INTRODUCTION
Lettuce (Lactuca sativa L.) root system is designed naturally to acquire nutrients by extracting nutrients and minerals enriched water from the growing bed (Kerbiriou, Stomph, Van Der Putten, Lammerts Van Bueren, & Struik, 2013; Puno, Sybingco, Dadios, Valenzuela, & Cuello, 2017).
Healthy roots enable crops to contain sufficient concentration of vitamins, such as retinol, ascorbic acid, and phytonadione, on its leaves. Lettuce roots are developed partly during the embryonic stage and mostly in the post-embryonic phase. Taproots, fibrous, and water roots are the three main classifications of root systems of developing lettuce depending on its genetic variation and environment where it is exposed. The latter is evident for
plants grown in water such as hydroponic lettuce.
Conversely, root hair, primary, seminal, and lateral are the three main types of lettuce root. Water roots normally have numerous root hairs compared to lettuce planted in soil. In soil-based agriculture, lettuce root length may descend up to 28 inches down the ground and 12 inches horizontal span for root branches. As nutrients are mobilized into the plant photosynthetic system, root traits particularly during the early embryonic life cycle resemble crop yield (Alejandrino et al., 2020; Moreira, Martins,
& Mourato, 2020; Wu, Asaduzzaman, Shephard, Hopwood, & Ma, 2020) Evidently, seed architecture has a direct relation with seed yield (Strock et al., 2019). Potent nitrogen and salt concentration (Fu et al., 2017; Wei et al., 2014), phosphate (Shi et al., ARTICLE INFO
Keywords:
Bioinspired algorithm Computer vision Lettuce
Root architecture phenotyping Seed fortification and stratification Article History:
Received: October 31, 2020 Accepted: January 10, 2021 Corresponding author:
E-mail: ronnie_concepcionii@dlsu.
edu.ph
ABSTRACT
Ecophysiological stimulators directly affect root morphology, especially in the embryonic stage. To enhance crop germination, an understanding of the root traits under abiotic inducers is needed. In this study, the combined impacts of white and red-blue light spectrums, cold stratification, and seed fortification involving various concentrations of bioactive chemicals namely simple nutrient addition program solution, gibberellic acid, α-naphthaleneacetic acid with thiamine hydrochloride were evaluated on loose-leaf lettuce (Lactuca sativa var. Altima) seedling root architecture. The growth-promoting effects of these nutrients varied the growth rate and morphology of roots which are immediately shown during the radicle development. Integrated computer vision and computational intelligence were employed for phytomorphological signatures extraction of seedlings that were cultivated in a customized modulable spectrum experimental chamber (MSPEC). Root phenotype model was developed using graph-cut segmentation and region properties, and the ideal germination nutrient concentration was optimized using bioinspired models with firefly algorithm optimal result of 204.1 mg/L for nitrate, 238.15 mg/L for phosphate, and 158.08 mg/L for potassium. It was verified that lettuce seedlings can endure highly concentrated nutrients, however, it is more sensitive to phosphate as this macronutrient significantly promotes root growth with the increased whorl number on white light spectrum exposure with cold stratification.
ISSN: 0126-0537Accredited First Grade by Ministry of Research, Technology and Higher Education of The Republic of Indonesia, Decree No: 30/E/KPT/2018
Cite this as: Concepcion II, R. S., & Dadios, E. P. (2021). Bioinspired optimization of germination nutrients based on Lactuca sativa seedling root traits as influenced by seed stratification, fortification and light spectrums. AGRIVITA Journal of Agricultural Science, 43(1), 174–189. https://doi.org/10.17503/agrivita.v43i1.2843
Bioinspired Optimization of Germination Nutrients Based on Lactuca sativa Seedling Root Traits as Influenced by Seed Stratification, Fortification and Light Spectrums
Ronnie S. Concepcion II*) and Elmer P. Dadios De La Salle University, Manila, Philippines
2013), and copper oxide nanoparticles (Margenot et al., 2018; Wang & Shen, 2012; Wang et al., 2020) in grow bed must seriously be managed to avoid speeding up the degradation of roots. Proper nutrients must be delivered to growing crops for health development. Photosynthetic performance greatly varies based on the light spectrum such as in 664 nm red light, 446 nm blue light, and the broad spectrum of white light (Izzo, Hay Mele, Vitale, Vitale, & Arena, 2020). Diseased, slender, and stunted roots may advertently impact the growth rate and quality of a crop.
