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JACIII Vol.25 No.5 pp. 618-624
doi: 10.20965/jaciii.2021.p0618
(2021)

Paper:

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

Christan Hail R. Mendigoria*,†, Heinrick L. Aquino*, Oliver John Y. Alajas*, Ronnie S. Concepcion II*, Elmer P. Dadios**, Edwin Sybingco*, Argel A. Bandala*, and Ryan Rhay P. Vicerra**

*Electronics and Communications Engineering Department, De La Salle University
2401 Taft Ave, Malate, Manila 1004, Philippines

**Manufacturing Engineering and Management Department, De La Salle University
2401 Taft Ave, Malate, Manila 1004, Philippines
†Corresponding author

Received:
March 1, 2021
Accepted:
May 21, 2021
Published:
September 20, 2021
Keywords:
computational intelligence, computer vision, lettuce seeds, seed variety classification
Abstract
Varietal Classification of <i>Lactuca Sativa</i> Seeds Using an Adaptive Neuro-Fuzzy Inference System Based on Morphological Phenes

Lettuce seed classification (FIS) model

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.

Cite this article as:
Christan Hail R. Mendigoria, Heinrick L. Aquino, Oliver John Y. Alajas, Ronnie S. Concepcion II, Elmer P. Dadios, Edwin Sybingco, Argel A. Bandala, and Ryan Rhay P. Vicerra, “Varietal Classification of Lactuca Sativa Seeds Using an Adaptive Neuro-Fuzzy Inference System Based on Morphological Phenes,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.5, pp. 618-624, 2021.
Data files:
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Last updated on Oct. 22, 2021