Additionally, subsequent method involves insect pest count modification using spatiotemporal analysis for a series of sticky paper trap images. A dynamic two-dimensional array was created to log the location and times at which insect pests were detected on the sticky paper trap images. Employing a voting algorithm can ascertain the definitive count of insect pests for each time interval, thereby minimizing the potential for miscounts, achieved a MAPE error of only about 5% compared to ground truth. The analysis results indicate that the proposed framework effectively enhances the insect pest recognition performance and the overall data quality. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-16T16:51:40Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-16T16:51:40Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii 英文摘要 iii Table of Contents v List of Figures viii List of Tables xi CHAPTER 1 Introduction 1 1.1 Background of the study 1 1.2 Objectives 3 CHAPTER 2 Literature Review 5 2.1 Image-based insect pest recognition techniques 5 2.1.1 Traditional image processing methods 5 2.1.2 Machine learning techniques 7 2.1.3 Deep learning techniques 9 2.2 Image quality assessment 12 2.2.1 Application in insect pest monitoring 12 2.2.2 Image curation 13 2.2.3 Image quality enhancement 14 2.2.4 Indexes of image quality assessment 15 2.3 Data quality assessment 17 2.3.1 Applications of enhancing data quality 17 2.3.2 Indexes of data quality assessment 18 2.4 Application of voting and spatiotemporal analysis 19 2.4.1 Feature extraction in machine learning 19 2.4.2 Ensemble voting mechanism 20 2.4.3 Spatiotemporal analysis 21 CHAPTER 3 Materials and Methods 23 3.1 System overview 23 3.2 System architecture 24 3.2.1 Image curation 24 3.2.2 Model training 25 3.2.3 Data analysis 29 3.3 Experimental sites 32 3.4 Data collection 38 3.4.1 Wireless sensor network module 38 3.4.2 Target insect pests 39 3.4.3 Data augmentation 40 3.5 Size feature extraction 41 3.6 Model evaluation 43 3.7 System status front-end 47 3.7.1 MQTT broker 47 3.7.2 Website 49 CHAPTER 4 Results and discussion 51 4.1 Sticky paper image curation method 51 4.1.1 Unsupervised k-means clustering 52 4.1.2 Image curation results on six experimental sites 54 4.2 Object detection model development 55 4.2.1 YOLOv3-Tiny vs. YOLOv7 56 4.2.2 YOLOv7 training results of six experimental sites 57 4.2.3 YOLOv7 training results of sites combination 59 4.2.4 YOLOv7 training results of different crop types 59 4.3 Insect pest classification model development 60 4.3.1 Classification model comparison 61 4.3.2 Results of data augmentation 64 4.3.3 Results of size feature extraction 66 4.4 Spatiotemporal algorithm analysis 67 4.4.1 Raw data analysis and ground truth 70 4.4.2 Spatiotemporal algorithm 71 4.4.3 Model evaluation and data analysis 72 4.5 Reduction of missing data 77 CHAPTER 5 Conclusions and Recommendations 79 5.1 Conclusions 79 5.2 Recommendations 80 References 81 | - |
| dc.language.iso | en | - |
| dc.subject | 影像前處理 | zh_TW |
| dc.subject | 病蟲害整合管理 | zh_TW |
| dc.subject | 時空間分佈演算法分析 | zh_TW |
| dc.subject | 害蟲分類模型 | zh_TW |
| dc.subject | 物件偵測模型 | zh_TW |
| dc.subject | object detection | en |
| dc.subject | insect pest classification | en |
| dc.subject | spatiotemporal analysis | en |
| dc.subject | image curation | en |
| dc.subject | integrated pest management | en |
| dc.title | 應用影像庋用與機器學習於智慧害蟲監測系統資料品質之提升 | zh_TW |
| dc.title | Enhancing Data Quality in Intelligent Insect Pest Monitoring System with Image Curation and Machine Learning | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 陳世芳;張仲良 | zh_TW |
| dc.contributor.oralexamcommittee | Shih-Fang Chen;Chung-Liang Chang | en |
| dc.subject.keyword | 病蟲害整合管理,影像前處理,物件偵測模型,害蟲分類模型,時空間分佈演算法分析, | zh_TW |
| dc.subject.keyword | integrated pest management,image curation,object detection,insect pest classification,spatiotemporal analysis, | en |
| dc.relation.page | 91 | - |
| dc.identifier.doi | 10.6342/NTU202404155 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-08-14 | - |
| dc.contributor.author-college | 生物資源暨農學院 | - |
| dc.contributor.author-dept | 生物機電工程學系 | - |
| 顯示於系所單位： | 生物機電工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-112-2.pdf | 4.94 MB | Adobe PDF | 檢視/開啟 |
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。