In lettuce, the radicle or the embryonic primary root appears after a day of germination.
Seed germination refers to the sprouting of a seed, growing to seedling, and forming a spore. Seminal, lateral, and root hairs appear more dominantly during the post-embryonic phase when it is much needed as the presence of multiple leaf blades requires more minerals and nutrients. Lateral root increases the surface area of the root system. However, if the primary root is not exposed to sufficient nutrients, the root system will not progress. This important function of the embryonic radicle primarily defines the succeeding traits of the total root system and crop biomass where hypocotyl belongs. Hypocotyl is an embryonic stem found above the radicle and below the cotyledon. It acts as a natural vessel in transporting nutrients from radicle to cotyledon where nutrients are stored during embryonic to vegetative stage. Hypocotyl and radicle have a combined function that gives rise to roots and drought adaptation. Moreover, root primary growth corresponds to the increase in the length of the hypocotyl and root due to cell division happening in the shoot apical meristem, and secondary growth is the increase in girth of the plant due to lateral meristem development.
The complexity of root phenotyping involves open-field, partially controlled greenhouse, and controlled environment agriculture in generating throughout necessary for plant breeding, genomics, and agricultural intensification (Falk et al., 2020).
Morpho-agronomical characterization of root architecture has been carried out on wheat (Tomar et al., 2016), wild lettuce (L. serriola) (Fu et al., 2017), oilseed rape (Brassca napus) (Shi et al., 2013), rice (Oryza sativa L.) (Wang et al., 2020), pea (Pisum sativum L.) and melon (Cucumis melo L.) (Solano, Hernández, Suardíaz, & Barba-Espín, 2020), thale cress (Arabidopsis thaliana) (Iglesias
et al., 2019; Moore et al., 2013), common bean (Phaseolus vulgaris) (Strock et al., 2019), tomato (Solanum lycopersicum L.) (Alaguero-Cordovilla, Gran-Gómez, Tormos-Moltó, & Pérez-Pérez, 2018), and corn (Zea mays) (Ghorchiani, Etesami,
& Alikhani, 2018). Some seeds have a dormancy stage and cold stratification can trigger the seed membrane and promote germination. On the other hand, seed fortification involves the impregnation of bioactive chemicals such as crop micronutrients and growth regulators to invigorate seeds for growth and development.
Challenges in root architecture phenotyping are ingrained with the possible root damage during field excavation. Plant imaging includes X-ray computed tomography (XCT) (Teramoto et al., 2020) that highly expensive. With the advent of digital platforms, there are crop imaging programs that have been developed such as ImageJ software which was used to extract seminal root angles (Clark et al., 2011). Other root phenotyping platforms are the Automatic Root Imaging Analysis (ARIA) (Falk et al., 2020) and Root Reader 3D for five types of rice plant roots (Rufo, Salvi, Royo, &
Soriano, 2020). RootNav2.0 is a deep learning- based automatic map reading of complex crop root system architecture (RSA) (Yasrab et al., 2019).
Despite the mentioned advancement in field and laboratory root phenotyping, time and laboratory imaging device expenses are some of the major fallbacks. Currently developed devices require the samples to be brought to a laboratory for imaging and analysis which certainly adds up the challenge for on-site phenotyping in the field. The need for optimizing germination yield in lettuce is still an open research area. Likewise, as of this writing, loose-leaf lettuce root architecture concerning environmental stressors is not yet comprehensively studied.
In this study, computer vision and computational intelligence is integrated for non- destructive approach of root architecture phenotyping on the quantitative traits and bioassay concerning germinating loose-leaf lettuce (Lactuca sativa var.
Altima) seeds in varying thermal environments, light spectrum exposures and nutrient concentrations.
Specifically, a computer vision algorithm is developed to extract an array of morphological root traits. Optimization of nutrient concentration is also explored to improve germination using bioinspired algorithms namely artificial bee colony, firefly and genetic algorithms. The proposed approach and
model enables agriculturists and scientists to easily extract root traits using a consumer-grade camera.
The developed model in this study is part of the 10-module vision-phenotype-lettuce (VIPHLET) model used for adaptive nutrient management in a lettuce smart farm.
MATERIALS AND METHODS
The general architecture of the system for vision-based lettuce root architecture phenotyping and bioassay starts with allowing the seeds to undergo stratification and fortification as a core strategy in this study to differentiate growth pre- harvest factors in response to its biophysical growth (Fig. 1). Exposure to different artificial light
spectrums is necessary to discriminate its impacts on phytopigments and nutrients retained in its leaves when harvested. Bioassay is employed to Matlab R2020a that is the sole computational intelligence and computer vision platform used in developing the predictive models.
