Age reading of sardine scale using neural network

Research Project

Project/Area Number	03556027
Research Category	Grant-in-Aid for Developmental Scientific Research (B)
Allocation Type	Single-year Grants
Research Field	General fisheries
Research Institution	Ocean Research Institute, University of Tokyo
Principal Investigator	AOKI Ichiro ORI, Univ.of Tokyo, Associate Professor, 海洋研究所, 助教授 (40114350)
Co-Investigator(Kenkyū-buntansha)	INAGAKI Tadashi ORI, Univ.of Tokyo, Technical Official, 海洋研究所, 教務職員 (00151572) KOMATSU Teruhisa ORI, Univ.of Tokyo, Research Associate, 海洋研究所, 助手 (60215390) ISHII Takeo ORI, Univ.of Tokyo, Professor, 海洋研究所, 教授 (80013564)
Project Period (FY)	1991 – 1993
Project Status	Completed (Fiscal Year 1993)
Budget Amount *help	¥6,900,000 (Direct Cost: ¥6,900,000) Fiscal Year 1993: ¥1,100,000 (Direct Cost: ¥1,100,000) Fiscal Year 1992: ¥1,400,000 (Direct Cost: ¥1,400,000) Fiscal Year 1991: ¥4,400,000 (Direct Cost: ¥4,400,000)
Keywords	Neural Network / Image Processing / Pattern Recognition / Sardine / Scale / Age Determination / ニュ-ラルネット / パタ-ン認識
Research Abstract	1. A personal-computer-based system was developed for counting annual circuli in scales of Japanese sardine using artificial neural network. The image processing was used to digitize the video image of a scale and store the light intensity from five radial transects. The values and positions of the peaks of the intensity along the transect were determined and normalized to input to the neural network as 5^<>40 mesh two-dimensional data. 2. Performance of age determination of the system was examined using a data set from 20 samples of sardine scales. In the learning, the neural network showed learning ability to determine age from training samples without error. However, the performance of the trained neural network for test samples got no further than 50% of correct recognition rate, while error rate was low. 3. The followings remain to be examined : (1) much more learning is needed as the number of learning data set were too small, (2) there is room for improvement of illumination to get better image with clear annual circuli, (3) since radial grooves, which have higher contrast than annual circuli in the scale image prevent counting annual circuli correctly, some pre-processing procedure of the image is required to eliminate the noisy grooves, and (4) input data to the neural network should be examined further for larger number of measureing transect and/or larger mesh size than 5^<>40, which increase resolution power on the one side and the number of units in the neural network on the other hand, i.e. much more calculating time.