Computers and Electronics in Agriculture 109 (2014) 221–231
Contents lists available at ScienceDirect
Computers and Electronics in Agriculture
journal homepage: www.elsevier.com/locate/compag
Analysis and optimization of the effect of light and nutrient solution on
wheat growth and development using an inverse system model strategy
Chen Dong a,b,1,2, Dawei Hu a,b,c,d,1,2, Yuming Fu a,b,1,2, Minjuan Wang a,2, Hong Liu a,b,c,d,⇑
a
School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
Institute of Environmental Biology and Life Support Technology, Beihang University, Beijing 100191, China
c
International Joint Research Center of Aerospace Biotechnology & Medical Engineering, Beihang University, Beijing 100191, China
d
State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing 100191, China
b
a r t i c l e
i n f o
Article history:
Received 6 June 2014
Received in revised form 7 September 2014
Accepted 12 October 2014
Keywords:
Bioregenerative Life Support Systems
Wheat cultivation
Light quality
Nutrient solution
System identification
a b s t r a c t
Wheat (Triticum aestivum L.) has been selected as one of the core crops in Bioregenerative Life Support
Systems (BLSS) for future long-term space mission, and its cultivation is affected by several environmental factors. Both light system and nutrient solution are most efficient for plant growth. The objective of
this study was to investigate the influences of different spectra combinations and ionic concentration
2
(NH+4, K+, Mg2+, Ca2+, NO
3 , H2PO4 , SO4 ) on wheat growth, photosynthetic rate, transpiration rate, antioxidant capacity and biomass yield. The results showed that red–white light (RW) and white light (W) are
more conducive to wheat growth and development. There are obvious advantages: photosynthetic rate,
harvest index, thousand kernel weight, edible and inedible biomass. In order to conduct wheat cultivation
for good quality, high yield and efficiency in the artificial environment, a valid state-space model of wheat
growth process (WGP) was developed by experimental data and system identification, and then the
inverse system model of WGP was derived accordingly to theoretically optimize the planting regime
including light intensity and mineral ions concentrations based on prescribed output responses of
WGP and computer simulation. Analysis of the most efficient nutrient mixtures showed that depending
on the light (intensity and quality) and the plant age the different absorption of mineral elements from
nutrient solution was observed. Therefore, it is important to develop a balanced nutrient mixture and
light, which would provide the optimum uptake by plants of each element for the growing season.
Ó 2014 Elsevier B.V. All rights reserved.
1. Introduction
Bioregenerative Life Support Systems (BLSS) is an artificial
ecosystem consisting of many complex symbiotic relationships
among higher plants, animals, and microorganisms (Gitelson and
Lisovsky, 2004; Hu et al., 2012; Tong et al., 2011). Biotechnology
Abbreviations: BLSS, Bioregenerative Life Support Systems; WGP, wheat growth
process; PCA, principal component analysis; LEDs, light-emitting diodes; R, red
light; RB, red–blue light; RW, red–white light; W, white light; SD, simulation data;
ED, experimental data; PPFD, photosynthetic photon flux density; Pn, net photosynthetic rate; POD, peroxidase; TKW, thousand-kernel weight.
⇑ Corresponding author at: Institute of Environmental Biology and Life Support
Technology, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China. Tel.: +86 10 82339837; fax: +86 10 82339837.
E-mail addresses: [email protected] (C. Dong), [email protected]
(D. Hu), [email protected] (Y. Fu), [email protected] (M. Wang),
[email protected] (H. Liu).
1
These authors contributed equally to this work.
2
Tel.: +86 10 82339283.
http://dx.doi.org/10.1016/j.compag.2014.10.013
0168-1699/Ó 2014 Elsevier B.V. All rights reserved.
and engineering control technologies are perfectly integrated to
build BLSS according to the principles of ecological system. Similar
to the Earth’s biosphere, higher plants in BLSS can provide human
beings with fresh air, clean drinking water, nutrient-rich food and
necessary spiritual consolation, which are essential for long-term
manned space missions (Lasseur et al., 1996). In particular, wheat
(Triticum aestivum L.), which is one of the core crops in BLSS
(Tikhomirov et al., 2003, 2011) and unban vertical farms
(Despommier, 2009, 2013), is often restricted from growing by
light and nutrient adversity because of higher planting density,
skeletal structure shading, inappropriate artificial light and nutrient solutions. Moreover, the main factors affecting the plant
growth are light source and mineral nutrition in artificial ecosystems. Improvement of the crop yield and quality by controlling
light system and nutrient solution is therefore a matter of interest
for researchers in both space and unban agriculture fields.
As a primary source of energy, light is one of the most important
environmental factors for plant growth. Light-emitting diodes
(LEDs) are a promising irradiation source for plant growth in space
222
C. Dong et al. / Computers and Electronics in Agriculture 109 (2014) 221–231
because of lightweight, reliable and durable characteristics (Barta
et al., 1992; Kim et al., 2004). Wheat can complete its life cycle
under red LEDs alone, but larger plants and greater amounts of
seed are produced in the presence of red LEDs supplemented with
a quantity of blue light (Goins et al., 1997). However, previous
study has shown that wheat is not sensitive to the blue light dose
induction (Cope and Bugbee, 2013; Dougher and Bugbee, 2001). In
addition, the red-blue-white LED light source indeed is predominant in improving the output and nutritional quality of crops such
as lettuce (Lin et al., 2013) and tomatoes (Lu et al., 2012) in comparison to the red-blue LED light source. While it is widely understood that light intensity could positively affect phytochemical
accumulation (Li and Kubota, 2009; Vergeer et al., 1995), the
effects of light quality are more complex and often reported with
mixed results. Light intensity is essential for the growth, morphogenesis and other physiological responses of plants (Ali et al., 2005;
Hussey, 1963; Schneider et al., 2006). Previous researches mainly
focused on the impacts of different light intensities on the growth
and development of plants in the natural sunlight (Chaturvedi and
Ingram, 1989; Flore, 1980). However, little is known about the
effects of different light intensities on the growth and development
of plants under LEDs. What effects will different artificial light
intensities, especially in RW and W conditions on the growth and
development of plants? And which period under low light intensity
will be not only suitable to the culture of plants, but also beneficial
to energy saving? For these reasons, it is necessary to investigate
the appropriate light intensities of LEDs combined for the industrialized production and evaluate different consequences caused by
the different light intensities in the artificial condition. Although
available information on the low light intensity at seedling stage
resulted in small differences in leaf vegetables (Avercheva et al.,
2009), very little work has been carried out on the significance of
nutrient solution in imparting high or low intensity stress tolerance to wheat plants during ontogenesis.
