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J. People Plants Environ > Volume 27(6); 2024 > Article
Ha, Jeong, and Kwack: Non-destructive Detection of Growth and Quality in Basil Seedlings Grown in a Plant Factory

ABSTRACT

Background and objective: Basil is one of the high-value crops cultivated in plant factories. The production of uniform and healthy seedlings has a direct impact on the yield and quality of the final harvest. The objectives of this study were; (1) to ascertain whether non-destructive detecting parameters [projected canopy area (PCA) and vegetation indices (VIs)] could predict changes in growth and quality of basil seedlings, and (2) to determine the feasibility of grading basil seedlings based on PCA to establish a baseline for stable yields after transplanting.
Methods: Basil seedlings were grown in 2- and 3-day irrigation cycles for 25 days after sowing, and the growth parameters and image analysis parameters using a multispectral camera were examined at regular intervals. The correlations between growth parameters and PCA/VIs were investigated to detect growth and quality of basil seedlings. At the time of transplanting, the basil seedlings were classified into grades A–D based on their PCA values, and the growth and yield after transplanting of basil seedlings in each grade were evaluated.
Results: The basil seedlings in the 3-day irrigation treatment showed higher growth, and the correlation between the PCA values and the leaf area and fresh weight resulted in a coefficient of determination greater than 0.93. Among the VIs, the VARI, GI, and NGRDI were correlated with the growth and quality with coefficients of determination greater than 0.6. And, the growth and yield after transplanting were dependent on the seedling grade based on PCA values at the time of transplanting.
Conclusion: This study confirmed that it is possible to predict the growth and quality of basil seedlings using non-destructive image analysis, and that the grading criteria for basil seedlings that can be expected to produce stable yields after transplanting can be determined using image analysis.

Introduction

Changing climates, including extreme weather, drought, and typhoon, are making it increasingly difficult to cultivate crops in open fields (Mendelsohn and Dinar, 1999; Soussana et al., 2010). Therefore, crop production systems are needed that are not affected by the external environment and can produce crops of consistent quality throughout the year. Plant factory is a vertical, multi-layer cultivation system with artificial light, and has also been used in polar regions and space industry (Kitaya, 2019; McCartney and Lefsrud, 2018). Furthermore, as consumer interest in sustainable agriculture and environmental concern intensifies, the popularity of plant factories that do not utilize pesticides and can reduce environmental pollution with a closed system is on the rise. As a result, plant factories that can consistently produce high-quality crops can serve as an environmentally friendly solution for future food production (Csambalik et al., 2023).
Basil is a member of the Lamiaceae family, native to tropical Asia, and an herb with a distinctive flavor. The distinctive flavor and aroma are attributed to the accumulation of phenolic compounds, which is why it is used as a seasoning vegetable in cooking. Additionally, it is extracted for its essential oil and employed as an ingredient in cosmetics (Avetisyan et al., 2017). To ensure that the highest quality basil is harvested, researches are conducted to improve flavor and increase yields in plant factories (Carvalho et al., 2016; Chutimanukul et al., 2022; Dou et al., 2018; Rihan et al., 2020).
In commercial plant factories, leafy greens or high-value crops are typically cultivated (Goto, 2012; Shimizu et al., 2011), and it is crucial to produce crops of consistent quantity and quality throughout the production period. The production of robust seedlings can increase yields after transplanting and optimize the efficiency with which resources and space are used in plant factories. However, the selection of healthy seedlings in most plant factories is currently based on the experience and judgement of the workers, rather than on objective criteria. The most accurate method for distinguishing between healthy and unhealthy seedlings is to employ destructive measurement of seedling growth characteristics. However, destructive measurement has the limitation of reducing merchantability and not being able to accomplish a large amount of work (Ihuoma and Madramootoo, 2017).
Imaging systems can be used to non-destructively detect crop growth using color, shape, and reflectance information (Clemente et al., 2023; Guo and Tan, 2015; Xue and Su, 2017). Image measurement techniques can be categorized into RGB, multispectral, and hyperspectral image analysis techniques depending on the type of camera used (Sugiura et al., 2000). Recently, crop phenotyping using images for non-invasive research and crop stress analysis using spectral reflectance information have been studied (Ban et al., 2023; Ihuoma and Madramootoo, 2019; Swe et al., 2023). The application of image measurement technology to crop analysis has the potential to reduce labor and costs and facilitate the production of uniform and high-quality crop.
This study was conducted with the following objectives: (1) to ascertain whether non-destructive detecting parameters [projected canopy area (PCA) and vegetation indices (VIs)] could accurately predict changes in growth and quality of basil seedlings, and (2) to determine the feasibility of grading basil seedlings based on PCA to establish a baseline for stable yields after transplanting.

