Indirect Prediction of Aquaponic Water Nitrate Concentration Using Hybrid Genetic Algorithm and Recurrent Neural Network

Oliver John Alajas, Ronnie Concepcion, Ryan Rhay Vicerra, Argel Bandala, Edwin Sybingco, Elmer Dadios, Joel Cuello, Vanessa Fonseca

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Nitrate concentration contained in the aquaponic water has a crucial effect that influences the growth of lettuce crops. However, most of the existing methods of testing its presence remain mostly destructive, expensive, utilize multiple sensors. To address this issue, a hybrid method of indirectly identifying the nitrate concentration present in aquaponic water of lettuce growth chamber was developed, bridging the integration of computer vision and computational intelligence. The dataset is composed of 720 images of loose-leaf lettuce taken from an aquaponic vertical farm located at Morong, Rizal, Philippines. Graph-cut segmentation was used to segment vegetative green pixels from the background. A regression tree (RTree) multi-feature dimensionality reduction technique was employed to reduce the number of significant features to 15 (a∗, correlation, B, R, S, Cb, contrast, H, Cr, energy, mathrm{b}{star}, entropy, homogeneity, G, and L). To determine the number of optimal hidden layers for the recurrent neural network model, a multigene symbolic regression (MGSR) tool called GPTIPSv2 was employed to derive a fitness function. This function was deployed for optimization using Genetic Algorithm which led to a 107-85-69 hidden-neuron-combination pick. The GA-RNN15 network surpassed its unoptimized version with 10.25% higher mathrm{R}{2} for training, 12.28% higher mathrm{R}{2} for validation and 7.28% higher mathrm{R}{2} for testing. The findings proved that a nondestructive, cost-efficient, and accurate way of indirectly identifying nitrate concentration levels in aquaponic farm chambers is feasible.

Original languageEnglish (US)
Title of host publication2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665401678
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021 - Manila, Philippines
Duration: Nov 28 2021Nov 30 2021

Publication series

Name2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021

Conference

Conference2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021
Country/TerritoryPhilippines
CityManila
Period11/28/2111/30/21

Keywords

  • aquaphotomics
  • computer vision
  • genetic algorithm
  • lettuce cultivation
  • neural networks
  • nitrate concentration

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Human-Computer Interaction
  • Information Systems
  • Information Systems and Management
  • Environmental Science (miscellaneous)
  • Control and Optimization

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