Plant growth is constantly affected by biotic and abiotic stresses, which are especially expressed in plant leaves. Therefore, leaf phenotype is considered to be an important indicator of phenotypic plasticity in plants. The effects of various growth environmental factors on the final size of
Keywords
 humidity
 image analysis
 light intensity
 rosette leaves
 temperature
Plant phenotype is the result of the interaction between genotype and growth environments. Studying the phenotype of plants under various growth environments is the core of understanding plant function. Recent research indicates that in the context of rapid climate change, phenotypes play a key role in plant adaptation to various growth factors (Hu et al. 2008; de Jong et al. 2012; Gratani et al. 2014; Scharr et al. 2016), and biotic and abiotic stresses lead to phenotypic changes in plants. Therefore, identifying the plant phenotypic signatures under biotic or abiotic stress conditions is helpful for the early detection of biotic or abiotic stresses, which have great economic benefits for plant production (Pauli et al. 2016).
Leaves are the energy factories of plants and play an important role in the process of plant survival and growth. Through photosynthesis, leaves convert solar into chemical energy, which can then be used for further metabolism and ultimately in the production of food, feed, and fuel (Xu et al. 2009; Rodriguez et al. 2014). Since most photosynthesis occurs in leaves, it is important to characterize them in terms of size, shape, and number, which are regulated by the growth environment and genetic factors (Gonzalez et al. 2010, 2012; Mishra et al. 2012; Weraduwage et al. 2015). Many functional genomic studies have been carried out to improve agricultural and forestry crops using highthroughput genomic tools. However, there are relatively few studies on the phenotypic characteristics (e.g., the size and morphological structure of organs) of plants with specific genotypes under various growth conditions (Rahaman et al. 2015). Therefore, the study of the relationships between phenotypes, genes, and environments should be expanded. It will deepen our understanding of the relationships between the observable plant phenotypes and their physiological status, and the effects of different growth environments on plant growth, yield, and quality (Ke 2014; Orgogozo et al. 2015).
Studying the effects of environmental factors on plant phenotype is helpful in understanding the biological functions of plant development, serving in breeding to develop cultivars with ideal phenotypes. However, the majority of experiments testing the response of plants to changes in environmental conditions have focused on a single stress treatment applied to plants under controlled conditions (Wang & Zhou 2021). In contrast, in practice, a number of different stresses can occur simultaneously. These may include irradiance, temperature, humidity, or water availability and may alter plant metabolism in a novel manner that may be different from that caused by each of the different stresses applied individually.
Earlier studies did not analyze the effect of different growth environments (temperature, humidity, light intensity) on
Using image analysis to nondestructively analyze plant leaf size in greenhouses, a number of studies have been conducted (Fahlgren et al. 2015; Ge et al. 2016). Compared to destructive sampling, image analysis enables the measurements of leaf size multiple times during the plant growth cycle, and also allows the quantification of dynamic traits such as growth rate and leaf expansion rate (Liang et al. 2018).
The aim of this paper was to study the effect of environmental factors of temperature, humidity, and light intensity on the phenotypic characteristics of
From November 2015 to December 2016, the area of individual leaves and the number of leaves of
Trial environment parameters of each group
Trial number  Trial factors  Trial factors  