Mineral metabolism plays a significant role in photosynthesis,
respiration and carbohydrate accumulation of plants (Chapin,
1980; Hocking, 1994). Ion concentration and transpiration rate
have an effect on ion exchange properties of roots and ionic interactions within the root apoplasm. Using soil-like substrate, ceramic
matrixes or other medium cannot be controlled directly. Hydroponic method of growing plants is the biotechnological process
of obtaining the high-quality crops because it allows rational control of the mineral composition through the regulation of their
mineral nutrition during ontogenesis. Therefore, we cultivated
wheat plants on negative pressure porous titanium tubes, which
implemented water supply on demand and were controlled
efficiently.
Environmental factors combined with dynamic must be
optimized in BLSS, which can ensure the healthy growth of plant
and then maximize security and stability of the system. However,
there are too many environmental factors and time varying systems, which means it is difficult to obtain the optimal combination
and change rules by using traditional experimental methods.
Mathematical modeling and experimental simulation are important means to study the problem.
In our previous studies we reported the effect of light quality
and intensity on growth, photosynthetic characteristics, antioxidant capacity and biomass yield and quality (Dong et al.,
2014a,b). As had been expected, the wheat cultivated in the red–
white light was characterized by highest harvest index and lowest
lignin in inedible biomass, which was more beneficial to
recycle substances in the processes of the environment regeneration. In addition, low-light treatment at seedling stage, biomass,
nutritional contents and healthy index (including POD activity,
MDA and proline content) of wheat plants has no significant difference to the control. However, low-light treatment at grain filling
stage affected the final production significantly in previous experiments. Here, we have designed a single red light (R), a red–blue
light (RB, R:B = 4:1), a red-white light (RW, R:W = 4:1) and a white
light (W), which were used as light systems. The PPFD in RW
treatment from 800 to 2100 lmol m2 s1 after 42 days (mature
period) and in W treatment below 400 lmol m2 s1 during the
whole life cycle were controlled. In addition, we controlled light
intensity from low to high to accelerate development and also maturation. In such conditions, the physiological and biochemical
indexes including Pn, transpiration rate, the output and the compositions of inedible biomass were studied during the whole life
cycle of wheat plants. The kinetic model of WGP was precisely
identified based on experimental data, and then optimal planting
regime was theoretically and upfront developed by the inverse
kinetic model and prescribed dynamic response specification of
WGP (Chatterjee et al., 2014), which could be extensively applied
for highly effective cultivation of wheat in BLSS, greenhouse and
other agricultural facilities.
2. Materials and methods
2.1. Cultivation conditions
Common wheat plants (Triticum aestivum L.) were cultivated on
negative pressure porous titanium tubes, which implemented
water supply on demand and provided good aeration of root zone.
The wheat planting density was 1000 seeds per m2. Air temperature, relative humidity, and CO2 levels were maintained in growth
chambers at 21 ± 1.3 °C, 70 ± 4.6% and 500 ± 48.2 lmol mol1,
respectively. The growth period was about 72 days. Modified 1X
Hoagland nutrient solution (Table 1) (Hoagland and Arnon, 1950)
was the basic culture medium. Different nutrient supply changed
every 3 days during the whole life cycle of wheat plants (Table 2).
Red LEDs only (R), mixtures of red plus blue LEDs (RB,
R:B = 4:1), mixtures of red plus white LEDs (RW, R:W = 4: 1) and
white LEDs (W) were used. For all treatments lighting was continuous (24/0 h light/dark). PPFD levels were measured daily at the
top of plant canopy with a quantum sensor (Li-250A, Li-Cor,
USA). PPFD was about 500 lmol m2 s1 for all the treatments, as
calculated (Avaspec-2048-UA, AvantesB.V., Netherlands) from
Table 3 and the spectral absorptance was from 300 to 800 nm.
2.2. Morphological and nutrient elements analyses
The height of wheat plants was measured every 3 days by
straight scale and vernier caliper. The samples were selected on
random within measurement process. Wheat plants state was
analyzed as precisely as possible (GC and SZ, 1994). One cycle
was for 3 days, and nutrient solution inside of the porous tube
was analyzed by an ICS-90 Ion chromatography system (Dionex
Corp., Sunnyvale, CA, USA) after each cycle.
Table 1
Nutrient compositions (1X).
Compositions
Concentration (mg/L)
Ca(NO3)24H2O
KNO3
(NH4)2HPO4
MgSO47H2O
EDTA-2NaFe
H3BO3
MnSO44H2O
ZnSO47H2O
CuSO45H2O
(NH4)6Mo7O244H2O
236
404
57
123
20
2.86
2.13
0.22
0.08
0.02
C. Dong et al. / Computers and Electronics in Agriculture 109 (2014) 221–231
Table 2
Different nutrient supply during the whole life cycle of wheat plants.
