Machine Learning in Aquaculture
Machine Learning in Aquaculture

Author: Mohd Azraai Mohd Razman

Publisher: Springer Nature

Published: 2020-01-02

Total Pages: 64

ISBN-13: 9811522375

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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.

Machine Learning in Aquaculture
Machine Learning in Aquaculture

Author: Mohd Azraai Mohd Razman

Publisher:

Published: 2020

Total Pages: 64

ISBN-13: 9789811522383

DOWNLOAD EBOOK

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.

Biosensors in Agriculture: Recent Trends and Future Perspectives
Biosensors in Agriculture: Recent Trends and Future Perspectives

Author: Ramesh Namdeo Pudake

Publisher: Springer Nature

Published: 2021-03-12

Total Pages: 496

ISBN-13: 3030661652

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This book reviews the application of nanosensors in food and agriculture. Nanotechnology has the potential to become transformative technology that will impact almost all sectors. Tools like nanosensors, which detect specific molecular interactions, can be used for on-site, in-situ and online measurements of various parameters in clinical diagnostics, environmental and food monitoring, and quality control. Due to their unprecedented performance and sensitivity, nanobiosensors are gaining importance in precision farming. The book examines the use of nanobiosensors in the monitoring of food additives, toxins and mycotoxins, microbial contamination, food allergens, nutritional constituents, pesticides, environmental parameters, plant diseases and genetically modified organisms. It also discusses the role of biosensors in increasing crop productivity in sustainable agriculture, and nanosensor-based smart delivery systems to optimize the use of natural resources such as water, nutrients and agrochemicals in precision farming.

Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data
Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data

Author: Robert B. Fisher

Publisher: Springer

Published: 2016-04-04

Total Pages: 0

ISBN-13: 9783319302065

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This book gives a start-to-finish overview of the whole Fish4Knowledge project, in 18 short chapters, each describing one aspect of the project. The Fish4Knowledge project explored the possibilities of big video data, in this case from undersea video. Recording and analyzing 90 thousand hours of video from ten camera locations, the project gives a 3 year view of fish abundance in several tropical coral reefs off the coast of Taiwan. The research system built a remote recording network, over 100 Tb of storage, supercomputer processing, video target detection and tracking, fish species recognition and analysis, a large SQL database to record the results and an efficient retrieval mechanism. Novel user interface mechanisms were developed to provide easy access for marine ecologists, who wanted to explore the dataset. The book is a useful resource for system builders, as it gives an overview of the many new methods that were created to build the Fish4Knowledge system in a manner that also allows readers to see how all the components fit together.

Smart Sensors for Real-Time Water Quality Monitoring
Smart Sensors for Real-Time Water Quality Monitoring

Author: Subhas C Mukhopadhyay

Publisher: Springer Science & Business Media

Published: 2013-03-17

Total Pages: 292

ISBN-13: 3642370063

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Sensors are being utilized to increasing degrees in all forms of industry. Researchers and industrial practitioners in all fields seek to obtain a better understanding of appropriate processes so as to improve quality of service and efficiency. The quality of water is no exception, and the water industry is faced with a wide array of water quality issues being present world-wide. Thus, the need for sensors to tackle this diverse subject is paramount. The aim of this book is to combine, for the first time, international expertise in the area of water quality monitoring using smart sensors and systems in order that a better understanding of the challenges faced and solutions posed may be available to all in a single text.

Neural Information Processing
Neural Information Processing

Author: Tingwen Huang

Publisher: Springer

Published: 2012-11-05

Total Pages: 740

ISBN-13: 3642344879

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The five volume set LNCS 7663, LNCS 7664, LNCS 7665, LNCS 7666 and LNCS 7667 constitutes the proceedings of the 19th International Conference on Neural Information Processing, ICONIP 2012, held in Doha, Qatar, in November 2012. The 423 regular session papers presented were carefully reviewed and selected from numerous submissions. These papers cover all major topics of theoretical research, empirical study and applications of neural information processing research. The 5 volumes represent 5 topical sections containing articles on theoretical analysis, neural modeling, algorithms, applications, as well as simulation and synthesis.

e-Learning, e-Education, and Online Training
e-Learning, e-Education, and Online Training

Author: Shuai Liu

Publisher: Springer Nature

Published: 2020-12-12

Total Pages: 363

ISBN-13: 303063955X

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This 2-volume set constitutes the proceedings of the 6th International Conference on e-Learning, e-Education, and Online Training, eLEOT 2020, held in Changsha, China, in June 2020. The conference was held virtually due to the COVID-19 pandemic. The 68 full papers presented were carefully reviewed and selected from 141 submissions. They focus on most recent and innovative trends and new technologies in for educational modernization, such as artificial intelligence and big data. The theme of eLEOT 2020 was “Education with New Generation Information Technology”.

Deep Technology for Sustainable Fisheries and Aquaculture
Deep Technology for Sustainable Fisheries and Aquaculture

Author: Amaj Rahimi-Midani

Publisher: Springer Nature

Published: 2023-08-12

Total Pages: 315

ISBN-13: 9819949173

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This book uses real-world examples from the aquaculture industry to demonstrate how deep technology is assisting farmers and vulnerable communities. Works conducted by Poseidon-AI (a deep tech company involved in the aquaculture sector) in different countries are presented as case studies to show the positive impacts of deep tech involvement in the aquaculture sector. Primary industries, such as fisheries and aquaculture, rely heavily on labor. Furthermore, the manual practices of these farming methods increase material waste and reduce yields, resulting in higher costs and lower revenues. Poikilotherms make up the majority of aquatic animals, and environmental changes have a significant impact on them. This means that, due to climate change, farming of these animals cannot continue in the same way that it has for centuries. Artificial intelligence, machine learning, image processing, sensing, and automation are approaches that can assist these farms in dealing with rapid environmental changes while also assisting farmers in growing their businesses sustainably. This book is of interest to climate change scientists, entrepreneurs, investors, civil workers, and policymakers. Furthermore, the book is a great complimentary material for graduate students of fisheries, aquaculture, ecology, soil science, water management and environmental sciences. All national and international policymakers working in implementation of UNSDGs and sustainability, will find this book a useful read.

Ensemble Machine Learning
Ensemble Machine Learning

Author: Cha Zhang

Publisher: Springer Science & Business Media

Published: 2012-02-17

Total Pages: 332

ISBN-13: 1441993258

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It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.

Deep Learning for Marine Science
Deep Learning for Marine Science

Author: Haiyong Zheng

Publisher: Frontiers Media SA

Published: 2024-05-15

Total Pages: 555

ISBN-13: 2832549055

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Deep learning (DL), mainly composed of deep and complex neural networks such as recurrent network and convolutional network, is an emerging research branch in the field of artificial intelligence and machine learning. DL revolution has a far-reaching impact on all scientific disciplines and every corner of our lives. With continuing technological advances, marine science is entering into the big data era with the exponential growth of information. DL is an effective means of harnessing the power of big data. Combined with unprecedented data from cameras, acoustic recorders, satellite remote sensing, and large model outputs, DL enables scientists to solve complex problems in biology, ecosystems, climate, energy, as well as physical and chemical interactions. Although DL has made great strides, it is still only beginning to emerge in many fields of marine science, especially towards representative applications and best practices for the automatic analysis of marine organisms and marine environments. DL in nowadays' marine science mainly leverages cutting-edge techniques of deep neural networks and massive data which collected by in-situ optical or acoustic imaging sensors for underwater applications, such as plankton classification and coral reef detection. This research topic aims to expand the applications of marine science to cover all aspects of detection, classification, segmentation, localization, and density estimation of marine objects, organisms, and phenomena.