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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 林恩仲 | zh_TW |
| dc.contributor.advisor | En-Chung Lin | en |
| dc.contributor.author | ALBERT FRESNIDO ASTILLERO | zh_TW |
| dc.contributor.author | ALBERT FRESNIDO ASTILLERO | en |
| dc.date.accessioned | 2024-02-22T16:10:57Z | - |
| dc.date.available | 2024-02-23 | - |
| dc.date.copyright | 2024-02-20 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-01-30 | - |
| dc.identifier.citation | Adão, T., Hruška, J., Pádua, L., Bessa, J., Peres, E., Morais, R., Sousa, J., 2017. Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry. Remote Sens. 9, 1110. https://doi.org/10.3390/rs9111110.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91678 | - |
| dc.description.abstract | 飼草是反芻動物營養的重要組成部分，依賴適當的牧場管理。然而，不當的管理會導致過度放牧和牧場生產力的總體下降，從而影響動物的表現。為了防止這種情況發生，畜牧農民需要及時和相關的飼草產量和質量信息。傳統的飼草產量和質量測量方法費時、勞動密集，並且對更廣泛的區域精度較低。利用無人機進行這種應用越來越受歡迎，因為它提供了更多的操作靈活性。然而，先前的研究由於使用覆雜的工作流程和非常昂貴的設備，其應用仍然有限。因此，有必要開展研究，開發一種準確而簡單、使用更便宜的工具、計算效率更高的工作流程。在這項研究中，使用了一架配備RGB相機的DJI Mavic 3 Classic 無人機獲取圖像。從這些圖像中提取了植被指數作為預測變量，開發了一個用於預測飼草產量和質量的多項式多元回歸模型。結果顯示，通過結合線性、二次和三次植被指數，可以從20米的高度高度預測鮮重和幹物質，調整後的R2分別為0.71和0.66。然而，預測粗蛋白和中性洗滌纖維含量的準確性較低，調整後的R2分別為0.48和0.53。這表明了一種替代簡單工作流程、成本效益工具和統計方法在Brachiaria humidicola牧場產量預測中的潛力。 | zh_TW |
| dc.description.abstract | Forages are a significant component of ruminant nutrition, which relies on proper pasture management. However, improper management results in overgrazing and an overall decline in pasture productivity, causing poor animal performance. To prevent this, timely and relevant information on forage yield and quality is necessary for the livestock farmer. Traditional ways of measuring forage yield and quality are laborious, time-consuming, and less accurate for wider areas. Using drones for this application is gaining popularity because it offers more operational flexibility. Previous research still has limited application because, despite promising results, they used complicated workflows and very expensive equipment. Thus, there is a need for research to develop a workflow that is accurate yet simple, uses cheaper tools, and is computationally efficient. In this research, a DJI Mavic 3 Classic drone with an RGB camera was used to obtain images. Vegetation indices were extracted from these images as predictors to develop a polynomial multiple regression model for the prediction of forage yield and quality. Results showed that fresh and dry matter can be highly predicted from 20-meter altitude with an adjusted R2 of 0.71 and 0.66, respectively by combining linear, quadratic, and cubic vegetation indices. However, the predictors have lower accuracy in predicting content of crude protein and neutral detergent fiber, with an adjusted R2 of 0.48 and 0.53, respectively. This demonstrates the potential of an alternative simple workflow, cost-effective tools, and statistical method for yield prediction in Brachiaria humidicola pastures. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-02-22T16:10:56Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-02-22T16:10:57Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledgement ii
