Varietal Classification of Lactuca Sativa Seeds Using an Adaptive Neuro-Fuzzy Inference System Based on Morphological Phenes

Seed varieties are often differentiated via the manual and subjective classification of their external textural, spectral, and morphological biosignatures. This traditional method of manually inspecting seeds is inefficient and unreliable for seed phenotyping. The application of computer vision is a...

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Bibliographic Details
Published in:Journal of advanced computational intelligence and intelligent informatics 2021-09, Vol.25 (5), p.618-624
Main Authors: Mendigoria, Christan Hail R., Aquino, Heinrick L., Alajas, Oliver John Y., II, Ronnie S. Concepcion, Dadios, Elmer P., Sybingco, Edwin, Bandala, Argel A., Vicerra, Ryan Rhay P.
Format: Article
Language:English
Subjects:
Adaptive systems
Artificial intelligence
Artificial neural networks
Classification
Computer vision
Fuzzy logic
Icebergs
Morphology
Roundness
Seeds
Shape factor
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Summary:Seed varieties are often differentiated via the manual and subjective classification of their external textural, spectral, and morphological biosignatures. This traditional method of manually inspecting seeds is inefficient and unreliable for seed phenotyping. The application of computer vision is an ideal solution allied with computational intelligence. This study used Lactuca sativa seed variants, which are commercially known as grand rapid, Chinese loose-leaf, and iceberg (which serves as noise data for extended model evaluation), in determining their corresponding classifications based on the extended morphological phenes using computational intelligence. Red-green-blue (RGB) imaging was employed for individual kernels. Extended morphological phenes, that is, solidity, roundness, compactness, and shape factors, were computed based on seed architectural traits and used as predictors to discriminate among the three cultivars. The suitability of ANFIS, NB, and CT was explored using a limited dataset. A mean accuracy of 100% was manifested in ANFIS; thus, it was proved to be the most reliable model.
ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2021.p0618