Research Methods

Plant materials and seedling production

Sweet basil (Ocimum basilicum L.) was used as the plant material in this study, and the seeds were sown in 'Growfoam' media (Smithers-Oasis Korea Co., Ltd., Chunam, Republic of Korea) in 128 cell trays. The basil seedlings were cultivated for 25 days after sowing (DAS) in the plant factory at the University of Seoul. The air temperature and relative humidity were maintained at 24/22°C and 65 ± 5%, respectively. White LEDs (Futuregreen Co., Ltd., Yongin, Republic of Korea) were used as a light source in the plant factory, and the light spectrum of white LED was shown in Fig. 1. The light intensity and photoperiod were 200 μmol·m−2·s−1 and 16/8 h, respectively. We established the treatments with two and three days of sub-irrigation cycles to vary the range of size and water content in basil seedlings. The irrigation cycles were determined The Yamazaki nutrient solution with pH 6.0 and EC 1.0 dS·m−1 was used for sub-irrigation.
The growth of basil seedling was measured destructively at 2–3 day intervals from 11 DAS when the seedlings reached a size that could be accurately measured with a leaf area meter. The plant height, number of leaves, leaf area, SPAD value, and shoot fresh weight were investigated, and the leaf area and SPAD value were measured using a leaf area meter (LI-3100, LI-COR Inc., Lincoln, NE, USA) and chlorophyll meter (SPAD-502PLUS, KONICA MINOLTA, Inc., Tokyo, Japan), respectively.

Basil cultivation after transplanting

At 25 DAS, the basil seedlings were transplanted into the growing plates (60 × 60 cm) with a plant density of 33.3 plants·m−2. The basil plants were cultivated for 15 days after transplanting, and the environment conditions were maintained at the same settings as those employed during the period of seedling production, with the exception of the light intensity which was increased to 220 μmol· m−2·s−1. The DFT hydroponic system was applied for the basil cultivation after transplanting using the Yamazaki nutrient solution with pH 6.0 and EC 1.5 dS·m−1. During the cultivation after transplanting, we investigated destructively the growth of basil plants including the plant height, leaf length, number of nodes and leaves, leaf area, SPAD value, and fresh and dry weight of shoot at 5 day intervals.

Collecting and analysis of image data

At each time point when the growth of basil was measured destructively, the image data was collected the plant image measurement system (PIMS). The PIMS was equipped with a multispectral camera (FS-3200T-10GE-NNC, JAI, Copenhagen, Denmark) and LEDs with 450, 550, 650, 750, and 830 nm affixed to the ceiling, with the distance between the tray and the multispectral camera set at 1.5m. The PCA value was calculated by taking into account the resolution and number of pixels in the image obtained from the top view after the vegetation was separated from the background using the ENVI program (ENVI 5.3, L3Harris Geospatial, Broomfield, CO, USA). Also, we calculated the vegetation indices using the reflectance of basil plants at five different wavelengths. The list of vegetation indices used in this experiment are shown in Table 1. The vegetation indices were correlated with the growth parameters such as shoot fresh weight (FW), leaf area index (LAI), and the indicator of water content (canopy water content, CWC) of basil plants. The CWC was obtained LAI was calculated by dividing the leaf area by the plug tray area, as shown in the following equation:
CWC=Plantwatercontent×LAIPlantwatercontent(%)=FW-DWFW×100

Grading of basil seedlings based on PCA

At 25 DAS, the basil seedlings were divided into four grades (A–D) based on PCA values, which can be used to non-destructively predict the growth of basil seedlings and the growth and yield of basil plants in each grade were subsequently investigated after transplanting.