Phenotype Data Description and Experimental Condition
Lactuca sativa var. Altima is the chosen plant cultivar that undergoes in vitro germination using petri plate for the first seven days and then was individually transplanted into test tubes containing media after the vegetative growth stage. It was conducted from August to September 2020 in a customized growth chamber in Cavite.
Fig. 1. General system architecture of the development of vision-based lettuce root architecture phenotyping and bioassay with seed stratification and fortification involving bioactive chemicals
Two batches of cultivation were done: first is direct fortification without cold stratification under white (W) and red and blue (RB) lights for 14 days inside the aseptic modulable spectrum experimental chamber (MSPEC) containing a spread for petri plates placement and test tube rack; and second is dark and cold stratification inside the fridge with 5 °C for 7 days after sowing (DAS) and another 7 days of exposure to RB lights inside the MSPEC with a temperature range of 25.4 to 34.9 °C (Fig.
2). Thus, there is a total of five treatments: red and blue lights without stratification (RB-WOS), white light exposure without stratification (W-WOS), no light exposure with stratification (NL-WS), red and blue lights exposure with stratification (RB-WS), and white light exposure with stratification (W-WS).
For each treatment, seeds were soaked in tap water and bioactive chemicals with differing macronutrient concentrations (Table 1). The body of the customized MSPEC is constructed using black
colored board wrapped aluminum foil on its inside pane. The top side of the chamber allows sufficient light intensity to pass through using a transparent film. Two T8 full spectra LED lamps and one set of 60 red and blue LED strips in the serpentine pattern were attached as artificial photosynthetic W and RB light sources respectively (Fig. 2a and Fig. 2b).
Arduino ESP32 microcontroller with built- in wireless capability through its WiFi module transmits light, temperature, and humidity sensor- acquired data inside the MSPEC to ThingSpeak online temporary data warehouse in daily frequency.
DHT11 and TSL2561 digital light sensors were configured to read temperature and humidity, and light intensity inductions. ESP32 microcontroller activates light source for 16 hours per day from 6:00 in the morning to 10:00 in the evening. 5V DC brushless mini-exhaust fans were installed to allow aeration.
Fig. 2. Operational platform of the customized wireless modulable spectrum plant experimental chamber using artificial photosynthetic light sources: (a) white, (b) combined red and blue; and (c) the corresponding system block diagram
There are 17 petri plates prepared to hold 6 lettuce seeds resulting in 102 seeds per light treatment per batch of stratification. This makes a total of 408 lettuce seeds that were cultivated for both direct fortification and stratification-fortification under two light exposure treatments.
Seeds were soaked in tap water and bioactive chemical solutions such as the simple nutrient addition program (SNAP) solution (Institute of Plant Breeding, University of the Philippines Los Baños), gibberellic acid (GA), α-naphthalene acetic acid (NAA) with thiamine hydrochloride (Table 1).
Seedling images were captured using IP Logitech camera in a weekly pattern for two weeks. The camera aspect ratio is 1:1 with a spatial resolution of 3000 x 3000 pixels. Included root traits for vision- based phenotyping are root surface area (SUA), convex area (CVA), total root length (TRL), root width (WID), root perimeter (PER), network area (NWA), and root solidity (SOL) as defined in Table 1. The laptop used in the development of computational intelligence models and image processing has Core i7 8th generation processor and 8 GB RAM.
Preparation of Bioactive Chemicals
SNAP, GA, and NAA are the three bioactive chemicals used to promote growth in lettuce
seedlings as they contain suitable concentrations of macro and micronutrients in terms of nitrate (NO3-), phosphate (PO43-), and potassium (K) when prepared optimally (Table 2). For 100% SNAP solution, 2.5 ml of solutions A and B were diluted with 1 l of tap water.
