光合色素分析儀與演算法開發

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姓名

劉柏廷(Po-Ting Liu) 查詢紙本館藏

畢業系所

光電科學與工程學系

論文名稱

光合色素分析儀與演算法開發
(Photosynthesis pigment analyzer and algorithm development)

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至系統瀏覽論文 ( 永不開放)

摘要(中)

北美對於娛樂大麻與醫療大麻的推動，促使高經濟作物的種植盛行，進而帶動LED植物燈以及相關種植設備的發展與導入，加上極端天氣變化頻繁以及疫情因素，更推升高經濟作物的市場需求。本論文針對高經濟價值之食用植物為標的，希望結合感測器與AI演算法開發出智慧型非破壞手持式光合色素分析儀，以建立LED照射光譜與光合色素的關聯性。所開發出的感測裝置可應用於預估植物生長情況，提供植物工廠栽種者於種植時的參考。
本研究使用番茄、萵苣、羽衣甘藍及皇宮菜為樣本。以HPLC量測結果作為基準，比對由手持式光合色素分析儀感測得到的葉綠素a、葉綠素b、β胡蘿蔔素、葉黃素四個光合色素之比例。感測所得的訊號，透過訊號特徵提取與AI模型的建立，提高非破壞手持式光合色素分析儀的精準度，根據實驗結果顯示，本儀器所量測到的各個經濟作物之數據與HPLC所量測到的結果差異皆在10%以內，證明以光合色素分析的方法測定植物營養素的準確率高達90%以上，符合原先之設計目標。

摘要(英)

The promotion of recreational and medical marijuana in North America has spurred the widespread cμLtivation of high-value crops, driving the development and adoption of LED grow lights and related equipment. Additionally, frequent extreme weather and the pandemic have further increased market demand for these crops. This paper focuses on developing a smart, non-destructive, handheld chlorophyll analyzer using sensors and AI algorithms to establish the correlation between LED light spectra and chlorophyll content. The device can estimate plant growth conditions, providing usefμL data for plant factory growers.
Tomatoes, lettuce, kale, and choy sum were used as samples. HPLC measurements served as the benchmark, against which the proportions of chlorophyll a, chlorophyll b, beta-carotenoids, and xanthophylls measured by the handheld analyzer were compared. By extracting signal features and building AI models, the accuracy of the analyzer was improved. Experimental resμLts show that the data from the analyzer differed from HPLC resμLts by less than 10%, achieving an accuracy rate of over 90%, which meets the initial design goals.

關鍵字(中)

★ 智慧農業
★ 物聯網
★ 人工智能
★ 光合色素

關鍵字(英)

★ Smart Agriculture
★ IoT
★ AI
★ Photosynthetic Pigments

論文目次

目錄
摘要 I
ABSTRACT II
致謝 III
目錄 IV
圖目錄 VII
表目錄 XI
第一章緒論 1
1-1. 研究背景 1
1-2. 研究動機 1
1-3. 研究貢獻 2
第二章文獻回顧 3
2-1. 簡介色素分析儀 3
2-2. 現階段色素分析儀之侷限性 5
2-3. 人工智慧—機器學習 7
2-3-1. 監督式學習 9
2-3-2. 非監督式學習 10
2-3-3. 增強式學習 11
2-3-4. 半監督式學習 12
2-3-5. 梯度提升模型 13
2-3-6. 隨機森林模型 14
2-3-7. 超參數 16
第三章理論 19
3-1. 光合作用機制 19
3-2. 光合色素 22
3-2-1. 光合色素吸收光譜 24
3-2-2. 光合色素比例 24
3-3. 光合色素分析儀 27
3-4. 植物葉片吸收度量測 31
3-5. 定量分析 32
3-5-1. 植物色素萃取 32
3-5-2. 紫外/可見光譜儀 33
3-5-3. HPLC ( Jasco HPLC ) 36
3-6. 製作檢量線以獲取實際光合色素比例 39
3-7. 機器學習模型的挑選與訓練 40
第四章實驗 57
4-1. 實驗架構 57
4-2. 實驗流程 59
4-2-1. 植物葉片樣本取樣與光合色素分析儀樣品機量 59
4-2-2. 植物色素萃取 61
4-2-3. HPLC量測配置與流程 64
4-2-4. 標準品檢量線的製備 67
4-2-5. 純物質的判定 71
4-2-6. 離群值的篩選 73
4-3. 實驗結果與討論 75
4-3-1. 光合色素分析儀量測結果 75
4-3-2. 標準樣品檢量線與滯留時間量測結果 78
4-3-3. 植物樣品色素濃度量測 82
4-3-4. 數值模型建立與訓練結果 92
第五章研究結論與未來展望 124
參考文獻 126

參考文獻

參考文獻
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指導教授

張榮森(Rong-Seng Chang)

審核日期

2024-7-17

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