Detection of broiler heat stress by using the generalised sequential pattern algorithm

Branco, Tatiane; Moura, Daniella Jorge de; Nääs, Irenilza de Alencar; Oliveira, Stanley Robson de Medeiros

doi:10.1016/j.biosystemseng.2019.10.012

Cited by 17 publications

(14 citation statements)

References 19 publications

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“…The GSP algorithm was designed for mining sparse and generalized sequential patterns that are repeated over time. A description of the GSP algorithm's operation can be found in Bureva et al [33] and Branco et al [8].…”

Section: Discussionmentioning

confidence: 99%

“…These behaviors were observed at both ages and temperatures assessed, ensuring a natural broiler pattern. Some behaviors were not seen because they had less than 40% support, among them 'Foraging' and 'Running' [8].…”

Section: The Gsp Algorithm's Approachmentioning

confidence: 99%

“…Research that improves the environment's quality for raising broilers is essential [3,4]. Previous studies show that monitoring the behavior of broilers might provide real-time information on thermal comfort in response to exposure to different thermal environments [1,[5][6][7][8], since the animal is used as a biosensor [9,10], becoming a valuable noninvasive tool for environmental analysis [11]. Behavioral changes can occur quickly and at a lower metabolic cost than physiological adjustments [12].…”

Section: Introductionmentioning

confidence: 99%

“…The method was first presented by Agrawal and Srikant [21] and later applied to analyze tourist behavior [22], the learning behavior of university students after exposure to educational games [23], and the behavior of book lending transactions [24]. The mining of sequential patterns in poultry has been used to assess growing chicks' behavior under heat and cold stress [8]. However, there is no evidence of any current literature study focusing on broilers' sequential behavior close to slaughter.…”

Section: Introductionmentioning

confidence: 99%

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The Sequential Behavior Pattern Analysis of Broiler Chickens Exposed to Heat Stress

Branco

Moura

Nääs³

et al. 2021

AgriEngineering

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Broiler productivity is dependent on a range of variables; among them, the rearing environment is a significant factor for proper well-being and productivity. Behavior indicates the bird’s initial response to an adverse environment and is capable of providing an indicator of well-being in real-time. The present study aims to identify and characterize the sequential pattern of broilers’ behavior when exposed to thermoneutral conditions (TNZ) and thermal stress (HS) by constant heat. The research was carried out in a climatic chamber with 18 broilers under thermoneutral conditions and heat stress for three consecutive days (at three different ages). The behavior database was first analyzed using one-way ANOVA, Tukey test by age, and Boxplot graphs, and then the sequence of the behaviors was evaluated using the generalized sequential pattern (GSP) algorithm. We were able to predict behavioral patterns at the different temperatures assessed from the behavioral sequences. Birds in HS were prostrate, identified by the shorter behavioral sequence, such as the {Lying down, Eating} pattern, unlike TNZ ({Lying down, Walking, Drinking, Walking, Lying down}), which indicates a tendency to increase behaviors (feeding and locomotor activities) that guarantee the better welfare of the birds. The sequence of behaviors ‘Lying down’ followed by ‘Lying laterally’ occurred only in HS, which represents a stressful thermal environment for the bird. Using the pattern mining sequences approach, we were able to identify temporal relationships between thermal stress and broiler behavior, confirming the need for further studies on the use of temporal behavior sequences in environmental controllers.

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Section: Discussionmentioning

confidence: 99%

Section: The Gsp Algorithm's Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Sequential Behavior Pattern Analysis of Broiler Chickens Exposed to Heat Stress

Branco

Moura

Nääs³

et al. 2021

AgriEngineering

Self Cite

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“…Sequential pattern mining generates a frequent sequence with the minimum support at least [15]. Sequential pattern mining extracts a sequential pattern with meaningful knowledge and applies it to sequential analysis, classification & prediction, and recommendation system [16].…”

Section: A Sequential Pattern Mining For Generating Frequent Sequencementioning

confidence: 99%

PrefixSpan Based Pattern Mining Using Time Sliding Weight From Streaming Data

2020

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This study proposes the prefixSpan based pattern mining using time sliding weight from streaming data. To discover sequential patterns, it applies a time sliding weight to create a structure of projected DB Tree. For the time sliding weight, a time window is applied to the sequential data to calculate the label and support of the window. When a projected DB Tree is designed, the time weight calculated for each pattern is inserted in a table. At this time, the tree is updated by deleting the node whose time weight is less than the reference value. For this reason, whenever data is updated, the tree is sorted again. The reordering process removes the pattern of less influence by applying time weights. Therefore, it is possible to construct a projected DB Tree that can extract influential patterns. The performance of the proposed method is evaluated in three aspects. Firstly, the conventional PrefixSpan algorithm is compared with the proposed PrefixSpan algorithm based on time sliding weight in terms of the pattern generation time according to size of data. Secondly, the fitness of the proposed algorithm is evaluated through cross-validation. Thirdly, GSP, SPADE, and prefixSpan algorithms are compared with the proposed algorithm through F-measure. As a result of the evaluation, the proposed algorithm improves accuracy 75% more than PrefixSpan algorithm and the sequential pattern algorithms of GSP and SPADE. Regarding the comparison of F-measure based on precision and recall, the proposed one improves its performance about 83%.

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Detection and tracking of chickens in low-light images using YOLO network and Kalman filter

Siriani

Kodaira

Mehdizadeh³

et al. 2022

Neural Comput & Applic

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Detection of broiler heat stress by using the generalised sequential pattern algorithm

Cited by 17 publications

References 19 publications

The Sequential Behavior Pattern Analysis of Broiler Chickens Exposed to Heat Stress

The Sequential Behavior Pattern Analysis of Broiler Chickens Exposed to Heat Stress

PrefixSpan Based Pattern Mining Using Time Sliding Weight From Streaming Data

Detection and tracking of chickens in low-light images using YOLO network and Kalman filter

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