Aquaphotomics determination of nutrient biomarker for spectrophotometric parameterization of crop growth primary macronutrients using genetic programming
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- @Article{CONCEPCION:2021:IPA,
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author = "Ronnie Concepcion and Sandy Lauguico and
Jonnel Alejandrino and Elmer Dadios and Edwin Sybingco and
Argel Bandala",
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title = "Aquaphotomics determination of nutrient biomarker for
spectrophotometric parameterization of crop growth
primary macronutrients using genetic programming",
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journal = "Information Processing in Agriculture",
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year = "2021",
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ISSN = "2214-3173",
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DOI = "doi:10.1016/j.inpa.2021.12.007",
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URL = "https://www.sciencedirect.com/science/article/pii/S2214317321000998",
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keywords = "genetic algorithms, genetic programming,
Aquaphotomics, Plant nutrients, Physicochemical
composition, Spectrophotometry, Water quality
monitoring",
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abstract = "Water quality assessment is currently based on
time-consuming and costly laboratory procedures and
numerous expensive physicochemical sensors deployment.
In response to the trend of device minimization and
reduced outlays in sustainable aquaponic water
monitoring, the integration of aquaphotomics and
computational intelligence is presented in this paper.
This study used the combination of temperature, pH, and
electrical conductivity sensors in predicting crop
growth primary macronutrient concentration (nitrate,
phosphate, and potassium (NPK)), thus, limiting the
number of deployed sensors. A total of 220 water
samples collected from an outdoor artificial aquaponic
pond were temperature perturbed from 16 to 36 degreeC
with 2 degreeC increments to mimic ambient range, which
varies water compositional structure. Aquaphotomics was
applied on ultraviolet, visible light, and
near-infrared spectral regions, 100 to 1 000 nm, to
determine NPK compounds. Principal component analysis
emphasized nutrient dynamics through selecting the
highly correlated water absorption bands resulting in
250 nm, 840 nm, and 765 nm for nitrate, phosphate, and
potassium respectively. These activated water bands
were used as wavelength protocols to
spectrophotometrically measure macronutrient
concentrations. Experiments have shown that multigene
symbolic regression genetic programming (MSRGP)
obtained the optimal performance in parameterizing and
predicting nitrate, phosphate, and potassium
concentrations based on water physical properties with
an accuracy of 87.63percent, 88.73percent, and
99.91percent, respectively. The results have shown the
established 4-dimensional nutrient dynamics map reveals
that temperature significantly strengthens nitrate and
potassium above 30 degreeC and phosphate below 25
degreeC with pH and electrical conductivity ranging
between 7 and 8 and 0.1 to 0.2 mS cm-1 respectively.
This novel approach of developing a physicochemical
estimation model predicted macronutrient concentrations
in real-time using physical limnological sensors with a
50percent reduction of energy consumption. This same
approach can be extended to measure secondary
macronutrients and micronutrients",
- }
Genetic Programming entries for
Ronnie S Concepcion II
Sandy Lauguico
Jonnel D Alejandrino
Elmer Jose P Dadios
Edwin Sybingco
Argel A Bandala
Citations