Abstract iii Table of Contents iv List of Tables vii List of Figures viii List of Appendices x Abbreviations xi Chapter 1. Introduction 1 Chapter 2. Review of Related Literature 3 2.1. Industry situation in the Philippines 3 2.2. Brachiaria humidicola and its importance in pasture and ruminant production 4 2.3. Forage yield and quality estimation methods: traditional to drone-based method 5 2.4. Drone platform 8 2.5. Camera or sensors 9 2.6. Image pre-processing 12 2.7. Extraction of vegetation indices 14 2.8. Data analysis and prediction models 17 2.9. Forage sampling and laboratory analysis as ground truth 18 2.10. Knowledge gap, challenges, and opportunities 19 2.11. Objectives of the study 20 Chapter 3. Materials and Methods 21 3.1. Study area 21 3.2. Experimental layout 22 3.3. Drone image collection 25 3.4. Forage sampling and laboratory analysis 26 3.5. Image processing 29 3.6. Merging of the field, laboratory, and image datasets 32 3.7. Statistical modeling 34 Chapter 4. Results 38 4.1. Image dimension and camera settings 38 4.2. Image radiometric quality 38 4.3. Correlation analysis 40 4.4. Optimum regression model 40 4.5. Fixed effect of different sampling areas on the optimum regression model 46 4.6. Effect of single vs combined altitude datasets on the optimum regression model 47 4.7. Prediction accuracy by comparing measured vs predicted values 48 Chapter 5. Discussions 51 5.1. Drone, sensor characteristics and settings effect on image quality 51 5.2. Image processing workflow 52 5.3. Random forage sampling and field work efficiency 53 5.4. Regression model accuracy and implications 54 Chapter 6. Conclusion and Perspective 59 References 61 Appendices 80 Appendix A. Experimental workflow 80 Appendix B. Experimental layout and random forage sampling points in Area A 80 Appendix C. Experimental layout and random forage sampling points in Area B 81 Appendix D. Experimental layout and random forage sampling points in Area C 81 Appendix E. Correlation between the X and Y variables at 20-m altitude 82 Appendix F. Correlation between the X and Y variables at 25-m altitude 82 Appendix G. Correlation between the X and Y variables at 30-m altitude 82 Appendix H. Scatterplot between the FM and VI’s at 20 m altitude, (a) FM vs ExG, (b) FM vs ExGR, (c) FM vs ExR, (d) FM vs GLI, (e) FM vs MGRVI, (f) FM vs NGBI, (g) FM vs NGRDI, (h) FM vs RGBVI 83 Appendix I. Scatterplot between the DM and VI’s at 20 m altitude, (a) DM vs ExG, (b) DM vs ExGR, (c) DM vs ExR, (d) DM vs GLI, (e) DM vs MGRVI, (f) DM vs NGBI, (g) DM vs NGRDI, (h) DM vs RGBVI 84 Appendix J. Number of samples, mean plant height, age of grass, and fertilizer management in the study area 85 Appendix K. DJI Mavic 3 classic drone and camera specifications 85 Appendix L. Temperature and humidity during the activity in the field 85 | - |
| dc.language.iso | en | - |
| dc.title | 無人機RGB影像衍生植生指數預測Humidicola (Brachiaria humidicola)產量和品質 | zh_TW |
| dc.title | Drone RGB Image-Derived Vegetation Indices to Predict Yield and Quality in Humidicola (Brachiaria humidicola) Grass | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | Thomas Banhazi;馬宇安 | zh_TW |
| dc.contributor.oralexamcommittee | Thomas Banhazi;Yu-An Ma | en |
| dc.subject.keyword | 牧草質量,飼草質量,無人機,RGB相機,植被指數,多元回歸,Brachiaria humidicola, | zh_TW |
| dc.subject.keyword | forage yield,forage quality,drone,RGB camera,vegetation indices,multiple regression,Brachiaria humidicola, | en |
| dc.relation.page | 85 | - |
| dc.identifier.doi | 10.6342/NTU202400366 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-02-01 | - |
| dc.contributor.author-college | 共同教育中心 | - |
| dc.contributor.author-dept | 全球農業科技與基因體科學碩士學位學程 | - |
| Appears in Collections: | 全球農業科技與基因體科學碩士學位學程 | |
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