In elemental form, SNAP solution A is composed of 6.10% nitrogen (N), 4.25% calcium (Ca) and 3.09%
potassium (K), and solution B consists of 0.376%
phosphorus (P), 0.494% magnesium (Mg), and 0.151% iron (Fe) that are pre-diluted in about 85%
distilled water (dH2O). For 100% gibberellic acid solution, 9 g of gibberellin A3 powder is dissolved to 99.2 ml of isopropanol (IPA), and 14.8 ml of this solution is diluted with 236.6 ml of dH2O. For 100% NAA solution, 1 ml of commercially available NAA hormone is mixed with 14.8 ml of dH2O. The equivalent nitrate, potassium, and phosphate concentrations for tap water, SNAP, GA, and NAA were measured using UV-Vis spectrophotometry at 205, 765, and 84 nm wavelength protocols respectively. To verify the bioassay of these bioactive chemicals during seedling growth, especially in root phenotypic traits, seeds were cultivated in different sensor-measured concentrations with equivalent pH, electrical conductivity (EC), nitrate, phosphate, and potassium concentrations shown in Table 2.
Table 1. Tap water and bioactive chemical solutions concentration Liquid and Bioactive
Chemical Solutions
Chemical Concentration pH Electrical conductivity
(µS/cm) Nitrate
(mg/l) Phosphate
(mg/l) Potassium (mg/l)
Tap water 7.14 139 73.32 57.85 43.28
SNAP
2.05a 2.35b 2.77c 3.44d
353 2392 1601 730
287.60 275.04 258.29 229.01
38.51 10.39 34.71 33.90
64.93 274.26 193.30 102.99 GA
5.96a 6.07b 6.19c 6.41d
1372 1096 891 561
124.70 120.09 112.90 105.63
65.23 42.37 42.55 63.25
168.74 139.00 119.18 85.42 NAA
6.44a 6.62b 6.73c 6.79d
132 148 163 168
100.95 92.37 87.18 88.25
56.66 53.90 56.57 58.80
42.40 44.48 46.05 46.23 Remarks: a This row corresponds to 100% concentration of the specified bioactive chemical; b-d 75%, 50%, and 25%
concentration of diluted bioactive chemical respectively
Table 2. Root traits captured by the developed vision-based root model
Root Trait Name Symbol Root Trait Description
Root surface area SUA 2-D surface area of the primary root
Convex area CVA Convex hull area confining the whole root section Total root length TRL Full extent of primary root length
Width WID Maximum horizontal span of the root architecture
Perimeter PER Combination of connected network and background pixels Network area NWA Pixels connected to the skeletonized root image
Solidity SOL Ratio of network area and convex area
Development of Computational Root Phenotype Model
The computational root phenotype model consists of processes of root segmentation and root numerical traits extraction. Graph-cut segmentation is a computer vision technique based on energy minimization that computationally determines the arrangement of atoms in physical space or picture elements for images in terms of color features through K-means clustering (Concepcion, Lauguico, Alejandrino, Dadios, & Sybingco, 2020; Lauguico, Concepcion, Alejandrino, Tobias, & Dadios, 2020).
It uses lazy snapping that snaps true object pixels from low contrast edges (Concepcion, Lauguico, Alejandrino, Dadios, & Sybingco, 2020). In this study, graph-cut segmentation extracts root pixels and removes non-vegetative and leaf and hypocotyl pixels (Fig. 3). Root starts on the bent region at the bottom of the hypocotyl. In the vegetative growth stage of lettuce, root and hypocotyl have almost the same colors and surface morphology signatures in some instances depending on the degree of how the environment-induced the seed to grow.
It starts with converting the raw RGB image into Lab color space and configuring L channel from 0 to 100, a* channel from -86.1827 to 98.2343 and b*
channel from -107.8602 to 94.4780. It is proceeded by imposing 45100 superpixels on the transformed Lab image and masking the snapped pixels based on lazy snapping clustering. Image border pixels and clutter objects with less than 300 pixels are also removed to make sure that no non-root signals are connected to the annotated image. Wireframe root skeleton image is constructed by eroding the side portions of the root strand. In the case of lettuce seedling in the vegetative stage, there is usually no extended seminal root strands. Image region properties were used to measure root SUA (cm2),
CVA (cm2), TRL (cm), WID (cm), and PER (cm).
NWA (pixel) is measured by computing the number of pixels connected to the skeletonized root and SOL (unitless) is computed as the quotient of NWA and CVA (Table 1). To convert the pixel values of these root traits, a scaling factor (Mactual / Mpixel) is generated through the use of an actual 2.54 cm by 2.54 cm white reference marker component (Fig.
3) with the equivalent of 467.88 by 467.88 pixels.
The actual metric value of the measured root trait
(Ractual) is determined using (1) by multiplying the
extracted root pixel value (Rpixel) with the reference marker actual metric size (Mactual) and dividing it by reference marker pixel size (Mpixel).
