Tuna classification using super learner ensemble of region-based CNN-grouped 2D-LBP models

作     者：Jisha Anu Jose C.Sathish Kumar S.Sureshkumar 

作者机构：Rajiv Gandhi Institute of TechnologyKottayamIndia Government Engineering CollegeIdukkiIndia Kerala University of Fisheries and Ocean StudiesKochiIndia 

出 版 物：《Information Processing in Agriculture》 (农业信息处理（英文）)

年 卷 期：2022年第9卷第1期

页      面：68-79页

核心收录：

学科分类：0907[农学-林学] 0908[农学-水产] 08[工学] 0710[理学-生物学] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 081104[工学-模式识别与智能系统] 0905[农学-畜牧学] 0707[理学-海洋科学] 0906[农学-兽医学] 0829[工学-林业工程] 0901[农学-作物学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：South Indian Federation of Fishermen Societies 

主　　题：Tuna classification Convolutional neural network Grouped 2D-local binary pattern Super learner ensemble 

摘      要：Tuna is superior among the marine fishes that are exported in the forms of raw fish and processed food.Separation of Tuna into their species is done in industries manually,and the process is tiresome.This work proposes an automated system for classifying Tuna spe-cies based on their images.An ensemble of region-based deep neural networks is used.A sub region contrast stretching operation is applied to enhance the images.Each fish image is then divided into three regions and is augmented before giving as input to pre-trained convolutional neural networks(CNN).After fine-tuning the models,the output from the last convolutional layer is given to a grouped 2D-local binary pattern descriptor(G2DLBP).Statistical features from the descriptor are applied to different classifiers,and the best clas-sifier for each image region model is identified.Different ensemble methods are subse-quently used to combine the three CNN-G2DLBP models.Among the ensemble techniques,super learner ensemble method with random forest(RF)classifier using 5-fold cross-validation shows the highest classification accuracy of 97.32%.The perfor-mance of different ensemble methods is analyzed in terms of accuracy,precision,recall,and f-score.The proposed system shows an accuracy of 93.91% when evaluated with an independent test dataset.An ensemble of region-based CNN with textural features from G2DLBP is applied for the first time for fish classification.