LEADER 04228nam 22007095 450 001 9910373937903321 005 20200705040144.0 010 $a981-15-2237-5 024 7 $a10.1007/978-981-15-2237-6 035 $a(CKB)4940000000158708 035 $a(MiAaPQ)EBC6005441 035 $a(DE-He213)978-981-15-2237-6 035 $a(PPN)242846246 035 $a(EXLCZ)994940000000158708 100 $a20200102d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning in Aquaculture$b[electronic resource] $eHunger Classification of Lates calcarifer /$fby Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian-Antonio Susto, Yukinori Mukai 205 $a1st ed. 2020. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2020. 215 $a1 online resource (64 pages) 225 1 $aSpringerBriefs in Applied Sciences and Technology,$x2191-530X 311 $a981-15-2236-7 327 $a1 Introduction -- 2 Monitoring and feeding integration of demand feeder systems -- 3 Image processing features extraction on fish behaviour -- 4 Time-series identification of fish feeding behaviour. 330 $aThis book highlights the fundamental association between aquaculture and engineering in classifying fish hunger behaviour by means of machine learning techniques. Understanding the underlying factors that affect fish growth is essential, since they have implications for higher productivity in fish farms. Computer vision and machine learning techniques make it possible to quantify the subjective perception of hunger behaviour and so allow food to be provided as necessary. The book analyses the conceptual framework of motion tracking, feeding schedule and prediction classifiers in order to classify the hunger state, and proposes a system comprising an automated feeder system, image-processing module, as well as machine learning classifiers. Furthermore, the system substitutes conventional, complex modelling techniques with a robust, artificial intelligence approach. The findings presented are of interest to researchers, fish farmers, and aquaculture technologist wanting to gain insights into the productivity of fish and fish behaviour. 410 0$aSpringerBriefs in Applied Sciences and Technology,$x2191-530X 606 $aWildlife 606 $aFish 606 $aComputational intelligence 606 $aComputer simulation 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aFish & Wildlife Biology & Management$3https://scigraph.springernature.com/ontologies/product-market-codes/L25080 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 615 0$aWildlife. 615 0$aFish. 615 0$aComputational intelligence. 615 0$aComputer simulation. 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 14$aFish & Wildlife Biology & Management. 615 24$aComputational Intelligence. 615 24$aSimulation and Modeling. 615 24$aSignal, Image and Speech Processing. 676 $a006.31 700 $aMohd Razman$b Mohd Azraai$4aut$4http://id.loc.gov/vocabulary/relators/aut$0871679 702 $aP. P. Abdul Majeed$b Anwar$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aMuazu Musa$b Rabiu$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aTaha$b Zahari$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aSusto$b Gian-Antonio$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aMukai$b Yukinori$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910373937903321 996 $aMachine Learning in Aquaculture$91965987 997 $aUNINA