temperature (°C)  humidity (%)  light intensity (μmol·m^{−2}·s^{−1})  
1  22  50  278  22 °C; 50%; 278 μmol·m^{−2}·s^{−1} 
2  22  65  92  22 °C; 65%; 92 μmol·m^{−2}·s^{−1} 
3  22  80  184  22 °C; 80%; 184 μmol·m^{−2}·s^{−1} 
4  25  50  184  25 °C; 50%; 184 μmol·m^{−2}·s^{−1} 
5  25  65  278  25 °C; 65%; 278 μmol·m^{−2}·s^{−1} 
6  25  80  92  25 °C; 80%; 92 μmol·m^{−2}·s^{−1} 
7  28  50  92  28 °C; 50%; 92 μmol·m^{−2}·s^{−1} 
8  28  65  184  28 °C; 65%; 184 μmol·m^{−2}·s^{−1} 
9  28  80  278  28 °C; 80%; 278 μmol·m^{−2}·s^{−1} 
MATLAB (version R2010, MathWorks) was used for image analysis. Rosette leaves of
The smallest leaf was visible when the individual leaf area was approximately 0.5 mm^{2} (Cookson et al. 2010). In this paper, the number of leaves was calculated every 2 days during the period from two visible leaves to bolting.
Individual leaf area and leaf numbers were analyzed and fitted using a logistic model and a linear regression model. The regression equations between leaf area, leaf number, and growth time were established by SPSS software (IBM SPSS statistics 19.0). The equations of the models were as follows (Cookson et al. 2010; Karadavut et al. 2010; Jiao et al. 2018):
The final sizes of the leaf area under different growth environments were determined according to the established growth equations. Then, the effects of various growth environments on the final leaf size and the leaf number were analyzed.
Leaf 6 (the leaf number was determined by the order of the emergence) of each plant was taken as the target to analyze the effects of growth environments on the final leaf size of
It could be learned from Figure 2 that in the early stage, the growth and increase of leaf area were faster than in the maturation stage, producing a sigmoid curve. Moreover, the fitted values of leaf 6 agreed well with the measured values, with no obvious outliers. The results of regression analysis between leaf area and growth time (equation 2) were given in Table 2. The final size of leaf 6 for each experiment was determined by the parameter
Regression equations of leaf number under various growth environments (temperature; humidity; light intensity)
Trial factors  Growth regression equation  Trial factors  Growth regression equation 

22 °C; 50%; 278 μmol·m^{−2}·s^{−1}  N = 0.68*t + 0.17  22 °C; 80%; 184 μmol·m^{−2}·s^{−1}  N = 0.65*t + 0.40 
22 °C; 65%; 92 μmol·m^{−2}·s^{−1}  N = 0.47*t + 0.22  25 °C; 65%; 278 μmol·m^{−2}·s^{−1}  N = 0.68*t + 0.80 
25 °C; 50%; 184 μmol·m^{−2}·s^{−1}  N = 0.67*t + 0.99  25 °C; 80%; 92 μmol·m^{−2}·s^{−1}  N = 0.45*t + 0.61 
28 °C; 80%; 278 μmol·m^{−2}·s^{−1}  N = 0.71*t + 0.74  28 °C; 65%; 184 μmol·m^{−2}·s^{−1}  N = 0.57*t + 0.63 
28 °C; 50%; 92 μmol·m^{−2}·s^{−1}  N = 0.38*t + 1.53 
The regression equations of the number of leaves per rosette with time were established using equation (3). The trends of the leaf number of
Range analysis and variance analysis of the orthogonal test were conducted to evaluate the effect of various environmental factors (temperature, humidity, and light intensity) on the individual leaf final size and the number of rosette leaves of
Range analysis results of the final size and the number of the rosette leaves of
Trial number  Trial factors  Estimated results  

Final leaf size per mm^{2}  Leaf number  
temperature (°C)  humidity (%)  light intensity (μmol·m^{−2}·s^{−1})  
Trial design  1  22  50  278  129.03  21 
2  22  65  92  102.06  16  
3  22  80  184  104.26  18  
4  25  50  184  174.31  15  
5  25  65  278  95.99  15  
6  25  80  92  85.90  14  
7  28  50  92  84.75  12  
8  28  65  184  125.30  11  
9  28  80  278  139.18  12  
Final leaf size  335.35  388.09  272.71  
356.2  323.35  403.87  
349.23  329.34  364.21  

111.78  129.36  90.90  

118.73  107.78  134.62  

116.41  109.78  121.40  
Range 
6.95  21.58  43.72  
Leaf number analysis  55  48  42  
44  42  44  
35  44  48  

18  16  14  

15  14  15  

12  15  16  
Range 
6  2  2 
Analysis of variance for the final rosette leaf size of
Source  SS  DF  MS  

