請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90044完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 盧虎生 | zh_TW |
| dc.contributor.advisor | Huu-Sheng Lur | en |
| dc.contributor.author | 陳明陽 | zh_TW |
| dc.contributor.author | Ming-Yang Chen | en |
| dc.date.accessioned | 2023-09-22T17:10:58Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-09-22 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-08 | - |
| dc.identifier.citation | 林奇鴻. (2019) . 超甜玉米果穗品質變化與冠層微氣象之相關性分析.臺灣大學農藝學研究所學位論文 (2019年) . https://doi.org/10.6342/NTU201901901
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(1995) . Mathematical formulae for calculating the base temperature for growing degree days. Agricultural and Forest Meteorology, 74 (1) , 61–74. https://doi.org/10.1016/0168-1923 (94) 02185-M Zhai, Z., Martínez, J. F., Beltran, V., & Martínez, N. L. (2020) . Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170, 105256. https://doi.org/10.1016/j.compag.2020.105256 Zhou, G., & Wang, Q. (2018) . A new nonlinear method for calculating growing degree days. Scientific Reports, 8 (1) , 10149. https://doi.org/10.1038/s41598-018-28392-z | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90044 | - |
| dc.description.abstract | 智慧農業結合資通訊技術與實際資料的使用，有望提升農民在氣候變遷下的調適能力。甜玉米的適採期的預測相當關鍵，太晚採收會使品質降低。氣溫是影響作物生長最重要的氣象因子，而生育度日 (GDD) 是用來評估溫度對於作物生長反應的重要工具。近期GDD模式則逐漸重視高溫對於作物的影響，但仍缺乏對於個別作物品種的參數校正，在預測甜玉米的生育期的誤差過大。為此，本研究提出了一套窮舉演算法來最佳化作物參數，參數最佳化模式的表現相較傳統GDD10,30算法，採收期預測日數與實際日數的R2自0.69 – 0.79提升到0.93 – 0.96，RMSE也從5.56至8.60天減少至2.39至3.90天。本研究並利用經最佳化的參數及GDD算法，發展出一套網路甜玉米決策支援系統，系統開發過程使用網路程式設計工具及開源氣象資料，設計出一套使用者友善的決策支援系統，可於手機及電腦上使用。其預測能力優於現行臺灣良好農業規範的生育日數法，及農試單位常用之GDD10,30生育度日法，解決傳統生育度日模式無法精確預測甜玉米生育期的問題。此外，使用者亦可自訂參數，未來若搭配實際物候資料與參數最佳化演算法校正，有潛力套用在不同作物的物候預測上。在氣候變遷下，提供農民、企業、學術及農政單位一個快速評估作物在不同氣候條件下的生長反應的工具。 | zh_TW |
| dc.description.abstract | Integration of information and communication technology (ICT) and real data in intelligence agriculture holds the potential to enhance farmers' adaptive capacity under climate change. Predicting the optimal harvesting period for sweet corn is crucial as late harvesting can result in reduced quality. Temperature is the most important weather factor influencing crop growth, and growing degree days (GDD) is an important tool for assessing the temperature response of crops. Recent GDD models have started considering the impact of high temperatures on crops but still lack parameter calibration for individual crop varieties, resulting in significant errors in predicting the growth stages of sweet corn. Therefore, this study proposes an exhaustive search method to optimize crop parameters, and the performance of the parameter optimization model is compared to the traditional GDD10,30 algorithm. The R2 value for predicting the harvesting period improved from 0.69-0.79 to 0.93-0.96, and the root mean square error (RMSE) decreased from 5.56-8.60 days to 2.39-3.90 days. Using the optimized parameters and GDD algorithm, a web-based decision support system for sweet corn was developed. The system utilizes web programming tools and open-source meteorological data