Optimization of Germination Nutrient Concentrations
Optimization of germination nutrient concentrations involves constructing fitness function and developing bioinspired optimization models namely artificial bee colony (ABC), firefly algorithm (FA), and genetic algorithm (GA) (Concepcion, Lauguico, Tobias, et al., 2020). The goal of this phase is to generate the specific chemical concentration that has an optimal impact on the seedling growth of lettuce. The mathematical topology of the fitness function is based on the bioassay of bioactive chemical concentrations with extracted root traits (2). Kaiser-Meyer-Olkin (KMO) factorability test with KMO of at least 0.6 and Bartlett’s test of P-value less than 0.05 were employed to verify the indication of valuable extracting of significant features. The neighborhood component analysis (NCA) was configured with limited memory Broyden-Fletcher- Goldfarb-Shanno (LBFGS) algorithm. Hessian history size of 15 and line search method of
...1)
‘weakwolfe’, the most significant root trait correlated to nitrate, phosphate, and potassium was selected, resulting to root convex area and surface area.
LBFGS is the solver type necessary to estimate feature weights of less than 1,000 samples such as in this study. However, root CVA is considered as the ideal basis for observing root growth as influenced by ecophysiological inducers as it is highly impacted by both nitrate and phosphate variations. Using multiple linear regression, the resulting CVA fitness function is shown in (3) with corresponding weights on each predictive macronutrient x1, x2, and x3 as NO3-, PO43-, and K, respectively.
ABC model was configured using a colony size of 150, 15 onlooker bees, and 240
abandonment limit. FA model was set to provide its ideal value by using a swarm size of 50, the light absorption coefficient of 3, attraction coefficient of 3, 0.2 mutation coefficient, 0.98 coefficient damping ratio, and 0.2 uniform mutation range. On the other hand, genetic algorithm was configured using double vector population type, a population size of 50, rank fitness scaling function, tournament selection function with size of 4, reproduction elite count of 2.5, reproduction crossover fraction of 0.8, uniform mutation rate of 0.01, single-point crossover function, forward direction migration with a fraction of 0.2 and interval of 20, and nonlinear constraint penalty algorithm. All these three optimization models were set with lower and upper bounds of 0 and 500 and set a diverge to global optimum either after 500 iterations or stale test of 100 (Fig. 4)
Fig. 3. Graph-cut segmentation of root from seedling architecture, root part indication, reference marker for scaling factor, and wireframe skeleton binary image (magnified) using the developed functional vision- based root image processing model in Matlab
...2) ....3)
Fig. 4. Global divergence characteristic curve of (a) artificial bee colony, (b) firefly algorithm and (c) genetic algorithm as defined by best cost and fitness value
Microscopic Tool and Statistical Analysis
Root microscopic images in the longitudinal section were taken using a digital microscope with high definition CMOS sensor and eight super bright LED lights (Shenzhen, China) in comparing root general architecture and emphasizing the number of root whorls on vegetative lettuce that were exposed in different artificial light spectrums.
The microscope was accessed and calibrated using the HiView platform. All statistical analyses were performed using Minitab 19 (Minitab, LLC).
Significant data correlations were considered at α ≤ 0.05. Violin plots for showing median, interquartile, range, and frequency scores of lettuce seedling root traits were generated using Python OpenCV in Scientific Python Development Environment (Spyder) 3.3.6 IDE. Weight spectrum is generated using Statgraphics 19.
RESULTS AND DISCUSSION Root Traits Phenotype
In this study, lettuce seedling root morphology was captured using a consumer-grade camera that overcomes the need for expensive imaging devices such as XCT (Teramoto et al., 2020). Instead of using ImageJ (Clark et al., 2011), ARIA (Falk et al., 2020), and RootReader3D (Rufo, Salvi, Royo, & Soriano, 2020), a Matlab-based program was developed that will automatically generate the selected dominant root traits in order (Table 1). Correlation analysis confirmed that root surface area, convex area, total root length, width, perimeter, and network area are allometrically related to each other as they all have significant positive correlation coefficient for all stratification, fortification, and light spectrum treatments (Table 3). Additionally, solidity has a strong negative correlation with the convex area at α ≤ 0.05 as with other root traits.
