
Rice is a commodity that plays an important role in Indonesia's economy and is generally consumed by approximately 90% of the population as a staple food (Donggulo
The development of drought-tolerant varieties requires an effective and efficient screening process, which is carried out artificially or directly in the targeted environment. Moreover, the environment for artificial selection is considered easier to restrain, especially the control of stress concentration levels, which makes this method effective and efficient in the increase of the screening process (Anshori
Assessment of drought stress tolerance through screening is a common method that is often considered inaccurate. Based on the genetic constitution, drought tolerance nature is polygenic and is indicated by the QTL report that encodes these characteristics (Lanceras
Image-based phenotyping technology is currently being developed to characterize morphological and physiological responses of a plant using digital images. This technology makes it easy to calculate phenotypes by analyzing plants through images accurately in large numbers within a short time (Das Choudhury
A selection index is a breeding approach that combines several criteria in one formula (Anshori
The two experiments conducted in this study include the destructive test, which was carried out on static hydroponic (floating raft system), and the second was the non-destructive test carried out on dynamic hydroponic (deep flow technique system). These studies were carried out simultaneously in the greenhouse, Unhas Lecturer Residence, Makassar City, South Sulawesi Province, Indonesia (22.4 m above sea level [asl]) from August to November 2020.
The static hydroponic study used a split-plot design with the levels of drought as the main plot and varieties as the subplots (Fig. 1a).
However, the dynamic hydroponic used a nested design, where the replicates were nested in the drought level treatments (Fig. 1b) and the drought level factors were PEG 0%, PEG 10%, and PEG 20%. Furthermore, the varieties used were Inpari 34, IR 20 (drought-sensitive check), Salumpikit (drought-resistant check), Ciherang, and Jeliteng.
In this study, the medium used was ABmix with a concentration of 8 mL/L, and the PEG concentration treatments were gradually given to the hydroponic nutrient solution. The first stage was given 1/2 concentration of PEG treatment at the age of 13 DAS, while the second stage was given at the age of 16 DAS to prevent osmotic shock. The pH control was applied to keep it constant at the desired value, which from range 5.8 to 6.2. Meanwhile, the pH control was carried out by adding HCl or NaOH to lower or raise the pH. Data collection on the static hydroponic method was carried out 2 weeks after the treatment application or 30 DAS, while dynamic hydroponics data were collected at 68 DAS.
The observations for static hydroponic included shoot and root fresh weight (actual measurements), 2nd, 3rd, and 4th leaf length, shoot and green shoot areas from the side view, ratio of green shoot to shoot area from the side view, red, green, blue, ratio of red to green, and area growth rate. The traits measured in dynamic hydroponic included shoot and root fresh weight (actual measurements), convex hull from the top view, shoot area from the top view, green shoot area from the top view, the ratio of green shoot area to shoot area from the top view, number of leaves from the top view, red, green, blue, the ratio of red to green, the ratio of shoot area to the convex hull from the top view, object extent X from a side view, object extent Y from a side view, shoot area from a side view, green shoot area from a side view, a ratio of green shoot area to the shooting area from a side view, convex hull from a side view, and the ratio of shoot area to convex hull area from a side view (Table 1).
Table 1 . Phenotyping characters used to analyze growth and drought-related traits.
