Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/96452
標題: 設施園藝作物蒸發散模式應用於智慧灌溉之研究
The Study on Evapotranspiration Model of Intelligent Irrigation Management for Horticultural Crop in Protected Culture
作者: 陳令錫
Ling-Hsi Chen
關鍵字: 蒸發散量
光積值
蒸氣壓差
灌溉
人工智慧
類神經網路
微氣候
evapotranspiration
light integral value
vapor pressure deficit
irrigation
artificial intelligent
neural network
microclimate
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摘要: 基於水分平衡的蒸發散量(evapotranspiration, ET)節水灌溉技術為世界趨勢,本研究採蒸滲儀的重量法連結資料紀錄器監測園藝作物生長期重量變化與微氣候數據,該重量數據代表蒸發散量與灌溉操作之內涵與變化趨勢,將灌溉數據過濾平滑化後的數據視為蒸發散量之測量樣本,分成訓練與驗證數據集,線性迴歸比較5個自變數(Rn, VPD, T, RH, Ws)或2個自變數(Rn, VPD)估測ET之結果,R2均在80%以上;單變數Rn或VPD個別之解釋ET的估測性能也在80%以上,而單變數T或RH個別解釋ET的估測性能則降低到70%以下,顯示用單變數T或RH個別估測ET有不穩定的估測結果。溫室內風速較低,對估測ET的影響小。單獨蒸氣壓差(vapor pressure deficit, VPD)與ET有好的線性相關R2約88.4%,地域差異不顯著;而單獨太陽輻射則有地域的差異,太陽輻射對ET的影響溫帶地區弱、亞熱帶台灣則強。 經過各年度試驗,獲得臺灣自然通風溫室內之洋桔梗、番茄、小黃瓜成熟植株單株之日蒸發散量,蒸發散量在陰雨天隨雲層厚薄而改變,此結果可供作合理節水灌溉之控制決策依據。在晴天未達光飽和點條件下,番茄與小黃瓜的蒸發散量也隨太陽輻射強度與蒸氣壓差之升高而增加。 人工智慧(AI)類神經網路之估測效率用均方誤差(mean square error, MSE)呈現。用AI與迴歸估測ET,在訓練數據集之RMSE為0.597 (MSE為0.356)比5個自變數線性迴歸的RMSE約0.380為大;而測試數據集為0.530 (MSE為0.281)大於5個自變數的0.281,顯示用AI估測ET效果優異。 根據試驗結果觀察,研發適於當前臺灣農情的光積值灌溉技術,特色為低的購置與維護成本、類比於蒸發散量技術之效果、晴天足量灌溉、陰雨天智慧地自動減量灌溉,並且在晴天中午豔陽下較密集灌溉,發揮適時適量合理灌溉的特性。
The irrigation technology with evapotranspiration (ET), base on the water balance, is the global tendency for water-saving. This study adapted the lysimeter theory and utilized the weighing balance, data logger and weather sensors for monitoring of seasonal horticulture. The weighing raw data were constituted with two components: evapotranspiration and irrigation operation. The irrigation component was replaced by nearby average values with data smoothing technique to become the target evapotranspiration samples. The target evapotranspiration samples were divided into training data sets and testing data sets. The regression of R2 for estimating dependent variable ET of five independent variables (Rn, VPD, T, RH, Ws) or two independent variables (Rn, VPD) or single independent variables either Rn or VPD are all R2 above 80%. However, the R2 value of the single independent variables either T or RH was lower than 70%, that indicated the estimating ET performance was unstable. The wind speed inside greenhouse was low, so that its effect of estimating ET was small. Using a single VPD parameter to estimat ET has good linear correlation, the R2 was 88.4%. The effect of different regions are not significant. However, the single Rn to estimat ET was affected with different regions. The reason could be explained that the weak Rn for temper region and strong Rn for subtropical weather of Taiwan. The plants' ET data are the basic information for rationally irrigation decision strategy. The evapotranspiration of single plant of eustoma, tomato and cucumber were conducted for several years' experiments. ET of tomato and cucumber plant are increase with accumulation of solar radiation intensity and VPD under light saturation point on sunny days. ET will decrease with the change of cloud thickness. Mean square error (MSE) value was used to estimate the performance of artificial intelligent (AI). In training data set, the RMSE of AI is 0.597(MSE=0.356). The value was bigger than 0.380 of the RMSE of five independent variables with linear regression. In testing data set, the RMSE of AI was 0.530(MSE=0.281). The value was bigger than 0.281 of the five independent variables with linear regression. It shows AI has excellent eatimation ET performance. According to the experimental results, development of the Light Accumulation Irrigation Trigger Device (LAITD) was suitable for Taiwan farming. The features of LAITD are low installation and maintenance cost, approach to the ET technology, sufficient irrigation on sunny day, automatic reduced irrigation on raining day, and intensively irrigation around midday when high light shining. The LAITD enhance to a timely, appropriate and rational irrigation technique.
URI: http://hdl.handle.net/11455/96452
文章公開時間: 2021-08-01
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