Table 3. Correlation coefficients extracted from Pearson correlation analysis of vegetative lettuce root phenotypic traits of various stratification, fortification, and light treatments
Treatment N SUA
CVA CVA
PER TRL
NWA WID
PER PER
NWA NWA
SUA SOL
CVA
RB-WOS 106 0.954 0.950 0.680 0.869 0.745 0.809 -0.878
W-WOS 115 0.959 0.931 0.576 0.852 0.736 0.760 -0.881
NL-WS 130 0.933 0.923 0.495 0.859 0.739 0.762 -0.854
RB-WS 19 0.963 0.895 0.835 0.882 0.789 0.875 -0.914
W-WS 29 0.804 0.874 0.823 0.775 0.729 0.753 -0.824
Root Stratification and Fortification Bioassay Differences in root architecture based on bioactive chemical treatments (Table 2), cold stratification, and light spectrum exposure were observed and quantified. The result suggested the need for optimization of ecophysiological stressors to speed up the germination of lettuce seedling (Fig. 5).
Lettuce seedlings that were sown without cold stratification and exposed to red and blue lights are characterized by early sprouts only for using tap water, SNAP, and gibberellic acid inducers. For W-WOS, using 50% to 100% SNAP solution, the germinated seed yielded thin white roots with broad green leaves. While the sprout showed multiple root strands with broad green leaves in 25% SNAP solution, small sprouts in GA and small radicles with tiny cotyledons exposed in NAA solutions.
(Fig. 5). After 14 days of seedling cultivation, W-WOS resulted in multiple points of growing at the top end of hypocotyl and high ratio of hypocotyl to root length for those cultivated using tap water and disintegrated root tissue for those using GA and NAA solutions. It was also observed that at the end of cultivation, the clear GA solution turned into brownish color for the growth containers that are exposed to artificial white light. SNAP solution resulted in long roots with greener sprouts and multiple growing points. On the other hands, 14- day old seedlings in RB-WOS is characterized by disintegrated tissues when exposed to 100% SNAP and 25 to 100% GA solutions. NL-WS with 25%
NAA solution concentration resulted in a larger root structure but a comparably lower germination rate (Fig. 5 and Fig. 6). For 14-day old seedling grown in RB-WS environment, using 50% to 100% SNAP, 25% to 100% GA, and 50% to 100% NAA solutions resulted in disintegrated seedling tissue. Using tap water resulted in white and thin long roots.
25% NAA solution resulted in thick roots without green leaves. 25% SNAP solution yielded the best visually-oriented root trait as characterized by the thickness and multiple extended whorls. For a 14- day old seedling grown in a W-WS environment, 25% SNAP solution yielded the best root traits in terms of thick and extended roots. Tap water and GA solutions resulted the same under RB-WS.
Conversely, NAA solutions yielded thick short roots with brown-spotted leaves indicating the first chlorotic stage.
Lettuce seedling germinated under 25%
SNAP solution with cold stratification in the first 7 days and exposed to red and blue light spectrum for the succeeding 7 days had the largest root surface area, convex area, total root length. The largest network area and highest solidity value are characterized by lettuce seedling root germinated using 25% SNAP solution with cold stratification and exposed to the white light spectrum. The thickest root width is exhibited by lettuce seedling cultivated in 50% NAA solution with cold stratification for the first 7 days and exposed to white light for the next 7 days. Lettuce germinated in 25% NAA solution with cold stratification for the first 7 days and exposed to white light for the next days exhibited the largest perimeter. Seedlings that are grown in 50% NAA solution with cold stratification and exposed to red and blue lights yielded the smallest root surface area, convex area, root length, width, perimeter, and network area. Thus, the influence of the white light spectrum on lettuce seedlings grown in 50%
NAA with cold stratification is highly relative to better root yield compare to red and blue lights (Fig. 5). Growing lettuce seeds using 50% to 100%
GA solution, with or without cold stratification, and exposing it to red and blue light spectrum resulted in the lowest solidity value as characterized by almost disintegrating seedling tissue (Fig. 5M).
Fig. 5. Computational root traits (surface area, convex area, total root length) as influenced by light source spectrum and stratification, and categorized by macronutrient source for seed fortification namely (a-c) tap water, (d-f) SNAP, (g-i) GA, and (j-l) NAA; (m) various actual lettuce seedling root traits after 7 and 14 days after sowing as significantly influenced by abiotic stresses
In terms of bioactive chemical concentrations, the potency of phosphate and potassium has a direct impact to plant root growth (Fig. 6). Nutritional availability of this nitration without stratification has a weak negative impact on root surface area, convex area, width, and perimeter. Nitrate with stratification resulted in a weak positive relation on root surface area and width. Phosphate without stratification has a weak positive impact on root surface area and total root length, whereas almost no impact on root width. Phosphate with stratification has a strong negative influence on root surface area, convex area, and width. Potassium without stratification has a negative weak impact on root surface area, convex area, root length, width, and perimeter. Potassium with stratification has a strong influence on root surface area, convex area, width and perimeter, and a weak impact on root length. Root network area and solidity have constantly unchanged with varying bioactive chemical concentrations and stratifications (Fig. 6).