Characters | Description |
---|---|
Shoot fresh weight (SFW) | The weight of the fresh shoot at the end of observation. |
Root fresh weight (RFW) | The weight of the fresh root at the end of observation. |
2nd leaf length (LL2) | The length of the 2nd leaf from the tip of the plant. |
3rd leaf length (LL3) | The length of the 3rd leaf from the tip of the plant. |
4th leaf length (LL4) | The length of the 4thleaf from the tip of the plant. |
Shoot area (SA) | The number of pixels scaled in cm on the shoot of the plant. |
Green shoot area (GSA) | The number of pixels that have been scaled in cm in the shoot section has a hue value of 50. |
Convex hull area (CT for top and CS for side) | The smallest area is covered by theouter contour of an object. |
Object extend X (XT for top and XS for side) | The length of the X-axis of the rectangle covering the object is used to measure the width of the plant. |
Object extend Y (YT for top and YS for side) | The length of the Y-axis of the rectangle covering the object is used to measure the height of the plant. |
Number of leaves (NL) | The numberof leaf tips visible from above the plant. |
Area growth rate (AGR) | Dividing the difference in shoot area between the drought stress treatment intervals by drought stress treatment time (days). |
Red (R) | Color is measured by averaging the Red color value of all pixels in the shoot. RGB values are between 0 (no color) and 255 (maximum color). |
Green (G) | Color is measured by averaging the Green color value of all pixels in the shoot. RGB values are between 0 (no color) and 255 (maximum color). |
Blue (B) | Color is measured by averaging the Blue color value of all pixels in the shoot. RGB values are between 0 (no color) and 255 (maximum color). |
The images were taken with a Canon EOS 1200D RGB camera in a portable photo studio with a 75 cm × 75 cm × 75 cm. A white background with two 8 watt white LED lights in the studio with camera settings (5.6 F-stops, 1/160 seconds exposure time, ISO 800, and without flash) was used. The images were taken from a top view (0°) and the side view (90°) of the plant. For static hydroponic, the images were taken twice at 13 DAS (early treatment) and 30 DAS (after treatment) as shown in Fig. 2. For dynamic hydroponic, images were taken at 13 DAS (early treatment) and 68 DAS (after treatment) (Fig. 3 and the images results were analyzed using the Fiji application. Furthermore, the plant color was analyzed using hue channels with a hue 50 (green area) and the area growth rate was calculated using the projection of shoot area over two time periods.
The data were analyzed independently for variance and the characters that have significant interactions with each other were further analyzed using the Pearson correlation test and Principal Component Analysis (PCA) with STAR 2.01 software. After static hydroponic has been tested by correlation analysis, it was followed by path analysis. All further analysis based on the stress tolerance index (STI) value of each character on individual PEG concentration (0% PEG (normal) –10% PEG (drought stress) = STI 1 and 0% PEG (normal)–20% PEG (drought stress) = STI 2). This concept was reported by Anshori
Stress Tolerance Index (STI) is calculated by the equation (Fernandez 1992):
Note: Yp = The character value of each variety in normal / non-stressed conditions.
Ys = The character value of each variety in a stressed condition.
?p = Average character values of all varieties in normal / non-stressed conditions.
The selected characters from the PCA analysis results were used to obtain the index value. The validation of static hydroponic was conducted by the regression test toward the dynamic hydroponic.
In the static hydroponic method, the analysis of variance showed that the diversity of PEG concentrations had a significant effect on almost all the characters, except for the green, ratio of red to green, and the ratio of green shoot area to shoot area (Table 2).
Table 2 . Means square value of analysis of variance on the static hydroponic method.
Characters | D | V | D × V | |||
---|---|---|---|---|---|---|
SFW | 178.73 | ** | 28.92 | ** | 10.37 | ** |
RFW | 24.77 | ** | 7.3 | ** | 1.96 | ** |
LL2 | 1,486.95 | ** | 326.19 | ** | 14.68 | ns |
LL3 | 1,471.64 | ** | 466.95 | ** | 49.37 | * |
LL4 | 669.49 | ** | 188.37 | ** | 28.07 | ns |
SA | 65,556.88 | ** | 13,117.85 | ** | 3,438.49 | ** |
GSA | 61,237.72 | ** | 9,849.18 | ** | 3,261.87 | ** |
R | 3,460.07 | ** | 86.95 | ns | 748.95 | ns |
G | 879.98 | Ns | 87.35 | ns | 1,498.13 | ns |
B | 1,680.60 | * | 96.75 | ns | 272.54 | ns |
RRG | 0.6 | Ns | 0.8 | ns | 0.96 | ns |
RGSS | 0.57 | Ns | 0.02 | ns | 0.03 | ns |
AGR | 202.25 | ** | 32.11 | ** | 10.6 | ** |
D: Drought level, V: Variety, *: Significant effect at
The genotype variance analysis also showed a significant effect on the shoot and root fresh weight, 2nd, 3rd, and 4th leaf length, shoot area, shoot green area, and area growth rate. Meanwhile, the characters that were affected significantly by drought-genotype interactions included shoot and root fresh weight, 3rd leaf length, shoot area, green shoot area, and area growth rate. All characters that significantly affected the interaction effect were continued by deep analysis on the static hydroponic system. Meanwhile, the phenotype varieties on these characters are shown in Supplementary 1.
