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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89335完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 顏炳郎 | zh_TW |
| dc.contributor.advisor | Ping-Lang Yen | en |
| dc.contributor.author | 孫意珺 | zh_TW |
| dc.contributor.author | Yi-Jun Sun | en |
| dc.date.accessioned | 2023-09-07T16:34:47Z | - |
| dc.date.available | 2025-08-04 | - |
| dc.date.copyright | 2023-09-11 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-03 | - |
| dc.identifier.citation | 林思妤、杜元凱、游舜期、傅月英、林大鈞。2021。植物表型體分析平台於蔬菜自動外表型分析之應用。台灣農業研究 70(1): 11-23。
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Mortensen. 1995. Color Indices for Weed Identification Under Various Soil, Residue, and Lighting Conditions. Journal of the ASABE. Wang, B., Y. Ding, C. Wang, D. Li, H. Wang, Z. Bie, Y. Huang and S. Xu. 2022. G-ROBOT: An intelligent greenhouse seedling height inspection robot. Journal of Robotics . Yau, W. K., O.-E. Ng and S. W. Lee. 2021. Portable device for contactless, non-destructive and in situ outdoor individual leaf area measurement. Computers and Electronics in Agriculture 187: 106278. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89335 | - |
| dc.description.abstract | 目前現有的表型平台大多應用於糧食及蔬果作物為主,然而在蔬菜類作物中,較少應用於葉菜類作物,葉菜類作物生長通常相對容易且生長週期短,提供快速的農作物供應。若能夠建立針對葉菜類作物之表型平台,針對作物生長和表現的全面監測與評估,便能提高葉菜類作物的生產效率和品質。本研究開發天車與無人地面載具作物表型平台用於量測葉菜類作物之株高及葉面積,其架構主要分為三個部分,天車與無人地面載具表型平台、深度校正與株高及葉面積估測演算法。天車與無人地面載具表型平台使用RGB-D相機收集RGB及深度影像,然而所收集之深度影像,會因為環境亮度等原因導致誤差,因此本研究預先建立深度校正查找表,對相機量測到之深度進行校正。株高及葉面積估測演算法,以AR marker作為參考基準,首先,透過影像處理技術將AR marker及植株進行分割,再透過演算法將株高及葉面積估算出來。本研究分別使用天車及無人地面載具對小白菜及青江菜之株高及葉面積進行調查,天車之株高RMSE為0.99 cm,葉面積RMSE為5.532cm^2 ; 無人地面載具之株高RMSE為1.365 cm,葉面積RMSE分別為6.969cm^2。結果顯示,本研究天車及無人地面載具表型平台可以針對株高及葉面積進行估測。 | zh_TW |
| dc.description.abstract | The current existing phenotyping platforms are mostly applied on food, vegetable and fruit crops, however, in vegetable crops, it is less used in leafy vegetable crops. Leafy vegetable crops are usually relatively easy to grow and have a short growth cycle, which provide rapid crop supply. If phenotyping platform for leafy vegetable crops can be developed to monitor and evaluate crop growth and performance, the production efficiency and quality of crops can be improved. This study develops crop phenotype platform for crane and unmanned ground vehicle (UGV) to measure plant height and leaf area of leafy vegetable crops. The architecture is divided into three parts: the crane and UGV phenotype platform; the depth calibration; plant height and leaf area estimation algorithm. Crane and UGV phenotyping platforms are used to move and capture RGB and depth images. The depth images will have errors due to environmental brightness and some other elements, therefore, this study intend to reduce the errors by utilizing depth calibration lookup table. The plant height and leaf area estimation algorithm use the AR marker as reference. The AR marker and the plant are segmented with image processing approach, while the plant height and leaf area are estimated through the algorithm. In this study, the crane and UGV were used to investigate the plant height and leaf area of Bokchoy and Spoon Cabbage. The measurement of plant height RMSE of crane is 0.99 cm, and the leaf area RMSE is 5.532cm^2, meanwhile, RMSE of UGV is 1.365 cm, and the leaf area RMSE is 6.969cm^2. The results show that the phenotype platform of the crane and UGV in this study can estimate the plant height and leaf area. