Machine Learning in Aquaculture: Hunger Classification of Lates calcarifer - Couverture souple
Livre 77 sur 104: SpringerBriefs in Continuum Mechanics
Synopsis
1. Introduction
1.1 Fish hunger behaviour1.2 Image processing parameters
1.3 Machine learning in fish behaviour
2.0 Monitoring and feeding integration of demand feeder systems
2.1 Overview
2.2 Fish monitoring setup
2.3 Fish Growth
2.4 Results and discussion
3. Image processing features extraction on fish behaviour
3.1 Overview3.2 Data pre-processing
3.3 Clustering
3.4 Feature extraction and classification
3.5 Results and discussion
4. Time-series identification of fish feeding behaviour
4.1 Overview
4.2 Event identification
4.3 PCA based feature selection
4.4 Classification accuracy
4.5 Results and discussion
5. Hyperparameter tuning for hunger state classification
5.1 Overview5.2 Optimization of Bayesian
5.3 Classification accuracy
5.4 Result and discussion
6.0 Concluding remarks
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