Supported by the findings that root traits were affected by induced stratification and fortification, 25% SNAP solution in tandem with cold seed stratification for the first 7 days and exposure to combined red and blue light spectrums yielded the best lettuce seedling root morphology. However, a prepared 25% SNAP solution may vary its nitrate, phosphate, and potassium concentrations depending on the environmental temperature (Puno, Sybingco, Dadios, Valenzuela, & Cuello, 2017),
thus, the exact value of these macronutrients must be optimally determined through higher intelligence such as an evolutionary algorithm.
Across all experiments, phosphate potency has the most significant influence on all included root traits, followed by potassium and nitrate which has the weakest influence (Fig. 6). There is an increase of 0.008598 cm2 in root surface area, 0.013459 cm2 in convex area, 0.030048 cm in total root length, 0.00507 cm in width, 0.205087 cm in perimeter, 4.48x10-6 in network area and 1.25448x10-10 in root solidity per 1 mg/l in line with the increase of phosphate concentrations. There is an increase of 0.003821 cm2 in root surface area, 0.005982 cm2 in convex area, 0.013355 cm in total root length, 0.002253 cm in width, 0.091154 cm in perimeter, 1.99x10-6 in network area and 5.57569x10-11 in root solidity per 1 mg/l in accordance with the increase of potassium concentrations. There is an increase of 0.002631 cm2 in root surface area, 0.004119 cm2 in convex area, 0.009196 cm in total root length, 0.001552 cm in width, 0.062767 cm in perimeter, 1.37x10-6 in network area and 3.83932x10-11 in root solidity per 1 mg/l in line with the increase of nitrate concentrations. In this study, it was observed that lettuce seedling root architecture can be quickly phenotyped using a consumer-grade RGB camera for a computer vision approach (Fig. 3). Cold stratification helps in breaking the dormancy of most lettuce seeds as a result of a higher germination rate compare to seeds without stratification.
Fig. 6. Germination yield based on the combined influence of light spectrum and macronutrient concentration recorded 7 days after sowing
Impacts of Light Spectrum in Germination For the first 7 days of cultivation, exposing seeds in a no light (NL) environment resulted in an 84.667% germination rate (Fig. 7). All seeds in triplicate batches that are soaked in 100% SNAP, 50% GA, and 50% NAA solutions sprouted at the expected time. However, those which are soaked in 25% NAA have an overall 58% germination rate.
Seeds that were cultivated directly using red and blue light (RB) have the highest germination rate by using SNAP solution, and lowest germination by using tap water and NAA solution. Seeds that were cultivated directly using white light (W) have the highest germination rate by using tap water
and SNAP solution, and the lowest germination is exhibited by using NAA solution (Fig. 7). All seeds that were exposed to white and red and blue artificial photosynthetic light sources disintegrated with 25% NAA solution, thus, no output images of seedlings were taken for those samples. The trend of germination using white and red and blue lights are generally above 70% for all growth containers containing tap water, SNAP, and GA solutions.
Overall, the germination rate for MSPEC with white, and red, and blue lights is 82.051% and 71.795%, respectively, making white light the more dominant light spectrum in growing better root architecture.
Fig. 7. Interaction curves highlighting the influence of bioactive chemical concentration in terms of nitrate, phosphate, and potassium on lettuce root (a) surface area, (b) convex area, (c) total root length, (d) width, (e) perimeter, and (d) network area that is conditionally affected by cold stratification
It was verified using a digital microscope with high definition color CMOS sensor and eight super bright white LED lights that light spectrum affects the number of root whorls in lettuce (Fig.
