Adaptive fertigation system using hybrid vision-based lettuce phenotyping and fuzzy logic valve controller towards sustainable aquaponics

Ronnie S. Concepcion, Sandy C. Lauguico, Jonnel D. Alejandrino, Argel A. Bandala, Edwin Sybingco, Ryan Rhay P. Vicerra, Elmer P. Dadios, Joel L. Cuello

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)610-617
Number of pages8
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume25
Issue number5
DOIs
StatePublished - Sep 2021

Keywords

  • Computer vision
  • Fertigation system
  • Fuzzy logic
  • Lettuce phenotype model
  • Precision agriculture

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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