The correlation analysis on static hydroponic results was based on the static hydroponic Stress Tolerance Index (STI) value (Table 3). The results showed that the 3rd leaf length (0.769 and 0.745), shoot area (0.981 and 0.983), green shoot area (0.978 and 0.984), and area growth rate (0.981 and 0.961) had a significant correlation on the shoot and root fresh weights, respectively. These correlation analyzes were followed by independent path analysis on the shoot and root fresh weights, which were the main characters of actual morphology under artificial vegetative screening with different roles. Therefore, this independent concept analysis can determine the specific relationship among image-based phenotyping characters to the conventional morphology characters.
Table 3 . Correlation analysis of STI values on the static hydroponic method.
RFW | LL3 | SA | GSA | AGR | SFW | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
RFW | 1 | 1 | 1 | 1 | 1 | 1 | |||||
LL3 | 0.745 | ** | 0.789 | ** | 0.991 | ** | 0.977 | ** | 0.981 | ** | |
SA | 0.983 | ** | 0.748 | ** | 0.984 | ** | 0.978 | ** | |||
GSA | 0.984 | ** | 0.802 | ** | 0.981 | ** | |||||
AGR | 0.963 | ** | 0.769 | ** | |||||||
SFW | 0.982 | ** |
The numeric in table indicate the correlation value. *: Significant correlated at
Path analysis on the STI value of shoot fresh weight in static hydroponic showed representative results with a determinant coefficient value of 0.86 (Table 4).
Table 4 . Path analysis of the STI value on shoot fresh weight on the static hydroponic method.
Characters | Direct influence | Indirect influence | Residual | ||||
---|---|---|---|---|---|---|---|
RFW | LL3 | SA | GSA | AGR | |||
RFW | 0.600** | ?0.006 | ?0.108 | ?0.059 | 0.555 | 0.589 | |
LL3 | ?0.009 | 0.447 | ?0.086 | ?0.045 | 0.461 | ?0.007 | |
SA | ?0.109 | 0.59 | ?0.007 | ?0.059 | 0.567 | ?0.107 | |
GSA | ?0.060 | 0.59 | ?0.006 | ?0.108 | 0.562 | ?0.059 | |
AGR | 0.576** | 0.578 | ?0.007 | ?0.108 | ?0.059 | 0.565 |
R2: 0.86, **: Significant direct effect at
Based on this analysis, root fresh weight is the character that has the highest direct effect (0.600) on shoot fresh weight. Similarly, based on the image-based phenotyping charac-ter, the area growth rate is the character that has the highest direct effect (0.576) on shoot fresh weight. Therefore, the area growth rate directly affects the variance of shoot fresh weight as one of characters selection.
Moreover, the path analysis on the STI value of root fresh weight in static hydroponic showed representative results with a determinant coefficient value of 0.87 (Table 5). Based on this analysis, shoot fresh weight was the character that had the highest direct effect (0.548) on root fresh weight. Based on image-based phenotyping charac-ters, the shooting area (0.493) and green shoot area (0.377) were the characters that had a significant direct effect on root fresh weight. Meanwhile, the area growth rate has a negative significant direct effect (?0.417) on the root fresh weight. This showed that the high correlation of area growth rate to root fresh weight was due to the indirect effect of the shoot and green shoot areas. Therefore, shoot area and green shoot area as image-based phenotyping characters can represent the root fresh weight variance, while the area growth rate is unable to be a selection character to represent root fresh weight variance.
Table 5 . Path analysis of the STI value on root fresh weight on the static hydroponic method.
