TY - JOUR
T1 - Adaptive fertigation system using hybrid vision-based lettuce phenotyping and fuzzy logic valve controller towards sustainable aquaponics
AU - Concepcion, Ronnie S.
AU - Lauguico, Sandy C.
AU - Alejandrino, Jonnel D.
AU - Bandala, Argel A.
AU - Sybingco, Edwin
AU - Vicerra, Ryan Rhay P.
AU - Dadios, Elmer P.
AU - Cuello, Joel L.
N1 - Publisher Copyright:
© Fuji Technology Press Ltd.
PY - 2021/9
Y1 - 2021/9
N2 - Sustainability is a major challenge in any plant factory, particularly those involving precision agriculture. In this study, an adaptive fertigation system in a three-tier nutrient film technique aquaponic system was developed using a non-destructive vision-based lettuce phenotype (VIPHLET) model integrated with an 18-rule Mamdani fuzzy inference system for nutrient valve control. Four lettuce phenes, that is, fresh weight, chlorophylls a and b, and vitamin C concentrations as outputted by the genetic programming-based VIPHLET model were optimized for each growth stage by injecting NPK nutrients into the mixing tank, as determined based on leaf canopy signatures. This novel adaptive fertigation system resulted in higher nutrient use efficiency (99.678%) and lower chemical waste emission (14.108 mg L-1) than that by manual fertigation (92.468%, 178.88 mg L-1). Overall, it can improve agricultural malpractices in relation to sustainable agriculture.
AB - Sustainability is a major challenge in any plant factory, particularly those involving precision agriculture. In this study, an adaptive fertigation system in a three-tier nutrient film technique aquaponic system was developed using a non-destructive vision-based lettuce phenotype (VIPHLET) model integrated with an 18-rule Mamdani fuzzy inference system for nutrient valve control. Four lettuce phenes, that is, fresh weight, chlorophylls a and b, and vitamin C concentrations as outputted by the genetic programming-based VIPHLET model were optimized for each growth stage by injecting NPK nutrients into the mixing tank, as determined based on leaf canopy signatures. This novel adaptive fertigation system resulted in higher nutrient use efficiency (99.678%) and lower chemical waste emission (14.108 mg L-1) than that by manual fertigation (92.468%, 178.88 mg L-1). Overall, it can improve agricultural malpractices in relation to sustainable agriculture.
KW - Computer vision
KW - Fertigation system
KW - Fuzzy logic
KW - Lettuce phenotype model
KW - Precision agriculture
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UR - http://www.scopus.com/inward/citedby.url?scp=85115321447&partnerID=8YFLogxK
U2 - 10.20965/jaciii.2021.p0610
DO - 10.20965/jaciii.2021.p0610
M3 - Article
AN - SCOPUS:85115321447
SN - 1343-0130
VL - 25
SP - 610
EP - 617
JO - Journal of Advanced Computational Intelligence and Intelligent Informatics
JF - Journal of Advanced Computational Intelligence and Intelligent Informatics
IS - 5
ER -