to create a user-friendly decision support system that can be accessed through mobile phones and computers. The predictive capability of the system surpasses the current Taiwan Good Agricultural Practice's method for calculating growing days and the commonly used GDD10,30 method employed by agricultural research units, addressing the issue of inaccurate prediction of sweet corn growth stages by traditional growing degree day models. Additionally, users can customize the parameters, and future applications may include the calibration of actual phenological data and parameter optimization algorithms for different crops' phenological predictions. In the face of climate change, this system provides farmers, businesses, academia, and agricultural authorities with a tool to quickly assess crop growth responses under different climate conditions. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-22T17:10:58Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-09-22T17:10:58Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員審定書 II
誌謝 III 中文摘要 IV 英文摘要 V 目錄 VII 圖目錄 X 表目錄 XI 第一章 前言 1 1.1 研究動機 1 1.2 研究問題與假說 2 1.3 研究流程與預期成果 3 第二章 文獻回顧 5 2.1 臺灣甜玉米生產概況與挑戰 5 2.2 溫度與生育度日 6 2.3 改良版生育度日演算法 10 2.4 智慧農業簡介 12 2.5 農業決策支援系統簡介 14 2.6 以網路為本的農業決策支援系統 17 2.7 小結 20 第三章 研究方法 21 3.1 使用資料及演算法 21 3.1.1 氣象資料取得與處理 21 3.1.2 甜玉米各生育期栽培管理建議 22 3.1.3 以窮舉法最佳化作物參數 23 3.2 生育度日算法與生育期判斷 26 3.3 本系統架構與使用者介面設計 28 3.4 開發工具與系統建置 29 3.4.1 HTML/CSS/JavaScript/jQuery 29 3.4.2 Bootstrap 30 3.4.3 Highchart 30 3.4.4 PHP 30 3.4.5 Python 31 3.4.6 MySQL 31 3.5 網站部屬 31 第四章 結果 32 4.1 窮舉法校正後的作物參數 32 4.2 白美人的採收日期驗證 35 4.3 系統功能概述 36 4.4 生育期推估及栽培建議 39 4.5 自訂參數模式 41 第五章 討論 43 5.1 不同季節與年份的生育情形 43 5.1.1 2020年夏秋季與冬春季的比較 43 5.1.2 2016年、2019年、2022年春季的生育推估比較 44 5.1.3 GDD10,30線性模式與最佳化線性模式的比較 46 5.2 與類似系統的比較 47 5.2.1 祕魯的香蕉GDD系統 47 5.2.2 農業小幫手—積溫計算器 48 5.2.3 農業小幫手—作物生育期推估系統 50 5.2.4 U2U玉米GDD 52 5.3 系統的限制 55 5.4 系統的貢獻 56 第六章 結論與未來展望 59 第七章 參考文獻 60 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 決策支援系統 | zh_TW |
| dc.subject | 生育度日 | zh_TW |
| dc.subject | 甜玉米 | zh_TW |
| dc.subject | 氣候變遷 | zh_TW |
| dc.subject | 物候學 | zh_TW |
| dc.subject | Sweet Corn | en |
| dc.subject | Growing Degree Days | en |
| dc.subject | Climate Change | en |
| dc.subject | Decision Support System | en |
| dc.subject | Phenology | en |
| dc.title | 甜玉米生育期推估及栽培管理之網路決策支援系統 | zh_TW |
| dc.title | Web-based Decision Support System for Sweet Corn Growth Stage Estimation and Production Plans Development | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 李紅曦;姚銘輝;劉力瑜;王淑珍 | zh_TW |
| dc.contributor.oralexamcommittee | Hung-Hsi Lee;Ming-Hwi Yao;Li-Yu Liu;Shu-Jen Wang | en |
| dc.subject.keyword | 決策支援系統,甜玉米,生育度日,氣候變遷,物候學, | zh_TW |
| dc.subject.keyword | Decision Support System,Sweet Corn,Growing Degree Days,Climate Change,Phenology, | en |
| dc.relation.page | 65 | - |
| dc.identifier.doi | 10.6342/NTU202303709 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-08-10 | - |
| dc.contributor.author-college | 生物資源暨農學院 | - |
| dc.contributor.author-dept | 農藝學系 | - |
| 顯示於系所單位： | 農藝學系 | |
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