Characters | Direct influence | Indirect influence | Residual | ||||
---|---|---|---|---|---|---|---|
LL3 | SA | GSA | AGR | SFW | |||
LL3 | ?0.013 | 0.389 | 0.282 | ?0.335 | 0.421 | ?0.010 | |
SA | 0.493** | ?0.010 | 0.374 | ?0.411 | 0.538 | 0.485 | |
GSA | 0.377** | ?0.010 | 0.488 | ?0.408 | 0.536 | 0.371 | |
AGR | ?0.417** | ?0.010 | 0.485 | 0.369 | 0.537 | ?0.402 | |
SFW | 0.548** | ?0.010 | 0.484 | 0.369 | ?0.409 | 0.538 |
R2: 0.87, **: Significant direct effect at
The principal components analysis showed that there was one main component that can be used as the basis for the selection index (Table 6),
Table 6 . Principle component analysis of the STI value on the static hydroponic method.
Variables | PC1 | PC2 | PC3 | PC4 | PC5 | PC6 |
---|---|---|---|---|---|---|
RFW | 0.4162 | ?0.2267 | 0.6382 | ?0.195 | 0.4902 | 0.2996 |
SFW | 0.4183 | ?0.1602 | ?0.1349 | ?0.7478 | ?0.4166 | ?0.2201 |
LL3 | 0.3527 | 0.9258 | 0.1169 | ?0.0058 | ?0.053 | 0.0447 |
SA | 0.421 | ?0.1139 | 0.0357 | 0.3858 | 0.1809 | ?0.7918 |
GSA | 0.4177 | ?0.2224 | 0.1076 | 0.5005 | ?0.6192 | 0.3613 |
AGR | 0.4192 | ?0.0579 | ?0.7403 | 0.0585 | 0.4089 | 0.3198 |
CP | 0.9284 | 0.9881 | 0.9935 | 0.9975 | 0.9989 | 1 |
EV | 5.5707 | 0.3578 | 0.0324 | 0.0243 | 0.0083 | 0.0064 |
The numeric in table indicate the eigenvector value, CP: Cumulative proportion, EV: Eigenvalues, PC: Principal component, SFW: Shoot fresh weight, RFW: Root fresh weight, LL3: 3rd leaf length, SA: Shoot area, GSA: Green shoot area, AGR: Area growth rate.
namely the first principal component (PC1) with a cumulative proportion of 0.928 which is approximately 1. Based on PC1, shoot fresh weight (0.4183) and root fresh weight (0.4183) in drought stress were in the same direction with phenotyping characters of shoot area (0.4210), green shoot area (0.4177), and area growth rate (0.4192). These three image-based phenotyping characters were combined in a static hydroponic selection index using the formula as follows:
Static hydroponic index = 0.421 shoot area + 0.4177 shoot green area + 0.4192 growth rate area
(Equation 1)
Analysis of variance in the dynamic hydroponic method in Table 7 showed that the PEG treatment as drought stress has an impact on all image-based-phenotyping characters, except on the ratio of red to green from the top view and the ratio of green shoot area to shoot area from the top view. Variety variance has a significant effect on the number of leaves, red, and blue from the top view, the ratio of shoot area to the convex hull from the top view, object extends X, and Y from a side view, and convex hull from a side view. Meanwhile, the characters that were significantly affected by the interaction include red and blue from the top view, the ratio of shoot area to the convex hull from the top view, object extend Y from a side view, and convex hull from a side view. All characters that were significantly affected by the interaction effect in the dynamic hydroponic system were correlated with the selection index on static hydroponic. This occurred to detect the selected characters that have a relationship with the static hydroponic. The varieties phenotype on these characters are shown in Supplementary 2.
Table 7 . Means square value of analysis of variance on the dynamic hydroponic method.