Statistical analysis

Statistical analysis was performed using the SAS program (Enterprise Guide 8.3, SAS Institute Inc., Cary, NC, USA). The T-test and Duncan’s multiple comparison test were used to determine significant differences (p < .05) between treatments.

Results and Discussion

Changes in basil seedling growth under different sub-irrigation cycle conditions

Table 2 showed that there were no significant differences in the basil seedling growth between the 2 days and 3 days of irrigation cycles until 22 DAS. At 25 DAS, significant differences were observed in the plant height, shoot fresh weight, and leaf area of basil seedlings. In particular, the shoot fresh weight and leaf area of basil seedlings in the 2 days irrigation cycle treatment exhibited 30% reduction compared to the 3 days treatment. Kalamartzis et al. (2020) compared the physiological characteristics and growth in five cultivars of basil under different irrigation conditions and reported that sweet basil exhibited a tendency to decrease in yield with increasing irrigation. Caliskan et al. (2017) investigated the growth of sweet basil under dry, normal, and over-wetting conditions and found that growth was more severely reduced under over-wetting conditions than under dry conditions. Although these studies were conducted in soil culture, the finding that the growth of sweet basil was inhibited by over-wetting conditions rather than by dry conditions was in accordance with the results obtained in hydroponics from this study.

Non-destructive detection of growth and quality in basil seedlings

In order to determine whether the growth of the basil seedlings could be detected by the non-destructive method, the fresh weight, leaf area, and PCA of basil seedlings were investigated periodically during the period of seedling production and their correlations were shown in Fig. 2. The PCA was determined to be a good indicator of the fresh weight and leaf area of basil seedlings, with a high coefficient of determination of over 0.93. It has been demonstrated by several researchers that PCA is an effective method for predicting the leaf area or leaf area index of a variety of crops (Baker et al., 1996; Raj et al., 2021; Yamaguchi et al., 2020). Jeong et al. (2024) have shown that PCA is also a reliable approach for estimating the fresh weight and leaf area of lettuce seedlings.
To determine the growth and quality of basil seedlings by the non-destructive method, the vegetation indices were calculated from multispectral camera images at 25 DAS and subsequently correlated with the shoot fresh weight, LAI and CWC. Of the vegetation indices investigated, the VARI, GI, and NGRDI were shown to be accurate in predicting basil seedling growth and quality, with coefficients of determination above 0.6 (Table 3). Previous studies have shown that vegetation indices are sensitive to water stress (Perry and Roberts, 2008; Rallo et al., 2014) and have been used to develop decision support systems for irrigation (Shi et al., 2019). VIs have been employed to predict crop growth in open fields; however, they have also been applied to analyze crop growth and quality in greenhouses and plant factories in recent times Cervera-Díaz et al., 2023; Wei and Fang, 2024). The SWIR region (1,000–2,500 nm) is known to be an effective detector of plant water content (Braga et al., 2021). However, in this study, vegetation indices were calculated using the reflectance values at five wavelengths using a multispectral camera. The study demonstrated that it is possible to detect the growth and water-related quality of basil seedlings without using SWIR imaging. Also, this study demonstrated the feasibility of detection under the conditions of moderate water stress due to differences in irrigation frequency within the plant factory.