75.11  2  37.56  0.026  
853.19  2  426.60  0.30  
3018.77  2  1509.39  1.06  
2860.54  2  1430.27  
6807.61  8 
Analysis of variance for the number of rosette leaves of
Source  SS  DF  MS  

66.89  2  33.45  42.88  **  
6.22  2  3.11  3.99  
6.22  2  3.11  3.99  
1.56  2  0.78  
80.89  8 
The increase in the individual leaf area resulted in the increase in the rosette area, which intensified the interception and utilization of light energy and provided higher aboveground biomass. It could be concluded by range analysis (Table 3) that the order of environmental factors affecting the final size of
The increase in the leaf number caused an increase in the rosette area. It could be concluded by range analysis that the order of environmental factors affecting the leaf number of
In order to intuitively analyze the rules and trends of the effect of environmental factors on the individual leaf size of
In Figure 4, the final size of leaf 6 reached its maximum value at 25 °C, and increased by 5.85%, and 1.95% compared with that at 22 °C and 28 °C, respectively. With the increase in temperature, the final leaf size exhibited little difference. This means that the effect of temperature on the final leaf size was not significant. When the relative humidity increased from 50% to 65%, the final size of leaf 6 decreased by 16.68%, and when the relative humidity increased from 65% to 80%, the final size of this leaf increased by 1.82%. Therefore, a low humidity (50%) was beneficial to the growth and development of leaf 6, and high humidity (80%) inhibited the growth. Compared with 184 μmol·m^{−2}·s^{−1}, the final size of leaf 6 was reduced by 32.84% and 9.82% at 92 and 278 μmol·m^{−2}·s^{−1}, respectively, which indicated that the final size of this leaf decreased significantly under lowlight conditions.
In Figure 4, the number of leaves was reduced by 19.97% and 38.19% at 25 °C and 28 °C, respectively, compared with 22 °C. At the relative humidity of 65%, the number of leaves increased by 12.5% and 6.67% compared with 50%, and 80%, respectively. When the light intensity increased from 92 μmol·m^{−2}·s^{−1} to 184 μmol·m^{−2}·s^{−1} and 278 μmol·m^{−2}·s^{−1}, the number of leaves increased by 6.67% and 6.25%, respectively. The analysis of the number of leaves under the influence of various environmental factors showed that the influence of temperature was significant, while the influence of humidity and light intensity was small.
The optimum combination of environmental factors could be determined by analyzing the above test results, as shown in Table 6. The optimization conditions of the final leaf size and leaf number obtained by individual leaf analysis were inconsistent. Therefore, the optimum environmental parameters for the growth of
Optimization table for the environmental factors
Characteristic  Combination of optimal growth conditions 

Final leaf size  25 °C, 50% humidity, 184 μmol·m^{−2}·s^{−1} 
Leaf number  22 °C, 50% humidity, 278 μmol·m^{−2}·s^{−1} 
The temperature was the major factor influencing the leaf number, but for the final leaf size, it was a secondary factor. Therefore, the temperature of 22 °C was selected as the optimum growth temperature according to the test result of leaf number. The effects of humidity on the final leaf size and leaf number were both the secondary factor, and the value of 50% was selected as the optimum growth humidity. Light intensity was the major factor affecting the final leaf size, but for the leaf number, it was a secondary factor. Therefore, the light intensity 184 μmol·m^{−2}·s^{−1} was selected as the optimum growth according to the test result of the final leaf size. Finally, the optimum growth environments for the rosette leaves growth of
The main motivation for the present experiments was to study how the temperature, relative humidity, and light intensity affected the final leaf size and leaf number of
The dynamics of individual leaf area and leaf number as a function of time were analyzed and fitted by the statistical analysis method.
In this paper, an effective method of phenotypic assessment was developed, which can be helpful in optimizing phenotyping procedures applicable for breeding new cultivars adapted to given climatic conditions, or to select genotypes with the desired traits suitable for cultivation under specific conditions.
Range analysis results of the final size and the number of the rosette leaves of A. thaliana
Trial number  Trial factors  Estimated results  

