語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Machine Learning in Aquaculture = Hu...
~
Muazu Musa, Rabiu.
Machine Learning in Aquaculture = Hunger Classification of Lates calcarifer /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Machine Learning in Aquaculture/ by Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian-Antonio Susto, Yukinori Mukai.
其他題名:
Hunger Classification of Lates calcarifer /
作者:
Mohd Razman, Mohd Azraai.
其他作者:
P. P. Abdul Majeed, Anwar.
面頁冊數:
VI, 60 p.online resource. :
Contained By:
Springer Nature eBook
標題:
Wildlife. -
電子資源:
https://doi.org/10.1007/978-981-15-2237-6
ISBN:
9789811522376
Machine Learning in Aquaculture = Hunger Classification of Lates calcarifer /
Mohd Razman, Mohd Azraai.
Machine Learning in Aquaculture
Hunger Classification of Lates calcarifer /[electronic resource] :by Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian-Antonio Susto, Yukinori Mukai. - 1st ed. 2020. - VI, 60 p.online resource. - SpringerBriefs in Applied Sciences and Technology,2191-530X. - SpringerBriefs in Applied Sciences and Technology,.
1 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.
This 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.
ISBN: 9789811522376
Standard No.: 10.1007/978-981-15-2237-6doiSubjects--Topical Terms:
1254961
Wildlife.
LC Class. No.: QL81.5-84.7
Dewey Class. No.: 597
Machine Learning in Aquaculture = Hunger Classification of Lates calcarifer /
LDR
:02777nam a22004215i 4500
001
1030486
003
DE-He213
005
20200705040144.0
007
cr nn 008mamaa
008
210318s2020 si | s |||| 0|eng d
020
$a
9789811522376
$9
978-981-15-2237-6
024
7
$a
10.1007/978-981-15-2237-6
$2
doi
035
$a
978-981-15-2237-6
050
4
$a
QL81.5-84.7
050
4
$a
QL614-639.8
072
7
$a
RNKH
$2
bicssc
072
7
$a
SCI070010
$2
bisacsh
072
7
$a
RNKH
$2
thema
082
0 4
$a
597
$2
23
082
0 4
$a
590
$2
23
100
1
$a
Mohd Razman, Mohd Azraai.
$e
editor.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1313712
245
1 0
$a
Machine Learning in Aquaculture
$h
[electronic resource] :
$b
Hunger Classification of Lates calcarifer /
$c
by Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian-Antonio Susto, Yukinori Mukai.
250
$a
1st ed. 2020.
264
1
$a
Singapore :
$b
Springer Singapore :
$b
Imprint: Springer,
$c
2020.
300
$a
VI, 60 p.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
SpringerBriefs in Applied Sciences and Technology,
$x
2191-530X
505
0
$a
1 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.
520
$a
This 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.
650
0
$a
Wildlife.
$3
1254961
650
0
$a
Fish.
$3
1254962
650
0
$a
Computational intelligence.
$3
568984
650
0
$a
Computer simulation.
$3
560190
650
0
$a
Signal processing.
$3
561459
650
0
$a
Image processing.
$3
557495
650
0
$a
Speech processing systems.
$3
564428
650
1 4
$a
Fish & Wildlife Biology & Management.
$3
680241
650
2 4
$a
Computational Intelligence.
$3
768837
650
2 4
$a
Simulation and Modeling.
$3
669249
650
2 4
$a
Signal, Image and Speech Processing.
$3
670837
700
1
$a
P. P. Abdul Majeed, Anwar.
$e
editor.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1313323
700
1
$a
Muazu Musa, Rabiu.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1310400
700
1
$a
Taha, Zahari.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1283527
700
1
$a
Susto, Gian-Antonio.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1327408
700
1
$a
Mukai, Yukinori.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1327409
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9789811522369
776
0 8
$i
Printed edition:
$z
9789811522383
830
0
$a
SpringerBriefs in Applied Sciences and Technology,
$x
2191-530X
$3
1253575
856
4 0
$u
https://doi.org/10.1007/978-981-15-2237-6
912
$a
ZDB-2-SBL
912
$a
ZDB-2-SXB
950
$a
Biomedical and Life Sciences (SpringerNature-11642)
950
$a
Biomedical and Life Sciences (R0) (SpringerNature-43708)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入