Growth and yield after transplanting of basil seedlings in different grades based on PCA values

As we found that the PCA was effective in detecting the growth differences in basil seedlings, we calculated the PCA from the image data of basil seedlings just before transplanting and classified them into A–D grades (Fig. 3). The PCA of all seedlings was measured, and the A–D grading criteria were divided in order to achieve the most equitable distribution of seedlings per grade. Table 4 showed the range of PCA values in basil seedlings within each grade, along with the corresponding range of fresh weight. Following transplanting, there was a variance in the growth of the basil seedlings by PCA-based grade (Fig. 4 and Table 5). The highest growth was observed in the seedlings of A grade, while the lowest was observed in the seedlings of D grade. In the study conducted by Jeong et al. (2024), it was observed that the yield of lettuce seedlings subjected to PCA-based grading did not differ significantly from that of seedlings graded above the standard. However, for basil with a relatively short growth period after transplanting, there was a significant difference in the yield of the basil crop according to the grades based on the PCA at the time of transplanting. It has been demonstrated by several researchers that the differences in seedling growth and quality resulting from factors such as age, nutrient management and growth promoting treatments affect the subsequent growth and yield of crops after transplanting (Kim et al., 2015; Moncada et al., 2020). The growth differences between basil seedlings resulted in significant differences in the growth and yield after transplanting, indicating that the selection of basil seedlings with an appropriate size for transplanting is crucial for the achievement of stable and uniform basil yields in the plant factory.
The PCA was calculated from the image data of basil, even after transplanting, and the correlation between PCA values and basil fresh weight and leaf area was analyzed. Fig. 5 showed the correlation between the fresh weight and leaf area of basil, as measured at regular intervals throughout the entire production period, and the PCA value derived from the image data. The correlation between the PCA values for the seedlings and plants of all grades with their fresh weight and leaf area showed a coefficient of determination with higher than 0.94. Although the plants in the grade D exhibited lower fresh weight and leaf area for a given PCS value compared to the grades A–C, our findings confirmed that fresh weight and leaf area of basil can be predicted non-destructively using PCA values derived from top-view images throughout the entire production period in the plant factory. This study concurs with previous researches (Ban et al., 2023; Jayalath and van Iersel, 2021; Jiang et al., 2018) in confirming that non-destructive prediction of crop growth changes is possible, both during the period of seedling production and during the cultivation period after transplanting.

Conclusion

This study has demonstrated that changes in the growth and quality of basil seedlings resulting from different irrigation frequencies during the period of seedling production, can be non-destructively detected using PCA and several vegetation indices (VARI, GI, and NGRDI). The grades were divided based on the PCA values of basil seedlings at transplanting, and the resulting data demonstrated significant differences in the growth and yield after transplanting of basil among the grades. Also, the PCA values analyzed during the entire production period of basil showed a high correlation with the fresh weight and leaf area of basil cultivated in the plant factory. The findings of this study indicate that the periodic collection and analysis of image data can be employed to predict crop growth during the production period, and to make informed decisions regarding seedling selection for transplanting, with the objective of achieving uniform and stable yields in the plant factory.

Notes

This research was carried out with the support of the "Smart Farm Innovation Technology Development Program" (Project No. 423001021HD030), Korea Smart Farm R&D Foundation.

Fig. 1
Spectral distribution of the LED light source used in the study.
ksppe-2024-27-6-551f1.jpg
Fig. 2
Correlation of projected canopy area (PCA) with shoot fresh weight (A) and leaf area (B) during the period of seedling production.
ksppe-2024-27-6-551f2.jpg
Fig. 3
Basil seedling in the grade of A–D.
ksppe-2024-27-6-551f3.jpg
Fig. 4
Basil crops at 15 days after transplanting of seedlings in A–D grades.
ksppe-2024-27-6-551f4.jpg
Fig. 5
Correlation of projected canopy area (PCA) with shoot fresh weight (A) and leaf area (B) during the entire cultivation period of basil.
ksppe-2024-27-6-551f5.jpg
Table 1
Vegetation indices and formulas used in this study
Names Index Formulation Reference
Normalized Difference Vegetation Index NDVI (R790-R650)/(R790+R650) Rouse et al. (1974)
Visible Atmospherically Resistant Index VARI (R550-R650)/(R550+R650-R450) Gitelson et al. (2002)
Greenness Index GI (R550/R650) Zarco et al. (2005)
Normalized Green Red Difference Index NGRDI (R550-R650)/(R550+R650) Hunt et al. (2005)
Modified Simple Ratio MSR (R790-R650-1)/(R790/R650+1)1/2 Sims and Gamon (2003)
Simple Ratio Index SRI (R790/R650) Birth and McVey (1968)
Table 2
Growth characteristics of basil seedlings cultivated under different irrigation cycle conditions for 25 days after sowing
DASz Irrigation cycle Plant height (cm) No. of leaves (/plant) Leaf area (cm2) Shoot freshSPAD value weight (g)
11 2 day 0.65 1.9 0.55 28.1 0.014
3 day 0.57 2.0 0.49 23.4 0.014
Significant NS NS NS NS NS

