Pubblicazione scientifica

Automated Fingerlings Counting Using Convolutional Neural Network

FILONE TECNOLOGICO	TEMA
trasformazione e commercializzazione

RIASSUNTO

The aim of this paper is to present automated fish fingerlings counting using image processing technique and to investigate the effectiveness of Convolutional Neural Network (CNN) in fish detection and counting accuracy. The proposed technique was tested in four different sizes of tilapia fingerlings – size 14, 17, 22 and 32. Threshold value was set to increase the level of efficiency of fish detection and counting accuracy. Experimental method and capturing images of fingerlings were conducted in the Bureau of Fisheries and Aquatic Resources (BFAR) Tilapia Hatchery and Fish Health Laboratory in Barcenaga, Naujan, Oriental Mindoro, Philippines. 2400 images of tilapia fingerlings were used in training and 1600 images in testing stage. The results of the experiment showed that CNN can be used in hatchery production and obtained a high level of fish detection and counting accuracy.

DATA

Data di pubblicazione: 01/02/2019

AUTORI

SHERYL MAY D LAINEZ
DENNIS B GONZALES

ENTE DI AFFERENZA

FACULTY COLLEGE OF COMPUTER STUDIES MINSCAT – BONGABONG CAMPUS LABASAN BONGABONG ORIENTAL MINDORO PHILIPPINES STUDENT SCHOOL OF GRADUATE STUDIES AMA UNIVERSITY
PROFESSOR SCHOOL OF GRADUATE STUDIES AMA UNIV & UNIV EAST QUEZON CITY PHILIPPINES

RIVISTA

2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS) (Page(s): 67-72)

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