Seed Architectural Phenes Prediction and Variety Classification of Dry Beans (Phaseolus vulgaris) Using Machine Learning Algorithms

Christan Hail Mendigoria, Ronnie Concepcion, Elmer Dadios, Heinrick Aquino, Oliver John Alaias, Edwin Sybingco, Argel Bandala, Ryan Rhay Vicerra, Joel Cuello

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

1 Scopus citations

Abstract

Proper identification and categorization of seeds at an earlier stage of the cultivation process is an imperative procedure that contributes to better crop quality and higher production yield. As a strategy to supplement this procedure, integration of computer vision approach and machine learning algorithms including gaussian process regression (GPR), decision trees for regression (RT) and classification (CT), support vector machine regression (SVMR), k-nearest neighbors (KNN), linear discriminant analysis (LDA) classifier, and Naïve Bayes (NB) classifier are explored in this study to predict the extended morphological features (solidity, roundness, compactness) and variety classification of dry bean (Phaseolus vulgaris L.). A total of 13, 611 image samples were used. CIELab color channel thresholding was applied in segmenting bean pixels and region properties for extracting the morphological features (bean biomass area, perimeter, major and minor axis lengths, convex area, eccentricity, extent, equivalent diameter, and axis length proportionality, shape factors, roundness, solidity, compactness). Based on RMSE and MAE performances, the optimized GPR is the most reliable model for predicting seed solidity, and regression tree for both seed roundness and compactness. Classification models with seven morphological predictors (LDA7, KNN7, CT7, NB7) exhibited sensitive classification performance, all having accuracies greater than 90%. Further, KNN7 bested out other models with 93.69% accuracy, 93.64% precision, 93.66% specificity, and 93.69% f1-score. The developed machine learning models are innovative approaches in the seed variety classification and phenotyping of dry bean seeds.

Original languageEnglish (US)
Title of host publicationProceedings of R10-HTC 2021
Subtitle of host publication9th Edition of IEEE Region 10 Humanitarian Technology Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665432405
DOIs
StatePublished - 2021
Event9th Edition of IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2021 - Bangalore, India
Duration: Sep 30 2021Oct 2 2021

Publication series

NameIEEE Region 10 Humanitarian Technology Conference, R10-HTC
Volume2021-September
ISSN (Print)2572-7621

Conference

Conference9th Edition of IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2021
Country/TerritoryIndia
CityBangalore
Period9/30/2110/2/21

Keywords

  • Computational intelligence
  • Computer vision
  • Dry bean
  • Feature prediction
  • Variety classification

ASJC Scopus subject areas

  • Waste Management and Disposal
  • Development
  • Geography, Planning and Development
  • Pollution
  • Renewable Energy, Sustainability and the Environment
  • Environmental Engineering

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