Final leaf size per mm^{2}  Leaf number  
temperature (°C)  humidity (%)  light intensity (μmol·m^{−2}·s^{−1})  
Trial design  1  22  50  278  129.03  21 
2  22  65  92  102.06  16  
3  22  80  184  104.26  18  
4  25  50  184  174.31  15  
5  25  65  278  95.99  15  
6  25  80  92  85.90  14  
7  28  50  92  84.75  12  
8  28  65  184  125.30  11  
9  28  80  278  139.18  12  
Final leaf size  335.35  388.09  272.71  
356.2  323.35  403.87  
349.23  329.34  364.21  

111.78  129.36  90.90  

118.73  107.78  134.62  

116.41  109.78  121.40  
Range 
6.95  21.58  43.72  
Leaf number analysis  55  48  42  
44  42  44  
35  44  48  

18  16  14  

15  14  15  

12  15  16  
Range 
6  2  2 
Analysis of variance for the final rosette leaf size of A. thaliana
Source  SS  DF  MS  

75.11  2  37.56  0.026  
853.19  2  426.60  0.30  
3018.77  2  1509.39  1.06  
2860.54  2  1430.27  
6807.61  8 
Analysis of variance for the number of rosette leaves of A. thaliana
Source  SS  DF  MS  

66.89  2  33.45  42.88  **  
6.22  2  3.11  3.99  
6.22  2  3.11  3.99  
1.56  2  0.78  
80.89  8 
Trial environment parameters of each group
Trial number  Trial factors  Trial factors  

temperature (°C)  humidity (%)  light intensity (μmol·m^{−2}·s^{−1})  
1  22  50  278  22 °C; 50%; 278 μmol·m^{−2}·s^{−1} 
2  22  65  92  22 °C; 65%; 92 μmol·m^{−2}·s^{−1} 
3  22  80  184  22 °C; 80%; 184 μmol·m^{−2}·s^{−1} 
4  25  50  184  25 °C; 50%; 184 μmol·m^{−2}·s^{−1} 
5  25  65  278  25 °C; 65%; 278 μmol·m^{−2}·s^{−1} 
6  25  80  92  25 °C; 80%; 92 μmol·m^{−2}·s^{−1} 
7  28  50  92  28 °C; 50%; 92 μmol·m^{−2}·s^{−1} 
8  28  65  184  28 °C; 65%; 184 μmol·m^{−2}·s^{−1} 
9  28  80  278  28 °C; 80%; 278 μmol·m^{−2}·s^{−1} 
Regression equations of leaf number under various growth environments (temperature; humidity; light intensity)
Trial factors  Growth regression equation  Trial factors  Growth regression equation 

22 °C; 50%; 278 μmol·m^{−2}·s^{−1}  N = 0.68*t + 0.17  22 °C; 80%; 184 μmol·m^{−2}·s^{−1}  N = 0.65*t + 0.40 
22 °C; 65%; 92 μmol·m^{−2}·s^{−1}  N = 0.47*t + 0.22  25 °C; 65%; 278 μmol·m^{−2}·s^{−1}  N = 0.68*t + 0.80 
25 °C; 50%; 184 μmol·m^{−2}·s^{−1}  N = 0.67*t + 0.99  25 °C; 80%; 92 μmol·m^{−2}·s^{−1}  N = 0.45*t + 0.61 
28 °C; 80%; 278 μmol·m^{−2}·s^{−1}  N = 0.71*t + 0.74  28 °C; 65%; 184 μmol·m^{−2}·s^{−1}  N = 0.57*t + 0.63 
28 °C; 50%; 92 μmol·m^{−2}·s^{−1}  N = 0.38*t + 1.53 
Optimization table for the environmental factors
Characteristic  Combination of optimal growth conditions 

Final leaf size  25 °C, 50% humidity, 184 μmol·m^{−2}·s^{−1} 
Leaf number  22 °C, 50% humidity, 278 μmol·m^{−2}·s^{−1} 