• Journal of Bio-Environment Control
• /
• v.32 no.4
• /
• pp.384-395
• /
• 2023

Since transpiration plays a key role in optimal irrigation management, knowledge of the irrigation demand of crops like tomatoes, which are highly susceptible to water stress, is necessary. One way to determine irrigation demand is to measure transpiration, which is affected by environmental factor or growth stage. This study aimed to estimate the transpiration amount of tomatoes and find a suitable model using mathematical and deep learning models using minute-by-minute data. Pearson correlation revealed that observed environmental variables significantly correlate with crop transpiration. Inside air temperature and outside radiation positively correlated with transpiration, while humidity showed a negative correlation. Multiple Linear Regression (MLR), Polynomial Regression model, Artificial Neural Network (ANN), Long short-term Memory (LSTM), and Gated Recurrent Unit (GRU) models were built and compared their accuracies. All models showed potential in estimating transpiration with R² values ranging from 0.770 to 0.948 and RMSE of 0.495 mm/min to 1.038 mm/min in the test dataset. Deep learning models outperformed the mathematical models; the GRU demonstrated the best performance in the test data with 0.948 R² and 0.495 mm/min RMSE. The LSTM and ANN closely followed with R² values of 0.946 and 0.944, respectively, and RMSE of 0.504 m/min and 0.511, respectively. The GRU model exhibited superior performance in short-term forecasts while LSTM for long-term but requires verification using a large dataset. Compared to the FAO56 Penman-Monteith (PM) equation, PM has a lower RMSE of 0.598 mm/min than MLR and Polynomial models degrees 2 and 3 but performed least among all models in capturing variability in transpiration. Therefore, this study recommended GRU and LSTM models for short-term estimation of tomato transpiration in greenhouses.

• Journal of Korean Society of Forest Science
• /
• v.70 no.1
• /
• pp.7-16
• /
• 1985

To grasp canonical correlations, their related backgrounds in various growth factors of stem, the characteristics of stem by synthetical dispersion analysis, principal component analysis and canonical correlation analysis as optimum method were applied to Larix leptolepis. The results are as follows; 1) There were high or low correlation among all factors (height ($x_1$), clear height ($x_2$), form height ($x_3$), breast height diameter (D. B. H.: $x_4$), mid diameter ($x_5$), crown diameter ($x_6$) and stem volume ($x_7$)) except normal form factor ($x_8$). Especially stem volume showed high correlation with the D.B.H., height, mid diameter (cf. table 1). 3) (1) Canonical correlation coefficients and canonical variate between stem volume and composite variate of various height growth factors ($x_1$, $x_2$ and $x_3$) are ${\gamma}_{u1,v1}=0.82980^{**}$, $\{u_1=1.00000x_7\\v_1=1.08323x_1-0.04299x_2-0.07080x_3$. (2) Those of stem volume and composite variate of various diameter growth factors ($x_4$, $x_5$ and $x_6$) are ${\gamma}_{u1,v1}=0.98198^{**}$, $\{{u_1=1.00000x_7\\v_1=0.86433x_4+0.11996x_5+0.02917x_6$. (3) And canonical correlation between stem volume and composite variate of six factors including various heights and diameters are ${\gamma}_{u1,v1}=0.98700^{**}$, $\{^u_1=1.00000x_7\\v1=0.12948x_1+0.00291x_2+0.03076x_3+0.76707x_4+0.09107x_5+0.02576x_6$. All the cases showed the high canonical correlation. Height in the case of (1), D.B.H. in that of (2), and the D.B.H, and height in that of (3) respectively make an absolute contribution to the canonical correlation. Synthetical characteristics of each qualitative growth are largely affected by each factor. Especially in the case of (3) the influence by the D.B.H. is the most significant in the above six factors (cf. table 2). 3) Canonical correlation coefficient and canonical variate between composite variate of various height growth factors and that of the various diameter factors are ${\gamma}_{u1,v1}=0.78556^{**}$, $\{u_1=1.20569x_1-0.04444x_2-0.21696x_3\\v_1=1.09571x_4-0.14076x_5+0.05285x_6$. As shown in the above facts, only height and D.B.H. affected considerably to the canonical correlation. Thus, it was revealed that the synthetical characteristics of height growth was determined by height and those of the growth in thickness by D.B.H., respectively (cf. table 2). 4) Synthetical characteristics (1st-3rd principal component) derived from eight growth factors of stem, on the basis of 85% accumulated proportion aimed, are as follows; Ist principal component ($z_1$): $Z_1=0.40192x_1+0.23693x_2+0.37047x_3+0.41745x_4+0.41629x_5+0.33454x_60.42798x_7+0.04923x_8$, 2nd principal component ($z_2$): $z_2=-0.09306x_1-0.34707x_2+0.08372x_3-0.03239x_4+0.11152x_5+0.00012x_6+0.02407x_7+0.92185x_8$, 3rd principal component ($z_3$): $Z_3=0.19832x_1+0.68210x_2+0.35824x_3-0.22522x_4-0.20876x_5-0.42373x_6-0.15055x_7+0.26562x_8$. The first principal component ($z_1$) as a "size factor" showed the high information absorption power with 63.26% (proportion), and its principal component score is determined by stem volume, D.B.H., mid diameter and height, which have considerably high factor loading. The second principal component ($z_2$) is the "shape factor" which indicates cubic similarity of the stem and its score is formed under the absolute influence of normal form factor. The third principal component ($z_3$) is the "shape factor" which shows the degree of thickness and length of stem. These three principal components have the satisfactory information absorption power with 88.36% of the accumulated percentage. variance (cf. table 3). 5) Thus the principal component and canonical correlation analyses could be applied to the field of forest measurement, judgement of site qualities, management diagnoses for the forest management and the forest products industries, and the other fields which require the assessment of synthetical characteristics.