Treatment time (day)
R
RB
RW
W
0
3
6
9
12
15
18
21
24
27
30
33
36
39
42
45
48
51
54
57
60
63
66
69
72
0
0.5X
X
1.5X
2X
1.5X
X
0.5X
0
2X
1.5X
X
0.5X
0
2X
1.5X
X
0.5X
0
2X
1.5X
X
0.5X
0
Harvest
0
0.5X
X
1.5X
2X
1.5X
X
0.5X
0
2X
1.5X
X
0.5X
0
2X
1.5X
X
0.5X
0
2X
1.5X
X
0.5X
0
Harvest
0
0.5X
X
1.5X
2X
1.5X
X
0.5X
0
2X
1.5X
X
0.5X
0
2X
1.5X
X
0.5X
0
2X
1.5X
X
0.5X
0
Harvest
0
0.5X
X
1.5X
2X
1.5X
X
0.5X
0
2X
1.5X
X
0.5X
0
2X
1.5X
X
0.5X
0
2X
1.5X
X
0.5X
0
Harvest
223
(ADF), acid detergent lignin (ADL) and acid-insoluble ash (Ash) in
wheat straw was determined according to Van Soest et al. method
(Van Soest et al., 1991) using FIWE six raw fiber extractor (VelpScientifica, Italy).
2.5. Principal component analysis, system identification and computer
simulation
The wheat demand for environmental factors was dependent on
its growth stage, so the environmental factors could not remain
invariant, and should be properly regulated to meet wheat realtime ecological requirements at every growth stage and realize
multi-objective optimization in the cultivation.
In the research, the dimensionality of original data of environmental factors was firstly reduced through PCA, and then the precise state-space model of WGP was identified by experimental data
on the platform of MatLab/System Identification Toolbox. The theoretically optimized planting regime composed of light intensity
and mineral ion concentrations was finally defined through inverse
system model and preferred output responses of WGP.
3. Results and discussion
3.1. Response of wheat growth to different treatments
X: basic culture medium.
Table 3
Spectral data for R, R + B, R + W and W treatments. Measurements were taken at the
top of the plant canopy with a spectroradiometer. Photosynthetic photon flux
integrations for each treatment were equal to 500 lmol m2 s1.
Wavelength range (nm)
300–400
400–500
500–600
600–700
700–800
Total photon flux (%)
R
RB
RW
W
0
0
0
99.65
0.35
0
19.78
0.52
79.41
0.29
0.09
8.64
11.29
79.53
0.45
0.07
33.74
48.01
16.06
2.12
The ICS-90 ion chromatography system, which includes an eluent generator and conductivity detector, was used to analyze the
concentration of positive ions (NH+4, K+, Mg2+, Ca2+) and negative
2
ions (NO
3 , H2PO4 , SO4 ) in this study. Dionex Chromeleon software version 6.6 was used for data processing. All the solutions
were prepared by using doubly deionized water (USF purelab plus,
Ransbach Baumbach, Germany, 18.2 M X cm resistivity).
There was a significant difference in straw height of wheat
plants as indicated in Fig. 1. In particular, the wheat height was
higher only when the red light was used. Once the blue light was
added, the plant height was suppressed at seeding stage. The
growth-induced ability of the red light was probably related to
the low activity of POD, which may make the stem become
extended (Normanly et al., 1997). In contrast, the blue light was
able to dwarf the plant. From earing to flowering period, the plant
height of RB was 0.5–1.5 cm lower than that of W and 3–4 cm
lower than the single red light. The compound light was beneficial
to wheat growth at seedling stage.
Lighting system was an important element for wheat growth,
net photosynthetic rate and transpiration rate. As the height
growth of wheat plants, light intensity on the canopy was stronger
and stronger (Fig. 2A). On the one hand, high light intensity can
improve net photosynthetic rate and transpiration rate.
On the other hand, there was only a limited effect of high light
intensity on photosynthesis and transpiration rate at later stages
(Fig. 2B and C). Our results showed that net photosynthesis rate
of the 4 samples increased rapidly after leaf unfolding, but it
increased more dramatically under RW than that of others, and
2.3. Net photosynthetic rate (Pn) and transpiration rate analyses
Net photosynthetic rate (Pn) and transpiration rate were measured using a photosynthesis instrument (LI-6400, LI-COR, USA)
(Peng et al., 2009). The experiment was repeated 3 times with 10
wheat plants in each treatment. Both of the parameters were analyzed every 3 days.
2.4. Edible biomass and inedible biomass analyses
The crude fiber (Li et al., 2013; Van Soest et al., 1991), sugar,
protein and fat of wheat seeds were determined respectively under
different conditions according to the method described by Gao,
2000. The TKW of wheat seeds was weighed respectively under 4
different light sources (Groos et al., 2003).
For determination of inedible biomass components, plant tissues were dried in an oven for 48 h at 70 °C before weighing. The
content of neutral detergent fiber (NDF), acid detergent fiber
Fig. 1. Response of straw height to environmental factors under R, RB, RW and W
conditions at different growth stages. Vertical bars are means ± SD. Within each
graph, bars labeled with lowercase letters are significantly different at p 6 0.05.
224
C. Dong et al. / Computers and Electronics in Agriculture 109 (2014) 221–231
reached a maximum during the 21–27 days (Fig. 2B). It is noteworthy that the duration of high photosynthetic rate was longer in RW
condition, which means a large net amount of fixed CO2 and carbohydrate accumulation were more. Though light intensity of W
treatment was lower than others, there was no significant difference between W and R, RB treatments in both photosynthetic rate
and transpiration rate. Besides intensities, light quality also played
an important role in wheat growth and development. Light sources
with different wavelengths affect different photoreceptors of
plants to control pigment synthesis (Stuefer and Huber, 1998).
Supplementary lighting was known to increase shoot and root
dry weight via increased photosynthetic rate in celery, tomato,
broccoli, lettuce, and scallion (Levine and Paré, 2009; Massa
et al., 2008). Furthermore, the photosynthetic rate also fluctuated
during the growth cycle, more or less in phase with the transpiration rate because of different nutrient supplies. The extent of this
fluctuation tends to increase as the transpiration rate increase
(Fig. 2C). Leaf maximum photosynthetic rate can evaluate potential impact of light on the photosynthetic performance (Nhut
et al., 2000). The maximum photosynthetic rate in RW condition
was the largest and RW may have the maximum photosynthetic
potential. Photosynthetic rate was not only affected by photosynthetic pigment content, but also significantly affected by stomatal
conductance, transpiration rate and intercellular CO2 (Ward and
Woolhouse, 1986). Positive correlation between transpiration and
photosynthesis was mostly because of stomatal limitations. Stomatal limitation and the decrease of intercellular CO2 partial pressure could be the main reason for the photosynthesis reduction
(Sui et al., 2012).
