Classifying habitat characteristics of wetlands using a self-organizing map

Kim, Seong-Hyeon; Cho, Kwang-Jin; Kim, Taesu; Lee, Changsu; Dhakal, Thakur; Jang, Gab-Sue

doi:10.1016/j.ecoinf.2023.102048

Cited by 5 publications

(1 citation statement)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other related research is entitled Implementing Artificial Neural Networks Using the Self-Organizing Map Method in Image Classification of Snapper Fish Types (Triwibowo & Sela, 2023). This research aims to implement an artificial neural network using the Self Organizing Map (SOM) method, which is used to classify types of snappers based on each snapper's color and texture characteristics (Asri & Wulanningrum, 2021;Kim et al, 2023). This research has several stages: image acquisition, image segmentation, color and texture feature extraction, image classification using a Self-Organizing Map, and testing the existing model (Cui et al, 2023;Shi et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Identification of Fresh and Unfresh Fish Based on Eye Image Using The Self-Organizing Maps (SOM) Method

Rafafi,

Sela

2023

IJSSR

View full text Add to dashboard Cite

Fish is a common protein source and easy to obtain in Indonesia, but because of its high water content, fish quickly spoils. Fish freshness can be detected using several conventional methods, such as chemical analysis, biochemistry, microbiological analysis, and sensory examination. Another identification method involves observing the color of the fish's eyes. Fish identification is crucial before any further processing, ensuring that the fish's quality delivered to consumers remains high. To tackle the problem of differentiating between fresh and non-fresh fish, this research employs Self-Organizing Maps (SOM) as the primary methodology. This research focuses on identifying fresh and non-fresh fish using the SOM method, using actual data involving tilapia as the research object. The data includes eye images of new and non-fresh fish, and various procedures are required to obtain the desired data. This data is then used as training and testing data. The process continues with the preprocessing stage, which is a data modification process to improve performance in subsequent steps and feature extraction using HSV color histograms. Classification of processed data is carried out using the SOM method. Once completed, the identification results are displayed. This research produces a system for identifying fresh and non-fresh fish based on eye images using SOM, which achieves a good accuracy of 85.71%.

show abstract