8). Exposure to both white and red and blue light spectrums results in different number of root whorls in which white spectrums allows extensive growth of multiple extended whorls (Fig. 5 and Fig. 8). These root basal whorls are essential indicators of more root growth angles in exploring nutrient availability over the unconstrained cultivation area (Miguel, Widrig, Vieira, Brown, & Lynch, 2013). In return, the higher number of root basal whorls is a determinant for better crop yield. In this study, the white artificial photosynthetic light source has this influence of promoting root basal whorls (Fig. 8) that was greatly observed in seedlings grown using 25% SNAP
solution with cold stratification (Fig. 5M). Pure red light results in hypocotyl elongation (Izzo, Hay Mele, Vitale, Vitale, & Arena, 2020) and it was extended in this study that 100% white light spectrum stimulates elongation of hypocotyl when exposed to cold stratification. Overall, the investigation of exposing various light spectrums to lettuce seeds during cultivation is an effective approach to gain a thorough phenotypic understanding in improving agricultural practices.
Optimized Germination Nutrient Concentration Tap water, simple nutrient addition program solution, gibberellic acid, and α-naphthaleneacetic acid with thiamine hydrochloride solution have corresponding macronutrient concentrations that are affected by varying environmental temperatures Fig. 8. Visual root architectures highlighting the contrast in root whorl number for lettuce seedling grown in (A) red-blue and (B) white artificial photosynthetic light sources. Captured on day 14 after sowing (600x magnification)
Table 4. Optimized germination nutrient concentration as generated by bioinspired models
Bioinspired Optimization Model Optimal Macronutrient Concentration
Nitrate (mg/l) Phosphate (mg/l) Potassium (mg/l)
Artificial bee colony 181.13 107.10 60.54
Firefly algorithm 204.10 238.15 158.08
Genetic algorithm 496.59 9.97 95.19
and may have adverse feedback to the growth of lettuce seedling. As roots are in direct contact with these growth promoters, the root is the first plant tissue that will exhibit the influence of non-optimal nutrient concentration. After several explorations of the developed artificial bee colony, firefly algorithm, and genetic algorithm models in optimizing germination nutrient in terms of nitrate, phosphate, and potassium, the optimal macronutrient concentration as extracted from the global optimum of the expression (3) is 204.10 mg/l for nitrate, 238.15 mg/l for phosphate and 158.08 mg/l for potassium (Table 2). It is in agreement with Ghorchiani, Etesami, & Alikhani (2018) and Rangarajan, Postma,
& Lynch (2018) that phosphate concentration in a root inducer bioactive chemical solution must be significant among the other components. In this study, the firefly algorithm-generated NPK ratio is 0.86:1:0.66 that resolves the issue of dependency on the environmental temperature in maintaining the optimal concentration of a root chemical growth promoter. Artificial bee colony and genetic algorithm optimization (Fig. 4) resulted in an NPK ratio of 1.69:0.57:1.13 and 49.81:1:9.55, respectively.
It can be noticed that for the latter NPK ratio, the concentration of phosphorus is severely suppressed by nitrate. This chemical solution setting results in a thinner root structure as nitrate is responsible for protein retention in leaves.
In this study, it was verified that lettuce seedlings can withstand at high NPK concentrations (Royo et al., 2020), however, there is a certain optimal combination of macronutrients that must be maintained to promote root growth and better crop yield (Table 4). The result of the bioinspired optimization firefly algorithm stimulates the occurrence of an increase in the number of root whorls resulting in a healthier crop. It supports the development of a new bioactive chemical solution with this specific FA-generated NPK ratio (Table 4).
Under chemical stress such as in the application of the generated NPK ratio of the artificial bee colony and genetic algorithm with the mixed influence of light spectrum and stratification, complications on root tissue are expected to occur as previously observed (Figure 5), thus, it requires further automation in the agricultural system to level the concentration to optimal values.
CONCLUSION
This study indicates that stratification, fortification, and light spectrum have a direct influence on the root growth of lettuce seedlings.
The number of basal root whorls is an expression that the white light spectrum stimulates faster root growth as long as the seedling undergoes cold stratification. Lettuce root morphology has different interactions with bioactive chemicals such as SNAP, GA, and NAA. Bioinspired firefly optimization algorithm generated the optimal nitrate, phosphate, and potassium concentrations ratio which promotes root growth. Root growth is highly sensitive to phosphate. For future studies, it is recommended to employ the same approach for optimizing macronutrient concentrations of carbon, oxygen, and hydrogen as influencers to seedling growth.
ACKNOWLEDGEMENT
The authors would like to acknowledge the support of the Engineering Research and Development for Technology (ERDT) of the Department of Science and Technology (DOST) of the Philippines and the Intelligent Systems Laboratory and the Molecular Biology Laboratory of the De La Salle University, Philippines.
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