3.2. Responses of edible and inedible biomass to different treatments
Fig. 2. Changing characteristics of light intensity on wheat canopy at different
stages (A), curves of variations in the net photosynthetic rate (B) and transpiration
rate (C) for the leaves of wheat plants at different stages.
The soluble sugar content of the edible part of wheat plants
decreased with narrowing spectrum such as in R or RB treatments,
and the accumulation of carbohydrate also decreased (Table 4).
This finding was similar to the cases of birch blades reported by
Sæbø et al., 1995. The contents of rough fat, hemicellulose and
ash in every treatment were relevantly closed, all of them were
at the range of the nutrients of wheat seeds from field. The accumulation of starch grain in mesophyll cells in the blue light was
less in comparison with that in the red light. It might be because
that the red light restrains the export of photosynthate from
blades, thereby increasing the accumulation of starch grain. However, the excessive accumulation of starch grain was helpless for
blade photosynthesis (Bondada and Syvertsen, 2005).
The analysis of variance revealed significant differences among
light treatments for TKW, harvest index and components of inedible biomass of samples (Fig. 3). When wheat was in the single red
light, TKW was 15.3% lower than that in the white light during the
Table 4
The contents of nutrients of wheat seed in different treatments (g/100 g).
Items
Soluble sugar
Carbohydrate
Rough protein
Rough fat
Ash
NDF
Hemicellulose
Cellulose
Lignin
Nitrogen
a
b
Treatment
R
RB
RW
W
7.44 ± 0.42abb
69.54 ± 10.78b
22.68 ± 8.91a
2.02 ± 0.21a
0.33 ± 0.03b
33.91 ± 4.21c
2.47 ± 0.41a
4.02 ± 0.23b
0.79 ± 0.02b
3.65 ± 0.12b
7.64 ± 0.54b
72.12 ± 9.14a
23.03 ± 9.61a
2.05 ± 0.16a
0.63 ± 0.11a
36.72 ± 6.31a
3.15 ± 0.28a
3.26 ± 0.17c
1.16 ± 0.07a
3.86 ± 0.42a
8.01 ± 0.67ab
72.33 ± 12.53a
22.14 ± 6.12a
2.11 ± 0.45a
0.66 ± 0.34a
34.54 ± 9.14b
2.81 ± 0.14a
4.97 ± 0.27a
0.45 ± 0.05c
3.56 ± 0.21b
8.77 ± 0.47a
73.81 ± 5.23a
21.09 ± 3.14a
2.09 ± 0.31a
0.17 ± 0.05c
31.83 ± 5.31d
3.69 ± 0.22a
4.03 ± 0.33b
0.52 ± 0.04c
3.41 ± 0.31c
Mean ± SE.
Mean values with the same letter are not significantly different, based on ANOVA followed by Tukey’s test at P 6 0.05.
225
C. Dong et al. / Computers and Electronics in Agriculture 109 (2014) 221–231
wheat plants, we determined the contents of lignin, cellulose and
hemicelluloses. The results showed that the single red light was
beneficial for the increase of lignin content (Fig. 3C), with maximal
mass fraction of 4.98%. However, when the compound light was
involved, the lignin content decreased to 4.55% in RB, 4.13% in
RW and 4.25% in W treatment. The content of cellulose and hemicelluloses increased in RW and W treatments. Furthermore, the
percentage of cellulose and hemicelluloses from high to low was
RW, W, RB and R, respectively, which was helpful for wheat straw
degradation in BLSS. These observations are consistent with previous study where in the red light was shown to lead to the enhancement of cortical cell activity and the accumulation of lignin in
broad bean seeding (Badiani et al., 1990).
3.3. Planting regime optimization through system identification and
computer simulation
The wheat production experiments were conducted under R,
RB, RW and W. The 8 controllable environmental factors used
1
was average light intensity (X1, PPF), NO
), H2PO
3 (X2, mg L
4
1
1
1
+
2
+
(X3, mg L ), SO4 (X4, mg L ), NH4 (X5, mg L ), K (X6, mg L1),
Mg2+ (X7, mg L1), Ca2+ (X8, mg L1). The desired cultivation result
was reflected on 3 process indicators, i.e., average net photosynthetic rate (Y1, lmol CO2 m2 s1), plant height (Y2, cm) and
transpiration rate (Y3, mmol H2O m2 s1). The experimental data
applied for theoretical researches were preprocessed by removing
outliers, constant offsets, systematic shift, linear trends, highfrequency noise and drift. The quantity of experimental data
available for PCA, system identification and planting regime
optimization was significantly increased through B-spline interpolation, having a reasonably short sampling period of 3 days.
3.3.1. Principal component analysis
There existed strong correlation among these 8 environmental
factors via the deep study and analysis, therefore they could be sufficiently represented by fewer orthogonally comprehensive factors
obtained from PCA, which was beneficial to indentify a state-space
model of WGP with the same number of inputs and outputs due to
reduction of the computation involved.
Because there were also large differences in dimension and
magnitude among original 8 environmental factors, so the environmental factors data used for PCA were gotten by original factor
vectors divided by their respective standard deviation. From Pareto
chart (Fig. 4), the first 3 principal components accounting for 95%
of the variance were selected, and the rest 5 could be negligible
under R, RB, RW and W.
For instance, the matrix of the first 3 principal components
under R as follows:
Fig. 3. TKW (A), final harvest index (B) and components of inedible biomass (C) of
samples in different light treatments. Vertical bars are means ± SD. Bars labeled
with lowercase letters are significantly different at p 6 0.05.
whole life cycle (Fig. 3A). Compared with the white light source,
the harvest index of wheat was higher than that in the RW condition, and the percentage of inedible biomass was lower (Fig. 3B).
