PIGMENTnet: Chlorophyll-b Prediction of Lactuca Sativa Leaf under Hybrid Genetic Algorithm and Recurrent Neural Network

Heinrick L. Aquino, Ronnie S. Concepcion, Ryan Rhay Vicerra, Christan Hail Mendigoria, Oliver John Alajas, Elmer P. Dadios, Joel Cuello

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

8 Scopus citations


Chlorophyll content is an imperative indicator of lettuce (Lactuca Sativa) health status. Through computational intelligence, this paper bestowed a noninvasive, accurate, cost-effective ensemble of machine learning algorithms for chlorophyll-b concentration prediction. A total of 107 images of loose-leaf lettuce var. Altima from an aquaponic farm situated in Rizal province in the Philippines was utilized. By employing CIELab color space, the leaf canopies were segmented and extracted with 18-feature predictors. The regression tree ranked and selected 10 selected significant leaf features (spectral: R, G, S, a*, b*, Cr; morphological: canopy area; textural: contrast, correlation, and homogeneity). A fitness function that optimized the recurrent neural network architecture was constructed using GPTIPSv2 which is a symbolic multigene regression (SMGR) tool. This convergence function was the main element in developing a genetic algorithm (GA)-optimized recurrent neural network model considered as the PIGMENTnet. It provides the optimal quantity of neurons in each of the three hidden layers in neural network architecture. A 75-100-10 conglomeration of neurons in each layer was recommended. The RMSE (0.1486), R2 (0.9998), and MAE (0.0751) results of PIGMENTnet surpassed the unoptimized RNN. Based on these findings, it implies that the developed PIGMENTnet is an effective Chl-b concentration predictor as it provided highly accurate and sensitive results than the sole RNN model.

Original languageEnglish (US)
Title of host publicationTENCON 2021 - 2021 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665495325
StatePublished - 2021
Event2021 IEEE Region 10 Conference, TENCON 2021 - Auckland, New Zealand
Duration: Dec 7 2021Dec 10 2021

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Conference2021 IEEE Region 10 Conference, TENCON 2021
Country/TerritoryNew Zealand


  • chlorophyll-b
  • computer vision
  • genetic algorithm
  • recurrent neural network

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering


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