Time-series identification on fish feeding behaviour - Razman, Mohd Azraan; Abdul Majeed, Anwar P.P.; Musa, Rabiu Muazu; Taha, Zahari; Susto, Gian Antonio; Mukai, Yukinori (Springer, 2020)
Abstract The 1 identification of relevant parameters that could describe the state of AQ1 2 fish hunger is vital for ensuring the appropriate allocation of food to the fish. The 3 establishment of these relevant parameters is non-trivial, particularly when develop4 ing an automated demand feeder system. The present inquiry is being undertaken 5 to determine the hunger state of Lates calcarifer. For data collection, a video anal6 ysis system is used, and the video was taken all day, where the fish was fed by 7 an automatic feeding system. Sixteen characteristics of the raw data set have been 8 extracted through feature engineering for 0.5 min, 1.0 min, 1.5 min and 2.0 min, 9 respectively, in accordance with the mean, peak, minimum and variability of each of 10 the different time window scales. Furthermore, the features extracted have been eval11 uated through principal component analysis (PCA) both for dimension reduction and 12 PCA with varimax rotation. The details were then categorized using support vector 13 machine (SVM), K-NN and random forest tree (RF) classifiers. The best identifica14 tion accuracy was shown with eight described features in the varimax-based PCA. 15 The forecast results based on the K-NN model built on selected data characteristics 16 showed a level of 96.5% indicating that the characteristics analysed were crucial to 17 classifying the actions of hunger among fisheries.