These results were much more beneficial to continuous cultivation
under energy confinement and high-recycling conditions. It was
also observed that there was no significant difference between
the RW (46.78%) and W (44.47%) treatments, which were treated
by sustained low intensity light during the whole life cycle. The
results presented in Fig. 3B highlight the fact that power light
intensity played an important role in seed maturation, but using
other measures such as appropriate nutrient solution and light system may solve this limitation. To investigate the influence of combined light sources with different wavelengths on inedible part of
2
0:161
0:347
0:889
3
6 0:407 0:204 0:182 7
7
6
7
6
6 0:376 0:275 0:223 7
7
6
6 0:424 0:073 0:221 7
7
6
M3 ¼ 6
7
6 0:176 0:737 0:228 7
7
6
6 0:364 0:230
0:024 7
7
6
7
6
4 0:384 0:401 0:159 5
0:419 0:028
0:004
The principal components were orthogonal to each other, so the
pseudo-inverse matrix (M1
3 ) of M3 could be written as follows:
T
M1
3 ¼ M3
ð1Þ
where superscript T denoted the transpose of M3. Hence the problem was greatly simplified by replacing the original 8 environmental factors (X) with new 3 variables (Z) as follows:
226
C. Dong et al. / Computers and Electronics in Agriculture 109 (2014) 221–231
Fig. 4. Pareto chart of PCA.
Fig. 5. Dynamic curve of new 3 variables via PCA.
Z ¼ X M 3 ¼ X ik Mkj i ¼ 1; 2; 3; ;n; j ¼ 1;2;3; k ¼ 1;2; 3; ; 8:
ð2Þ
T
T
where Z ¼ ½Z 1 ; Z 2 ; Z 3 , X ¼ ½X 1 ; X 2 ; ; X 8 , and the n was the length
of data, and X could be fully represented by Z (Fig. 5) without redundant information under R, RB, RW and W.
According to Eqs. (1) and (2):
X ¼ Z MT3
ð3Þ
3.3.2. System identification of wheat growth process
The relationship between environmental factors variations and
plant responses was usually very complicated, and generally
regarded as a ‘‘black box’’ process. In the research, all artificial
environmental factors were strictly regulated to change within
their permissible ranges in the course of wheat cultivation, thus
the WGP could be reasonably described as a linear system. Based
on a large number of time-based experimental data including control inputs of new 3 ‘‘environmental factors’’ from PCA and original
output responses of WFP, however, the state-space model of WGP
with general form (Eq. (4)) could be precisely identified under R,
RB, RW and W.
x_ ¼ Ax þ Bu
y ¼ Cx þ Du
ð4Þ
227
C. Dong et al. / Computers and Electronics in Agriculture 109 (2014) 221–231
Fig. 6. Validation of WGP state-space model under R, RB, RW and W.
where A was state matrix, B was input matrix, C was output matrix,
and D was transmission matrix.
For example, the state-space model with order of 5, 3 inputs
and 3 outputs under R was developed by system identification.
The number of delayed inputs was 1, 2, 2, respectively, indicating
the first input was composed of u1(t) and u1(t 1), the second
input was u2(t), u2(t1) and u2(t 2), the third input was u3(t),
u3(t 1) and u3(t 2). The Pade approximation of time delays
was applied to obtain WGP continuous-time state-space representation whose model parameters in Eq. (4) were listed as follows:
3
8:88 4:88 10:72 3:64 12:42
7
6
2:58
24:40 10:23 7
6 5:86 29:59
7
6
A¼6
0:07
1:46 7
7
6 11:22 9:96 27:22
7
6
9:05 5
4 0:07 29:48 0:50 14:69
26:32 12:26
0:07
11:62 18:47
3
2
0
10:44 2:2
7
6
6:21
5 7
6 1:05
7
6
B¼6
1:82 2:61 7
7
6 2:50
7
6
0
0 5
4 2:78
15:08 0:67 8:47
2
3
0:13 3:50 0:15 1:33
0
6
7
C¼4 0
3:61
3:24
0
0:90 5
0:12 5:21 0:50 0:52 10:50
2
3
0
1:01
11:02
6
7
0 5
D ¼ 4 0:13 14:21
0
3:67
0
2
All experimental data were divided into 2 parts, and one part
(60%) was applied for system identification, the rest (40%) for
model validation and verification. From Fig. 6 where all determination coefficients (R2 > 0.9) and standard deviation (STD < 2)
between simulation data (SD) and experimental data (ED) via statistical analysis, it suggested that these state-space models were
highly precise and could meet the needs of the design and
optimization of wheat planting regime under R, RB, RW and W.
Furthermore, for all the state-space models of WGP, the number
of poles was equivalent to zeros, and all poles located in the left
half complex plane, signifying all of them were absolutely stable.
Hence the inverse state-space models with general form (Eq. (5))
of WGP could be numerically obtained correspondingly (Sato,
2008; Mathworks, 2013)
y_ ¼ Ky þ Ju
ð5Þ
x ¼ Py þ Qu
The matrix of K, J, P, Q with the corresponding meaning as A, B, C, D
defined in Eq. (4) were numerically solved in the case of R as
follows:
2
9:21
6
6 2:28
6
K¼6
6 20:21
6
4 9:86
27:39
2
0:21
6
6 0:53
6
J¼6
6 0:24
6
4 0
0:77
2
3:57
6
P ¼ 4 0:03
20:73
12:20
5:43
144:47
41:92
38:51
42:92
3
7
294:80 7
7
434:18
69:22
37:63
780:02 7
7
7
522:95 107:79 56:66
853:71 5
2663:59 581:38 216:73 4692:07
3
0
2:90
7
7:79
28:38 7
7
18:60
72:55 7
7
7
20:73
80:38 5
112:30 435:44
3
177:29 38:55 15:08 309:97
7
1:42 0:14 0:14 2:86 5
0:01
0:45
0:03
0:13
0:26
2
3
0
7:45 28:88
6
7
0
0:27 5
Q ¼4 0
0:09
0
0:03
Based on the inverse system model and ideal output responses
of positive system of WGP, the optimal control inputs of WGP
could be obtained from backward prediction.