14 2 day 0.89 2.3 0.90 28.6 0.034
3 day 0.93 2.6 1.01 31.4 0.038
Significant NS * NS NS NS

17 2 day 0.76 3.1 1.55 38.83 0.058
3 day 0.83 3.3 1.55 38.04 0.046
Significant NS NS NS NS NS

20 2 day 1.50 3.9 3.01 27.26 0.126
3 day 1.32 4.0 3.15 26.89 0.113
Significant NS NS NS NS NS

22 2 day 1.18 3.8 3.64 29.13 0.143
3 day 1.22 3.8 4.52 29.16 0.171
Significant NS NS NS NS NS

25 2 day 1.48 4.3 6.99 29.30 0.275
3 day 1.82 4.6 10.08 27.43 0.390
Significant * NS * NS *

z Days after sowing.

NS, * Nonsignificant or significant at p < .05 by t-test.

Table 3
Coefficient of determination between growth parameters and vegetation indices in basil seedlings
NDVIy VARI GI NGRDI MSR SRI
FWz 0.2219 0.6601 0.6310 0.6255 0.2187 0.2164
LAI 0.2386 0.6884 0.6739 0.6696 0.2349 0.2322
CWC 0.2361 0.6207 0.6073 0.6061 0.2318 0.2288

z FW: shoot fresh weight, LAI: leaf area index, and CWC: canopy water content.

y See Table 1 for the original meanings of these abbreviations.

Table 4
Ranges of projected canopy area (PCA) and estimated shoot fresh weight of basil seedlings in A–D grades
Grade PCA (cm2/seedling) Estimated shoot fresh weight (g/seedling)
A Over 12 Over 0.4
B 9–12 0.4–0.3
C 6–9 0.3–0.2
D Under 6 Under 0.2
Table 5
Differences in the growth of basil according to the transplanting grade
DATz Grade Plant height (cm) No. of nodes (/plant) Leaf length (cm) No. of leaves (/plant) Leaf area (cm2) SPAD value Shoot fresh weight (g) Shoot dry weight (g)
5 A 3.20ay 3.05a 7.08a 4.1a 38.58a 30.79a 1.88a 0.19a
B 3.10a 2.75ab 6.23b 4.0a 27.94b 30.05a 1.32b 0.18a
C 2.00b 2.43bc 5.34c 4.0a 19.92c 30.49a 0.88c 0.08b
D 1.88b 2.23c 3.68d 2.8b 9.87d 30.65a 0.44d 0.04b

10 A 4.93a 4.07a 8.84a 12.80a 139.78a 34.18a 5.75a 0.54a
B 4.24b 4.17a 8.23a 12.42a 112.42b 33.81a 4.32b 0.41b
C 3.60c 3.88a 7.48b 12.00a 90.41c 34.14a 3.40c 0.33c
D 2.85d 3.69a 6.56c 10.31b 55.07d 32.25b 2.06d 0.20d

15 A 7.40a 5.75a 11.19a 20.88a 357.06a 35.32a 14.58a 1.25a
B 6.71ab 5.27b 11.03a 16.93b 288.71b 35.04a 11.98b 1.05b
C 6.00b 5.00b 9.91b 15.57b 250.57c 34.86a 9.99c 0.80c
D 4.55c 4.50c 7.75c 12.90c 160.21d 34.12a 6.19d 0.53d

z Days after transplanting.

y Mean separation within columns by Duncan’s multiple range test at p < 0.05.

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