A Hybrid of Extreme Learning Machine and Cellular Neural Network Segmentation in Mangrove Fruit Classification

Mangroves are a collection of plants that inhabit the intertidal zone, namely the area between the lowest and highest points reached by the tide. Overall, mangroves provide a range of advantages, including the prevention of coastal erosion, the inhibition of seawater intrusion onto land leading to brackish groundwater, and serving as habitats and food sources for diverse animal species. In addition, many types of mangrove fruit have been used as sustenance for humans and as ingredients in processed food products. Mangrove fruit has a considerable variety of species, each characterized by distinct forms. At now, farmers and the general public rely only on visual observation to identify mangrove fruit species. Consequently, their ability to accurately detect the correct species is not guaranteed. In order to address this issue, this study employs digital image processing using the Extreme Learning Machine technique to facilitate the identification of various kinds and varieties of mangrove fruit by the general public and farmers. The study utilizes gray-scaling and Contrast Enhancement as image processing methods, while segmentation is performed by the use of the Cellular Neural Network approach. Following extensive testing in this study, it was determined that the used methodology effectively identified several species of mangrove fruit. The results yielded an accuracy rate of 94.11% for extracting shape, texture, and color elements, and accuracy rate of 99.63% for extracting texture and color features.

@article{Rahmat2024,
  title     = {A Hybrid of Extreme Learning Machine and Cellular Neural Network Segmentation in Mangrove Fruit Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Romi Fadillah Rahmat and Opim Salim Sitompul and Maya Silvi Lydia and Fahmi and Shifani Adriani Ch and Pauzi Ibrahim Nainggolan and Riza Sulaiman},
  doi       = {10.14569/IJACSA.2024.0150881},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150881}
}