228
C. Dong et al. / Computers and Electronics in Agriculture 109 (2014) 221–231
Fig. 7. Prescribed output response curves of wheat through B-spline interpolation.
3.3.3. Planting regime optimization of wheat
The preferred output responses of average net photosynthetic
rate, plant height and transpiration rate of wheat to environmental
factors variation at certain time points could be predetermined
basing on related mechanisms of plant physiology, ecology and
cultivating experiments under the artificial environment. Due to
relatively short interval time between the adjacent two output
points, the smooth output response curves could be also defined
via B-spline interpolation (Fig. 7).
As these 3 prescribed output response curves were used as
inputs of the inverse state-space models of WGP, the optimum
dynamics of new 3 ‘‘environmental factors’’ in the principal component space could be determined correspondingly, and subsequently restored to optimized time-domain control inputs of 8
environmental factors (Fig. 8) with primitively physical
significances through linear transformation using Eq. (3) and then
multiplication by their respective standard deviation.
Similarly, the 8 optimized environmental factors which were
inputted to the positive system of WGP via PCA transformation
could exactly obtain 3 predetermined responses of WGP (Fig. 9).
From the viewpoint of signals and systems science, the physical
signal may be considered as combinations of step signals, ramp
signals, parabolic signals, sinusoidal signals, stochastic signals,
and so on (Ogata, 2005a,b). Hence the influence of these signals
on dynamic behaviors of WGP have been investigated through a
large amount of digital simulations, and the result proved that
the positive system and inverse system possessed high accuracy
with good dynamic performance including precise transient and
steady-state response characteristics to these signals and their
combinations (Fig. 10).
In this situation, it is to be expected that the ideal dynamic
response specification of WGP could be achieved if these optimal
8 control inputs used as the planting regime were applied in
practice, which would greatly increase yield and improve quality
of wheat cultivating in BLSS, greenhouse and other agricultural
facilities.
The main way to optimize farming practices for environmentalfriendly yields and to reduce costs is by applying precision agriculture (Houles et al., 2007), which requires the efficient supply of
light and nutrients. Plant nutritional and physiological status
affects plant reflectance, absorbance and transmittance measurements. Spectral light changes evoke different morphogenetic and
photosynthetic responses that can vary among different plant species. Such photoresponses are of practical importance in recent
plant cultivation technologies, since the feasibility of tailoring illumination spectra purposefully enables one to control plant growth,
development, and nutritional quality (Bourget, 2008; Massa et al.,
2008; Morrow, 2008). Red and blue lights have the greatest impact
on plant growth because they are the major energy sources for
photosynthetic CO2 assimilation in plants (Cosgrove, 1981; Lin
et al., 2013). However, previous study has shown that wheat is
not sensitive to the blue light dose induction(Cope and Bugbee,
2013; Dougher and Bugbee, 2001). The addition of W LED light
may have further increased plant growth, since W LED light might
better penetrate the plant canopy than RB LED light for photosynthesis. Perhaps, RW treatment achieved a balanced spectral environment by supplementing a favorable amount of W light to
plants. Among plants treated with 4 light qualities, the RW-treated
plants were selected as the most preferable item.
In addition to the light source, ionic concentration (NH+4, K+,
2
Mg2+, Ca2+, NO
3 , H2PO4 , SO4 ) plays a key role for absorbing by
plant roots. Overall, absorption of mineral nutrient reached the
top level from 20 to 40 days after planting (Fig. 8). Plants absorb
nitrogen as a mineral nutrient mainly from soil, and it can be
may come in the form of ammonium (NH+4) and nitrate (NO
3 ),
which is fundamental for the photosynthesis and respiration
C. Dong et al. / Computers and Electronics in Agriculture 109 (2014) 221–231
229
Fig. 8. Optimal 8 control inputs to WGP for generation of prescribed 3 output responses.
Fig. 9. Responses of WGP obtained from upfront defined scenario and positive system prediction.
process. Potassium (K) is a major macronutrient in plants, involved
in many essential processes, such as osmoregulation and cell
extension, stomatal regulation, activation of enzymes, protein
synthesis (Pettigrew, 2008). Magnesium (Mg) is an essential macronutrient and has major physiological and molecular roles in
plants, such as being a component of the chlorophyll molecule, a
cofactor for many enzymatic processes associated with phosphorylation, dephosphorylation, and the hydrolysis of various compounds, and a bridging element for the aggregation of ribosome
subunits necessary for protein synthesis. Partitioning at the cellular level also provides the necessary concentration gradients for
calcium (Ca) to be utilized as a second messenger, enabling cells
to sense and physiologically respond to a wide range of environmental signals. Mineral analysis is a tool to identify the nutrition
status of wheat plants and several phosphorus (P) indicators
derived thereof are available. P also plays a role in signal transduction, membrane biosynthesis, and root development and function.
2
Critical concentrations of NH+4, K+, Mg2+, Ca2+, NO
3 , H2PO4 , SO4 for
230
C. Dong et al. / Computers and Electronics in Agriculture 109 (2014) 221–231
Fig. 10. Dynamic response characteristics of positive and inverse system of WGP (A: inputs of positive system; B: outputs (Inputs) of positive (inverse) system; C: outputs of
inverse system which is exactly A).
wheat plants are also a major gap. Models are needed that integrate the direct effect of the nutrients on wheat growth and development during the whole life cycle.
The state-space model developed by system identification could
precisely capture the dynamic characteristics of the WGP in the
specifically artificial environment, and nonlinearities in the WGP
were inevitably neglected. Hence the state-space model of the
WGP might be further calibrated by taking the nonlinear regressors
into consideration for more generalized application and effective
planting regime establishment.
Beihang University (Grant No. BUAA-VR-14KF-07) and the Innovation Foundation of BUAA for PhD Graduates.
Appendix A. Supplementary material
Supplementary data associated with this article can be found, in
the online version, at http://dx.doi.org/10.1016/j.compag.2014.10.
013.