Characters | D | V | D × V | |||
---|---|---|---|---|---|---|
SFW | 8,042.24 | ** | 319.95 | ns | 376.25 | ns |
RFW | 467.01 | ** | 53.23 | ns | 14.64 | ns |
CT | 81,129,688.30 | ** | 4,224,903.16 | ns | 1,562,942.10 | ns |
SAT | 5,571,152.36 | ** | 474,034.51 | ns | 97,115.97 | ns |
GSAT | 3,072,273.82 | ** | 333,330.03 | ns | 80,272.07 | ns |
NLT | 2,478.87 | ** | 617.19 | * | 156.84 | ns |
RT | 3,946.91 | ** | 462.79 | ** | 189.34 | * |
GT | 4,734.46 | ** | 241.02 | ns | 59 | ns |
BT | 505.53 | ** | 193.2 | ** | 74.72 | * |
RRGT | 0.0051 | ns | 0.0082 | ns | 0.0099 | ns |
RGSST | 0.0083 | ns | 0.0674 | ns | 0.048 | ns |
RSCT | 0.0226 | ** | 0.0085 | ** | 0.001 | * |
XS | 300.57 | ** | 174.52 | * | 66.56 | ns |
YS | 1,636.09 | ** | 147.66 | ** | 110.49 | ** |
SAS | 3,696,517.43 | ** | 280,606.15 | ns | 91,206.26 | ns |
GSAS | 1,651,359.59 | ** | 212,567.33 | ns | 68,283.65 | ns |
CS | 48,116,645.30 | ** | 6,716,755.96 | ** | 947,650.84 | * |
RGSSS | 0.0853 | * | 0.0141 | ns | 0.0082 | ns |
RSCS | 0.0463 | ** | 0.0063 | ns | 0.0024 | ns |
D: Drought level, V: Varieties, *: Significant effect at
Significant characters on dynamic hydroponic were correlated with the selection index on static hydroponic. Based on this analysis (Table 8),
Table 8 . Correlation analysis of STI values on the dynamic hydroponic method.
RT | BT | RSCT | YS | CS | SH index | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
RT | 1 | ||||||||||
BT | 0.88 | ** | 1 | ||||||||
RSCT | ?0.74 | * | ?0.84 | ** | 1 | ||||||
YS | ?0.48 | ns | ?0.24 | ns | 0.3 | ns | 1 | ||||
CS | ?0.30 | ns | ?0.03 | ns | 0.14 | ns | 0.97 | ** | 1 | ||
SH index | ?0.16 | ns | ?0.04 | ns | 0.19 | ns | 0.9 | ** | 0.91 | ** | 1 |
The numeric in table indicate the correlation value, *: Significant correlated at
the object extends Y from the side view (0.91) and convex hull from the side view (0.91) have a significant correlation with the selection index of static hydroponic. Therefore, these characters can be continued in PCA analysis to develop a selection index of a dynamic hydroponic system.
The principal components analysis on dynamic hydroponic results was shown in Table 9, which showed that PC1 has the highest cumulative proportion with a value of 58.83% of the total diversity of the initial data. Therefore, PC1 can be the basis in the weighting of selection characters such as the static hydroponic index. However, red from the top view and blue from the top view have negative eigenvectors. In contrast, the characters of the ratio of shoot area to the convex hull from the top view, object extend Y and convex hull from the side view have positive eigenvectors. This showed that the YS and CS characters were combined into a selection index in dynamic hydroponic screening using the formula stated below:
Table 9 . Principle component analysis of the STI value on the dynamic hydroponic method.
PC1 | PC2 | PC3 | PC4 | PC5 | |
---|---|---|---|---|---|
RT | ?0.5098 | 0.2535 | ?0.4404 | 0.4184 | ?0.5539 |
BT | ?0.4794 | 0.4091 | ?0.3104 | ?0.3116 | 0.6399 |
RSCT | 0.4142 | ?0.3275 | ?0.8412 | ?0.0971 | 0.0643 |
YS | 0.4516 | 0.4988 | ?0.0229 | 0.6615 | 0.3305 |
CS | 0.3671 | 0.6422 | ?0.0396 | ?0.53 | ?0.4128 |
CP | 0.5883 | 0.8544 | 0.9536 | 0.9801 | 1 |
EV | 2.9414 | 1.3306 | 0.496 | 0.1322 | 0.0997 |
The numeric in table indicate the eigenvector value, CP: Cumulative proportion, EV: Eigenvalues, PC: Principal component, RT: Red from top view, BT: Blue from top view, RSCT: Ratio of shoot area to convex hull from top view, YS: Object extend Y from side view, CS: Convex hull from side view.