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-07T16:34:47Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-09-07T16:34:47Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝 i
中文摘要 ii Abstract iii 目錄 v 圖目錄 viii 表目錄 x 第一章 緒論 1 1.1 前言 1 1.2 研究動機及目的 3 1.3 論文架構 4 第二章 文獻探討 5 2.1 UGV表型量測平台 5 2.1.1 UGV與光學雷達(Light Detection and Ranging, LiDAR) 5 2.1.2 UGV與RGB-D相機 7 2.1.3 UGV 與飛時測距相機(Time of Flight, TOF) 9 2.1.4 UGV 與立體視覺相機(Stereo Camera) 10 2.2 UAV表型量測平台 11 2.2.1 UAV 與RGB影像 11 2.3 基於軌道表型量測平台 13 2.3.1 龍門與RGB-D相機 13 2.3.2 龍門與光學雷達(LiDAR)及RGB 相機 16 第三章 天車及UGV表型平台 21 3.1 硬體架構 21 3.1.1 天車硬體配置 21 3.1.2 UGV硬體配置 24 3.1.3 株高手臂 26 3.1.4 Intel RealSense D435i RGB-D 27 3.1.5 AR marker 標定桿 28 3.2 軟體開發環境 28 3.2.1 開發環境 29 3.2.2 函式庫及安裝包 29 3.3 使用者介面 30 3.3.1 天車使用者介面 30 3.3.2 UGV使用者介面 32 3.4 天車系統運作 33 3.4.1 上層控制 33 3.4.2 底層控制 34 3.5 UGV系統運作 36 3.5.1 上層控制 36 3.5.2 底層控制 36 第四章 深度誤差校正 37 4.1 RealSense D435i 深度量測原理 37 4.2 深度誤差校正 38 4.2.1 系統誤差校正 38 4.2.2 非系統誤差校正 39 第五章 表型估測演算法 44 5.1 影像處理 44 5.1.1 數據型態 44 5.1.2 形態學運算 (Image Morphology) 45 5.1.3 乘冪率轉換 (Power-Law Transformation) 46 5.1.4 過綠指數 (Excess Green Index) 47 5.1.5 SLIC演算法(Simple Linear Iterative Clustering) 47 5.1.6 K -means分群演算法(KMC) 48 5.2 AR marker 分割 49 5.3 植株分割 50 5.4 株高估測模型 52 5.5 葉面積估測模型 53 5.5.1 估測方法 53 第六章 實驗及結果與討論 56 6.1 實驗架設 56 6.1.1 實驗場域 56 6.1.2 實驗架設 56 6.1.3 數據分析 57 6.2 結果與討論 59 6.2.1 估測株高與人為量測結果 59 6.2.2 估測葉面積與葉面積儀量測結果 61 6.2.3 鮮重與葉面積比較結果 62 6.3 不同系統之比較 64 6.3.1 株高量測比較 64 6.3.2 葉面積量測比較 64 6.4 天車成本 67 第七章 結論與未來展望 68 7.1 結論 68 7.2 未來展望 69 參考文獻 71 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 葉面積估測 | zh_TW |
| dc.subject | 表型平台 | zh_TW |
| dc.subject | 深度校正 | zh_TW |
| dc.subject | 機器視覺 | zh_TW |
| dc.subject | 株高估測 | zh_TW |
| dc.subject | Depth Calibration | en |
| dc.subject | Phenotyping Platform | en |
| dc.subject | Leaf Area Estimation | en |
| dc.subject | Plant Height Estimation | en |
| dc.subject | Machine Vision | en |
| dc.title | 開發天車與無人地面載具表型平台於溫室葉菜作物株高及葉面積估測 | zh_TW |
| dc.title | Development of Crane and Unmanned Ground Vehicle Phenotyping Platform for Estimation of Plant Height and Leaf Area of Leafy Vegetable Crops in Greenhouse | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 劉力瑜;林淑怡;黃國益;李汪盛 | zh_TW |
| dc.contributor.oralexamcommittee | Li-Yu Liu;Shu-I Lin;Kuo-Yi Huang;Wang-Sheng Li | en |
| dc.subject.keyword | 表型平台,深度校正,機器視覺,株高估測,葉面積估測, | zh_TW |
| dc.subject.keyword | Phenotyping Platform,Depth Calibration,Machine Vision,Plant Height Estimation,Leaf Area Estimation, | en |
| dc.relation.page | 74 | - |
| dc.identifier.doi | 10.6342/NTU202302733 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-08-08 | - |
| 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 | |
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