Ecological Agriculture: Development of Precision Cultivation and Management Technology【Department of Agronomy / Kuo, Bo-Jein / Professor】
生態農業：農業精準栽培管理技術開發【農藝學系郭寶錚教授】
論文篇名 A 1D-SP-Net to determine early drought stress status of tomato (Solanum lycopersicum) with imbalanced Vis/NIR spectroscopy data
期刊名稱 AGRICULTURE
發表年份,卷數,起迄頁數 2022, 12(2), 259
作者 Tu, Yuan-Kai; Kuo, Chin-En; Fang, Shih-Lun; Chen, Han-Wei; Chi, Ming-Kun; Yao, Min-Hwi; Kuo, Bo-Jein(郭寶錚)*
DOI 10.3390/agriculture12020259
中文摘要 早期偵測作物逆境發生對於穩定作物生產是相當重要的。本研究利用Vis/NIR光譜資料建構一非破壞性、穩健的1維卷積神經網絡 (one dimensional convolutional neural networks, 1D-CNN) 模型用以判別番茄 (Solanum lycopersicum) 早期乾旱逆境生理狀態。為避免梯度消失以及強化特徵辨識能力，本研究併入殘差學習 (residual learning) 與全局 (global context) 模塊於1D-CNN架構中，整體模型命名為1D spectrogram power net (1D-SP-Net)。結果顯示1D-SP-Net整體模型表現優於淨最小平方法 (partial least squares discriminant analysis, PLSDA) 與隨機森林 (random forest, RF)，其中accuracy可達96.3%、precision為98.0%、Matthew’s correlation coefficient為0.92，F1 score則為0.95。進一步比較個模型在使用各種不平衡程度資料下表現，1D-SP-Net在使用不同程度不平衡資料狀況下仍可保持穩健的模型預測能力。上述結果說明本研究所提出之1D-SP-Net可準確的判斷番茄早期乾旱逆境生理狀態，並且較不受資料不平衡狀況影響。
英文摘要 Detection of the early stages of stress is crucial in stabilizing crop yields and agricultural production. The aim of this study was to construct a nondestructive and robust method to predict the early physiological drought status of the tomato (Solanum lycopersicum); for this purpose, a convolutional neural network (CNN)-based model with a one-dimensional (1D) kernel for fitting the visible and near infrared (Vis/NIR) spectral data was proposed. To prevent degradation and enhance the feature comprehension of the deep neural network architecture, residual and global context modules were embedded in the proposed 1D-CNN model, yielding the 1D spectrogram power net (1D-SP-Net). The 1D-SP-Net outperformed the 1D-CNN, partial least squares discriminant analysis (PLSDA), and random forest (RF) models in model testing, demonstrating an accuracy of 96.3%, precision of 98.0%, Matthew’s correlation coefficient of 0.92, and an F1 score of 0.95. Furthermore, when employing various synthesized imbalanced data sets, the proposed 1D-SP-Net remained robust and consistent, outperforming the other models in terms of the prediction capabilities. These results indicate that the 1D-SP-Net is a promising model resistant to the effects of imbalanced data sets and able to determine the early drought stress status of tomato seedlings in a non-invasive manner.
發表成果與本中心研究主題相關性 利用1D-SP-Net模式可準確的判斷番茄早期乾旱逆境生理狀態，可達到永續農業創新發展之目的。