2024-03-26
【學術亮點】探索使用多個光譜反射指數建立溫室番茄早期乾旱逆境預測模型之高效率方法
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【學術亮點】探索使用多個光譜反射指數建立溫室番茄早期乾旱逆境預測模型之高效率方法
Intelligent Detection: Development of expert diagnosis system for crop cultivation and management【Department of Agronomy / Bo-Jein Kuo / Professor】
智慧檢測：作物栽培管理專家診斷系統開發【農藝系郭寶錚教授】
上架日期2023/12/7
Intelligent Detection: Development of expert diagnosis system for crop cultivation and management【Department of Agronomy / Bo-Jein Kuo / Professor】
智慧檢測：作物栽培管理專家診斷系統開發【農藝系郭寶錚教授】
| 論文篇名 | 英文：Exploring Efficient Methods for Using Multiple Spectral Reflectance Indices to Establish a Prediction Model for Early Drought Stress Detection in Greenhouse Tomato 中文：探索使用多個光譜反射指數建立溫室番茄早期乾旱逆境預測模型之高效率方法 |
| 期刊名稱 | Horticulturae |
| 發表年份, 卷數, 起迄頁數 | 2023, 9(12), 1317 |
| 作者 | Shih-Lun Fang; Yu-Jung Cheng; Yuan-Kai Tu; Min-Hwi Yao; Kuo, Bo-Jein(郭寶錚)* |
| DOI | 10.3390/horticulturae9121317 |
| 中文摘要 | 早期偵測乾旱逆境對於溫室番茄（Solanum lycopersicum）的栽培是一個重要議題。通過光譜學，可以實現植物水分狀態的即時和非破壞性評估。然而，光譜數據通常受到共線性、類別不平衡和類重疊等問題的困擾，這需要一些有效的策略來克服。本研究使用了番茄（‘Rosada’品種）營養生長期的光譜數據集，計算十個光譜反射指數（spectral reflectance indices, SRIs），以開發溫室番茄的早期乾旱偵測模型。此外，本研究應用了隨機森林（random forest, RF）演算法和兩種重抽樣技術來探索分析多個SRI數據的高效率方法。結果發現，使用RF演算法建立預測模型可以克服數據的共線性。此外，合成少類別過抽樣技術在數據不平衡時能提高模型性能。對於高維度資料中的類重疊，本研究建議首先篩選出兩到三個重要的預測變數，然後使用散佈圖來判斷是否應該處理類重疊問題。最後，本研究提出一個基於三個SRIs（即RNDVI、SPRI和SR2）的RF模型，該模型只需收集六個光譜波段（即510、560、680、705、750和900納米）的數據就能實現超過85%的準確性。該模型可望成為溫室番茄精準灌溉的一個有用且具成本效益的工具，其感測器原型可以在未來的不同情境中進行開發和測試。 |
| 英文摘要 | Early detection of drought stress in greenhouse tomato (Solanum lycopersicum) is an important issue. Real-time and nondestructive assessment of plant water status is possible by spectroscopy. However, spectral data often suffer from the problems of collinearity, class imbalance, and class overlap, which require some effective strategies to overcome. This study used a spectroscopic dataset on the tomato (cv. ‘Rosada’) vegetative stage and calculated ten spectral reflectance indices (SRIs) to develop an early drought detection model for greenhouse tomatoes. In addition, this study applied the random forest (RF) algorithm and two resampling techniques to explore efficient methods for analyzing multiple SRI data. It was found that the use of the RF algorithm to build a prediction model could overcome collinearity. Moreover, the synthetic minority oversampling technique could improve the model performance when the data were imbalanced. For class overlap in high-dimensional data, this study suggested that two to three important predictors can be screened out, and it then used a scatter plot to decide whether the class overlap should be addressed. Finally, this study proposed an RF model for detecting early drought stress based on three SRIs, namely, RNDVI, SPRI, and SR2, which only needs six spectral wavebands (i.e., 510, 560, 680, 705, 750, and 900 nm) to achieve more than 85% accuracy. This model can be a useful and cost-effective tool for precise irrigation in greenhouse tomato production, and its sensor prototype can be developed and tested in different situations in the future. |
| 發表成果與AI計畫研究主題相關性 | 番茄是世界上主要的作物之一，而及早偵測乾旱逆境對於番茄栽培具有重要幫助。透過光譜技術可以對植物水分狀態進行即時、非破壞性的評估，但光譜資料時常存在共線性、類別不平衡和類重疊等問題，因而限制了其應用性。本研究結合隨機森林演算法和重抽樣技術，提出分析高維度光譜資料的有效策略，並以之開發出適用於溫室玉女番茄的早期乾旱偵測模型，此模型僅需6個光譜波段即可達到85%以上的準確度。本研究的成果是智慧栽培的一部分，為溫室番茄生產提供了一個有效的早期乾旱檢測工具，有助於實現溫室灌溉的更加精準和智慧化。 |