References
4. Conclusion
Our results clearly demonstrate that, the light quality is a very
significant environmental factor that affects wheat growth. The
plants were spindling and the output was very low when wheat
was in the single red light. However, after a certain amount of
the blue light or white light was mixed into the red light, the tendency of spindling was restrained gradually. The wheat harvest
index arrived at the highest and the lignin content of inedible biomass was the lowest in RW condition, which was more conductive
to substance recycling. The planting regime was theoretically and
upfront optimized through system identification, inverse system
model derivation and computer simulation.
Acknowledgements
This work was supported by the Ministry of Science and Technology of China (No. 2012DFR30570), the open funding project of
State Key Laboratory of Virtual Reality Technology and Systems,
Ali, M.B., Hahn, E.J., Paek, K.Y., 2005. Effects of light intensities on antioxidant
enzymes and malondialdehyde content during short-term acclimatization on
micropropagated Phalaenopsis plantlet. Environ. Exp. Bot. 54, 109–120.
Avercheva, O., Berkovich, Y.A., Erokhin, A., Zhigalova, T., Pogosyan, S., Smolyanina,
S., 2009. Growth and photosynthesis of Chinese cabbage plants grown under
light-emitting diode-based light source. Russ. J. Plant Physiol. 56, 14–21.
Badiani, M., De Biasi, M.G., Felici, M., 1990. Soluble peroxidase from winter wheat
seedlings with phenoloxidase-like activity. Plant Physiol. 93, 489–494.
Barta, D., Tibbitts, T., Bula, R., Morrow, R., 1992. Evaluation of light emitting diode
characteristics for a space-based plant irradiation source. Adv. Space Res. 12,
141–149.
Bondada, B.R., Syvertsen, J.P., 2005. Concurrent changes in net CO2 assimilation and
chloroplast ultrastructure in nitrogen deficient citrus leaves. Environ. Exp. Bot.
54, 41–48.
Bourget, C.M., 2008. An introduction to light-emitting diodes. HortScience 43,
1944–1946.
Chapin, F.S., 1980. The mineral nutrition of wild plants. Annu. Rev. Ecol. Syst. 11,
233–260.
Chatterjee, S.K., Ghosh, S., Das, S., Manzella, V., Vitaletti, A., Masi, E., Santopolo, L.,
Mancuso, S., Maharatna, K., 2014. Forward and inverse modelling approaches
for prediction of light stimulus from electrophysiological response in plants.
Measurement 53, 101–116.
Chaturvedi, G., Ingram, K., 1989. Growth and yield of lowland rice in response to
shade and drainage. Philippine J. Crop Sci. 14, 61–67.
C. Dong et al. / Computers and Electronics in Agriculture 109 (2014) 221–231
Cope, K.R., Bugbee, B., 2013. Spectral effects of three types of white light-emitting
diodes on plant growth and development: absolute versus relative amounts of
blue light. HortScience 48, 504–509.
Cosgrove, D., 1981. Rapid suppression of growth by blue light. Plant Physiol. 67,
584–590.
Despommier, D., 2009. The rise of vertical farms. Sci. Am. 301, 80–87.
Despommier, D., 2013. Farming up the city: the rise of urban vertical farms. Trends
Biotechnol. 31, 388–389.
Dong, C., Fu, Y., Liu, G., Liu, H., 2014a. Growth, photosynthetic characteristics,
antioxidant capacity and biomass yield and quality of wheat (Triticum aestivum
L.) exposed to LED light sources with different spectra combinations. J. Agron.
Crop Sci. 200, 219–230.
Dong, C., Fu, Y., Liu, G., Liu, H., 2014b. Low light intensity effects on the growth,
photosynthetic characteristics, antioxidant capacity, yield and quality of wheat
(Triticum aestivum L.) at different growth stages in BLSS. Adv. Space Res. 53,
1557–1566.
Dougher, T.A., Bugbee, B., 2001. Differences in the response of wheat, soybean and
lettuce to reduced blue radiation. Photochem. Photobiol. 73, 199–207.
Flore, J., 1980. The effect of light on cherry trees. Annual report Michigan State
Horticultural Society.
Gao, J.F., 2000. Experimental Technology in Plant Physiology. World Books
Publishing Company, Xi’an.
Gc, C., Sz, W., 1994. Wheat Ecology of Quality. China Science and Technology Press,
Beijing.
Gitelson, J.I., Lisovsky, G., 2004. Man-made Closed Ecological Systems. CRC Press.
Goins, G., Yorio, N., Sanwo, M., Brown, C., 1997. Photomorphogenesis,
photosynthesis, and seed yield of wheat plants grown under red lightemitting diodes (LEDs) with and without supplemental blue lighting. J. Exp.
Bot. 48, 1407–1413.
Groos, C., Robert, N., Bervas, E., Charmet, G., 2003. Genetic analysis of grain proteincontent, grain yield and thousand-kernel weight in bread wheat. Theor. Appl.
Genet. 106, 1032–1040.
Hoagland, D.R., Arnon, D.I., 1950. The water-culture method for growing plants
without soil. Circular California Agric. Exp. Station, 347.
Hocking, P., 1994. Dry-matter production, mineral nutrient concentrations, and
nutrient distribution and redistribution in irrigated spring wheat. J. Plant Nutr.
17, 1289–1308.
Houles, V., Guerif, M., Mary, B., 2007. Elaboration of a nitrogen nutrition indicator
for winter wheat based on leaf area index and chlorophyll content for making
nitrogen recommendations. Eur. J. Agron. 27, 1–11.
Hu, D., Zhou, R., Sun, Y., Tong, L., Li, M., Zhang, H., 2012. Construction of closed
integrative system for gases robust stabilization employing microalgae
peculiarity and computer experiment. Ecol. Eng. 44, 78–87.
Hussey, G., 1963. Growth and development in the young tomato: I. The effect of
temperature and light intensity on growth of the shoot apex and leaf primordia.
