Fast detection of pathogens in salmon farming industry

López-Cortés, Xaviera A.; Nachtigall, Fabiane M.; Olate, Verónica Rachel; Araya, Macarena; Oyanedel, Sandra; Díaz, Verónica; Jakob, Eva; Ríos-Momberg, Mauricio; Santos, Leonardo S.

doi:10.1016/j.aquaculture.2016.12.008

Cited by 40 publications

(14 citation statements)

References 41 publications

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“…Among other definitions, ML is defined as the scientific field that gives machines the ability to learn without being strictly programmed [ 1 ]. Year by year, ML applies in more and more scientific fields including, for example, bioinformatics [ 2 , 3 ], biochemistry [ 4 , 5 ], medicine [ 6 , 7 , 8 ], meteorology [ 9 , 10 , 11 ], economic sciences [ 12 , 13 , 14 ], robotics [ 15 , 16 ], aquaculture [ 17 , 18 ], and food security [ 19 , 20 ], and climatology [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Agriculture: A Review

Λιάκος

Busato

Moshou

et al. 2018

Sensors

1,870

827

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Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.

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Section: Introductionmentioning

confidence: 99%

Machine Learning in Agriculture: A Review

Λιάκος

Busato

Moshou

et al. 2018

Sensors

1,870

827

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show abstract

“…Moreover, animal welfare is a crucial factor to take into account in culture protocols, and new technological advances must be at the service of this purpose. In line with this, there already exist examples of the use of machine learning in aquaculture [26,27]. Under this framework, we have applied an innovative protocol, particularly adapted to this species, based on image recognition and machine learning (artificial neural networks) for biomass prediction.…”

Section: Discussionmentioning

confidence: 93%

Artificial Neural Network (ANN) as a Tool to Reduce Human-Animal Interaction Improves Senegalese Sole Production

et al. 2019

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Manipulation is usually required for biomass calculation and food estimation for optimal fish growth in production facilities. However, the advances in computer-based systems have opened a new range of applied possibilities. In this study we used image analysis and a neural network algorithm that allowed us to successfully provide highly accurate biomass data. This developed system allowed us to compare the effects of reduced levels of human-animal interaction on the culture of adult Senegalese sole (Solea senegalensis) in terms of body weight gain. For this purpose, 30 adult fish were split into two homogeneous groups formed by three replicates (n = 5) each: a control group (CTRL), which was standard manipulated and an experimental group (EXP), which was maintained under a lower human-animal interaction culture using our system for biomass calculation. Visible implant elastomer was, for the first time, applied as tagging technology for tracking soles during the experiment (four months). The experimental group achieved a statistically significant weight gain (p < 0.0100) while CTRL animals did not report a statistical before-after weight increase. Individual body weight increment was lower (p < 0.0100) in standard-handled animals. In conclusion, our experimental approach provides evidence that our developed system for biomass calculation, which implies lower human-animal interaction, improves biomass gain in Senegalese sole individuals in a short period of time.

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“…[8]. Machine learning (ML) encompasses more and more research areas per year, including, for example, bioinformatics [9], [10], biochemistry [11], [12], meteorology of medicine [13]- [16], economics [17]- [19], aquaculture [20], chemo-ecology [21], robotics [22]- [25], and climatology [26], [27].…”

Section: Machine Learningmentioning

confidence: 99%

A Review of Principal Component Analysis Algorithm for Dimensionality Reduction

Hasan

Abdulazeez

2021

JSCDM

135

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Big databases are increasingly widespread and are therefore hard to understand, in exploratory biomedicine science, big data in health research is highly exciting because data-based analyses can travel quicker than hypothesis-based research. Principal Component Analysis (PCA) is a method to reduce the dimensionality of certain datasets. Improves interpretability but without losing much information. It achieves this by creating new covariates that are not related to each other. Finding those new variables, or what we call the main components, will reduce the eigenvalue /eigenvectors solution problem. (PCA) can be said to be an adaptive data analysis technology because technology variables are developed to adapt to different data types and structures. This review will start by introducing the basic ideas of (PCA), describe some concepts related to (PCA), and discussing. What it can do, and reviewed fifteen articles of (PCA) that have been introduced and published in the last three years.

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Fast detection of pathogens in salmon farming industry

Cited by 40 publications

References 41 publications

Machine Learning in Agriculture: A Review

Machine Learning in Agriculture: A Review

Artificial Neural Network (ANN) as a Tool to Reduce Human-Animal Interaction Improves Senegalese Sole Production

A Review of Principal Component Analysis Algorithm for Dimensionality Reduction

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