Dynamic hydroponic index = 0.4516 object extend Y from the side view + 0.4177 Convex hull from the side view
(Equation 2)
The index value of each variety on static and dynamic hydroponics was shown in Table 10. Salumpikit variety at a PEG concentration of 10% (2.03) showed the highest value, while IR 20 at a PEG concentration of 20% (0.11) showed the lowest value in static hydroponics. Based on dynamic hydroponic, salumpikit variety at a PEG concentration of 10% (0.53) remained the best, while Inpari 34 (0.22) and IR 20 (0.22) were the varieties with the lowest index value. Furthermore, the regression analysis results showed that the static hydroponic index has a significant linear regression to the dynamic hydroponic index as shown in Fig. 4. Similarly, this figure also showed that the Salumpikit as the tolerant check variety has a good value index than IR 20 as a sensitive check variety on both 10% and 20% PEG.
Table 10 . Selection index on static and dynamic hydroponics.
Varieties | Treatments | SH index | DH index |
---|---|---|---|
Inpari 34 | PEG 10% | 0.61 | 0.31 |
IR 20 | PEG 10% | 0.27 | 0.23 |
Salumpikit | PEG 10% | 2.03 | 0.53 |
Ciherang | PEG 10% | 0.59 | 0.35 |
Jeliteng | PEG 10% | 0.78 | 0.34 |
Inpari 34 | PEG 20% | 0.12 | 0.22 |
IR 20 | PEG 20% | 0.11 | 0.22 |
Salumpikit | PEG 20% | 0.39 | 0.36 |
Ciherang | PEG 20% | 0.25 | 0.27 |
Jeliteng | PEG 20% | 0.16 | 0.3 |
SH index: Static hydroponic selection index, DH index, Dynamic hydroponic selection index.
The significant interaction in ANOVA is one of the early indicators in stress screening. Meanwhile, a previous study by Ak?ura and ?eri (2011); Anshori
The assessment of varieties' response to stress needs to be carried out using a tolerance index. Meanwhile, a study by Mau
The correlation and path analysis on the static hydroponic were focused on the shoot and root fresh weight characters. A previous study showed that these fresh weights have a close relationship with drought tolerance (Saha
The correlation and path analyses results on static hydroponic results showed that shoot and green shoot areas, as well as area growth rate can be used as selection criteria. This is in line with a study by Hairmansis
The principal component is a multivariate analysis that aims to extract important information from large data into a new simpler set of orthogonal variables (Ilmaniati and Putro 2019). This analysis has been reported as a weighting indicator for the selection index by Anshori
Validation of image-based phenotyping characters in static hydroponics was carried out by identifying the relationship between these characters and dynamic hydroponic characters. Relatively, dynamic hydroponic has a complex growth rate than static hydroponic (Sagita
The dynamic hydroponic ANOVA showed that red, blue, and ratio of shoot area to the convex hull from the top view, object extend Y and convex hull from a side view were the characters that were significantly affected by the interaction of drought level–genotype treatments. Meanwhile, shoot and root fresh weights showed an insignificant effect on the genotype and interaction of drought level–genotype. This was due to the high error of these characters and the variance domination from drought level treatment, hence, the genotype and its interaction are difficult to be determined on both fresh characters. Therefore, the shoot and root fresh weights are inappropriate as the main characters in detecting the selection characters from image-based phenotyping in this dynamic hydroponic system.
Moreover, the correlation analysis of image-based phenotyping characters on dynamic hydroponic that have significant interactions is a method of validating static hydroponic selection indexes. Based on the dynamic hydroponic screening correlation results, object extend Y and convex hull from the side view can be used as selection characters. This is in line with a study by Duan
Based on PCA results, the object extends Y and the convex hull from the side view has the same eigenvector direction. Meanwhile, the eigenvectors that are relatively similar in both dimensions showed the closeness of the variance between the two variables (Anshori
Therefore, the use of multivariate analysis on image-based phenotyping characters has the potential to increase the effectiveness of selection in drought stress tolerance screening. The selection index for static hydroponic is 0.421 shoot area + 0.4177 green shoot area + 0.4192 area growth rate. Also, the character selection index for dynamic hydroponic is 0.4516 objects extend Y from the side view + 0.4177 convex hull from the side view. These results showed that rice screening based on image-based phenotyping can be recommended as a more effective and efficient method for the rapid screening of drought stress.
We are grateful to Hasanuddin University for funding this research through the Penelitian Dasar UNHAS Scheme with contract number 2649/UN4.1/KEP/2020.
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