2024-03-26
【學術亮點】結合遞迴神經網絡和S形生長模型進行塑膠布溫室的溫度短期預報及番茄生長預測
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【學術亮點】結合遞迴神經網絡和S形生長模型進行塑膠布溫室的溫度短期預報及番茄生長預測
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2024/2/27
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：Combining Recurrent Neural Network and Sigmoid Growth Models for Short-Term Temperature Forecasting and Tomato Growth Prediction in a Plastic Greenhouse 中文：結合遞迴神經網絡和S形生長模型進行塑膠布溫室的溫度短期預報及番茄生長預測 |
| 期刊名稱 | Horticulturae |
| 發表年份, 卷數, 起迄頁數 | 2024, 10(3), 230 |
| 作者 | Yi-Shan Lin, Shih-Lun Fang , Le Kang, Chu-Chung Chen ,Min-Hwi Yao, Kuo, Bo-Jein(郭寶錚)* |
| DOI | 10.3390/horticulturae10030230 |
| 中文摘要 | 相較於露地栽培，溫室可透過環境控制為作物提供有利的生長條件。預測溫室微氣候是減少環境監測成本的一種方式。本研究使用了多個遞迴神經網絡模型，包括長短期記憶（long short-term memory, LSTM）、閘式遞迴單元以及雙向LSTM，以不同數量的隱藏層數和神經元數目建立塑膠布溫室的溫度預報模型。為了評估所提出的模型之泛化能力，我們使用最準確的預報模型來預測具有不同規格的溫室內部的溫度。經過四個月的測試期間，該最佳模型獲得的決定係數、平均絕對百分誤差和均方根誤差分別為0.962、3.216%和1.196 °C。隨後，溫度預報模型的輸出被用來計算生長度日（growing degree days, GDDs），而預測出的GDDs更進一步被用作S形生長模型的輸入變數來模擬番茄的葉面積指數、果實鮮重和地上部乾物重。本研究建立的三種性狀之生長模型的決定係數均高於0.80。此外，使用溫度預報模型預測出的GDD所建立的生長模型之參數估計值和以感測器實際觀測到的GDD所建立之模型十分接近。以上結果表示本研究所提出的溫度預報模型能夠準確地預測溫室內的溫度變化，並且預報結果可用於預測溫室番茄的生長。 |
| 英文摘要 | Compared with open-field cultivation, greenhouses can provide favorable conditions for crops to grow through environmental control. The prediction of greenhouse microclimates is a way to reduce environmental monitoring costs. This study used several recurrent neural network models, including long short-term memory (LSTM), gated recurrent unit, and bi-directional LSTM, with varying numbers of hidden layers and units, to establish a temperature forecasting model for a plastic greenhouse. To assess the generalizability of the proposed model, the most accurate forecasting model was used to predict the temperature in a greenhouse with different specifications. During a test period of four months, the best proposed model’s R2, MAPE, and RMSE values were 0.962, 3.216%, and 1.196 °C, respectively. Subsequently, the outputs of the temperature forecasting model were used to calculate growing degree days (GDDs), and the predicted GDDs were used as an input variable for the sigmoid growth models to simulate the leaf area index, fresh fruit weight, and aboveground dry matter of tomatoes. The R2 values of the growth model for the three growth traits were all higher than 0.80. Moreover, the fitted values and the parameter estimates of the growth models were similar, irrespective of whether the observed GDD (calculated using the actual observed data) or the predicted GDD (calculated using the temperature forecasting model output) was used. These results indicated that the proposed temperature forecasting model could accurately predict the temperature changes inside a greenhouse and could subsequently be used for the growth prediction of greenhouse tomatoes. |
| 發表成果與AI計畫研究主題相關性 | 溫室可透過環境控制為作物提供有利的生長條件。經四個月的測試，該最佳模型獲得的決定係數、平均絕對百分誤差和均方根誤差分別為0.962、3.216%和1.196 °C。隨後，溫度預報模型的輸出被用來計算生長度日（growing degree days, GDDs），而預測出的GDDs更進一步被用作S形生長模型的輸入變數來模擬番茄的葉面積指數、果實鮮重和地上部乾物重。本研究的成果是智慧栽培的一部分，本研究所提出的溫度預報模型能夠準確地預測溫室內的溫度變化，並且預報結果可用於預測溫室番茄的生長，有助於實現溫室栽培更加精準與智慧化。 |