J. Exp. Bot. 14, 316–325.
Kim, H.-H., Goins, G.D., Wheeler, R.M., Sager, J.C., 2004. Green-light
supplementation for enhanced lettuce growth under red-and blue-lightemitting diodes. HortScience 39, 1617–1622.
Lasseur, C., Verstraete, W., Gros, J., Dubertret, G., Rogalla, F., 1996. MELISSA: a
potential experiment for a precursor mission to the Moon. Adv. Space Res. 18,
111–117.
Levine, L.H., Paré, P.W., 2009. Antioxidant capacity reduced in scallions grown under
elevated CO2 independent of assayed light intensity. Adv. Space Res. 44, 887–
894.
Li, L., Zhao, Z., Liu, H., 2013. Feasibility of feeding yellow mealworm (Tenebrio
molitor L.) in bioregenerative life support systems as a source of animal protein
for humans. Acta Astronaut. 92, 103–109.
Li, Q., Kubota, C., 2009. Effects of supplemental light quality on growth and
phytochemicals of baby leaf lettuce. Environ. Exp. Bot. 67, 59–64.
Lin, K.H., Huang, M.Y., Huang, W.D., Hsu, M.H., Yang, Z.W., Yang, C.M., 2013. The
effects of red, blue, and white light-emitting diodes on the growth,
231
development, and edible quality of hydroponically grown lettuce (Lactuca
sativa L. var. capitata). Sci. Hortic. 150, 86–91.
Lu, N., Maruo, T., Johkan, M., Hohjo, M., Tsukagoshi, S., Ito, Y., Ichimura, T.,
Shinohara, Y., 2012. Effects of supplemental lighting with light-emitting
diodes(LEDs) on tomato yield and quality of single-truss tomato plants grown
at high planting density. Environ. Control Biol. 50, 63–74.
Massa, G.D., Kim, H.-H., Wheeler, R.M., Mitchell, C.A., 2008. Plant productivity in
response to LED lighting. HortScience 43, 1951–1956.
Mathworks Inc., 2013. MATLAB/System Identification Toolbox & Control System
Toolbox. <www.mathworks.com>.
Morrow, R.C., 2008. LED lighting in horticulture. HortScience 43, 1947–1950.
Nhut, D.T., Hong, L., Watanabe, H., Goi, M., Tanaka, M. 2000. Growth of banana
plantlets cultured in vitro under red and blue light-emitting diode (LED)
irradiation source. International Symposium on Tropical and Subtropical Fruits
575.
Normanly, J., Grisafi, P., Fink, G.R., Bartel, B., 1997. Arabidopsis mutants resistant to
the auxin effects of indole-3-acetonitrile are defective in the nitrilase encoded
by the NIT1 gene. Plant Cell Online 9, 1781–1790.
Ogata, K., 2005a. Modern Control Engineering, second ed. China Machine Press,
Beijing, pp. 80–430.
Ogata, K., 2005b. Discrete-time Control Systems. China Machine Press, Beijing, pp.
173–680.
Peng, Z., Wang, M., Li, F., Lv, H., Li, C., Xia, G., 2009. A proteomic study of the
response to salinity and drought stress in an introgression strain of bread
wheat. Mol. Cell. Proteomics 8, 2676–2686.
Pettigrew, W.T., 2008. Potassium influences on yield and quality production for
maize, wheat, soybean and cotton. Physiol. Plant. 133, 670–681.
Sato, M., 2008. Inverse system design for LPV systems using parameter-dependent
Lyapunov functions. Automatica 44, 1072–1077.
Sæbø, A., Krekling, T., Appelgren, M., 1995. Light quality affects photosynthesis and
leaf anatomy of birch plantlets in vitro. Plant Cell, Tissue Organ Cult. 41, 177–
185.
Schneider, S., Ziegler, C., Melzer, A., 2006. Growth towards light as an adaptation to
high light conditions in Chara branches. New Phytol. 172, 83–91.
Stuefer, J.F., Huber, H., 1998. Differential effects of light quantity and spectral light
quality on growth, morphology and development of two stoloniferous
Potentilla species. Oecologia 117, 1–8.
Sui, X.-L., Mao, S.-L., Wang, L.-H., Zhang, B.-X., Zhang, Z.-X., 2012. Effect of low light
on the characteristics of photosynthesis and chlorophyll a fluorescence during
leaf development of sweet pepper. J. Integrative Agric. 11, 1633–1643.
Tikhomirov, A., Ushakova, S., Manukovsky, N., Lisovsky, G., Kudenko, Y.A., Kovalev,
V., Gubanov, V., Barkhatov, Y.V., Gribovskaya, I., Zolotukhin, I., 2003. Mass
exchange in an experimental new-generation life support system model based
on biological regeneration of environment. Adv. Space Res. 31, 1711–1720.
Tikhomirov, A., Ushakova, S., Velichko, V., Tikhomirova, N., Kudenko, Y.A.,
Gribovskaya, I., Gros, J.-B., Lasseur, C., 2011. Assessment of the possibility of
establishing material cycling in an experimental model of the bio-technical life
support system with plant and human wastes included in mass exchange. Acta
Astronaut. 68, 1548–1554.
Tong, L., Hu, D., Liu, H., Li, M., Fu, Y., Jia, B., Du, F., Hu, E., 2011. Gas exchange
between humans and multibiological life support system. Ecol. Eng. 37, 2025–
2034.
Van Soest, P.J., Robertson, J.B., Lewis, B.A., 1991. Methods for dietary fiber, neutral
detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J.
Dairy Sci. 74, 3583–3597.
Vergeer, L., Aarts, T., De Groot, J., 1995. The ‘wasting disease’ and the effect of abiotic
factors (light intensity, temperature, salinity) and infection with Labyrinthula
zosterae on the phenolic content of Zostera marina shoots. Aquat. Bot. 52, 35–44.
Ward, D., Woolhouse, H., 1986. Comparative effects of light during growth on the
photosynthetic properties of NADP-ME type C4 grasses from open and shaded
habitats. I. Gas exchange, leaf anatomy and ultrastructure. Plant, Cell Environ. 9,
261–270.