The identification and filtering of fertilized eggs with a thermal imaging system

Lin, Chern‐Sheng; Yeh, Po-Ting; Chen, Der‐Chin; Chiou, Yih-Chih; Lee, Chi−Hung

doi:10.1016/j.compag.2012.12.004

Cited by 36 publications

(19 citation statements)

References 16 publications

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“…The Mamdani inference method (Mandani, 1976), adopted by several authors (Ponciano et al, 2012;Lin et al, 2013;Múnera Bedoya et al, 2015;Schiassi et al, 2015), was used for the development of the fuzzy model, and offers as a response, a fuzzy set arising from the combination of input values with their respective pertinence degrees through a minimum operator followed by rules overlapping through a maximum operator (Leite et al, 2010). The defined input variables were the enthalpies (H) in the birds' life phases defined in Table 1 and represented by trapezoidal pertinence curves ( Figure 5), which were chosen to better reproduce the data set (Schiassi et al, 2015).…”

Section: Development and Validation Of The Fuzzy Modelmentioning

confidence: 99%

Productive Responses From Broiler Chickens Raised in Different Commercial Production Systems - Part I: Fuzzy Modeling

et al. 2019

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Broiler chickens are classified as homoeothermic animals and require a production environment within well-defined thermal comfort intervals. Therefore, the development of algorithms (mathematical models) to control the environment that can be embedded in microcontrollers becomes necessary. Hence, this work aimed to develop a fuzzy model for predicting the productive performance of broiler chickens as a function of the thermal environment during the various breeding phases. The Mamdani inference and defuzzification methods were used, by means of the gravity center, to develop the fuzzy model. Two hundred and forty-three rules with weighting factors of 1.0 each were elaborated. Three commercial warehouses (conventional system, wind tunnel with negative pressure and dark house) were evaluated for testing of the model. We recorded the thermal environment (dry bulb temperature-tdb and relative humidity-RH) and productivity data (feed intake-FI, weight gain-WG, feed conversion-FC and productive efficiency index-PEI) over six lots in each aviary. The resulting fuzzy model was capable of forecasting FI, WG, FC, and PEI, with standard deviations and mean percentage errors of 4.16 g and 5.05%, 146.53 g and 8.04%, 0.06 g g-1 and 4.96%, and 24.51 g and 12.29%, respectively.

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Section: Development and Validation Of The Fuzzy Modelmentioning

confidence: 99%

Productive Responses From Broiler Chickens Raised in Different Commercial Production Systems - Part I: Fuzzy Modeling

et al. 2019

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“…Mamdani's inference method (Ponciano et al, 2012;Lin et al, 2013;Julio et al, 2015;Schiassi et al, 2015) and the defuzzification method using the center of gravity (Leite et al, 2010) were used. Two hundred and forty-three rules with weighting factors of 1.0 were elaborated (Yanagi Junior et al, 2012;Ponciano et al, 2012;Schiassi et al, 2013;Schiassi et al, 2014).…”

Section: The Fuzzy Modelmentioning

confidence: 99%

Productive Responses From Broiler Chickens Raised in Different Commercial Production System - Part Ii: Impact of Climate Change

Lourençoni

Yanagi

et al. 2019

Eng. Agríc.

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Broiler chickens are homoeothermic animals, i.e., animals capable of maintaining their body temperature within quite narrow limits; therefore, climate change poses a great challenge to poultry. With this in mind, this research aims to evaluate the performance of broilers submitted to different commercial production systems and exposed to different future scenarios, taking into account the climate change trends. To achieve this objective, we developed and validated a fuzzy model able to predict the performance of a broiler as a function of enthalpy along its life stages. This model was developed and validated in part I of this article based on experimental data collected for one year in three aviaries: conventional, negative pressure, and dark house systems. A Mann-Kendall nonparametric test and linear regression analysis were applied to the enthalpy values, which were calculated as a function of the ambient air temperature and relative humidity in order to study the climate change trends. Later, simulations were performed using the fuzzy model for 2025, 2050, 2075, and 2100 future scenarios. Specific improvements were observed when the heating trends coincided with the initial stages of breeding; however, in general, the productive responses of broilers in the different evaluated systems worsened with the climate change trends. Faced with the climate change trends, the responses improved in the order dark house aviary > negative pressure aviary> conventional aviary.

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“…They concluded that the embryo development can be detected using light transmission from 108 hours (day 4.5) of incubation. Lin et al 21 proposed a thermal imaging system for the identification and filtering of fertile eggs. Based on the thermal information and a fuzzy system, they could obtain a proper value for a threshold which was used to judge as to whether a given egg is fertile or not.…”

Section: Related Workmentioning

confidence: 99%

“…Some researchers have tried to determine the fertility of eggs by employing equipment such as MRI, infrared hyperspectral imaging, or thermal cameras 2,18,19,20,21 . However, using this expensive equipment would not be economical, especially in the case of mass production.…”

Section: Introductionmentioning

confidence: 99%

A Machine Vision System for Detecting Fertile Eggs in the Incubation Industry

Hashemzadeh¹,

Farajzadeh²

2016

IJCIS

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One of the important factors in increasing the productivity of the incubation industry is to be sure that the eggs placed in the incubators are fertile. In this research, a fertility detection machine vision system is developed and evaluated. To this end, a mechatronic machine is fabricated for acquiring accurate digital images of eggs without harming them. An appropriate and cheap light source is also introduced for illuminating the eggs, which potentially enables a CCD camera to obtain good quality and informative images from inner side of the eggs. Finally, a robust machine vision algorithm is developed to process the captured images and distinguish fertile eggs from infertile ones. In order to evaluate the system, a large egg image dataset is provided using 240 incubated eggs (including 190 fertile and 50 infertile eggs). The fertility detection accuracy of the system on the provided dataset reaches 47.13% at day 1 of incubation, 81.41% at day 2, 93.08% at day 3, 97.73% at day 4, and 98.25% at day 5. Comparisons with existing approaches show that the proposed method achieves a superior performance. The obtained results indicate that the proposed system is highly reliable and applicable in the incubation industry.

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The identification and filtering of fertilized eggs with a thermal imaging system

Cited by 36 publications

References 16 publications

Productive Responses From Broiler Chickens Raised in Different Commercial Production Systems - Part I: Fuzzy Modeling

Productive Responses From Broiler Chickens Raised in Different Commercial Production Systems - Part I: Fuzzy Modeling

Productive Responses From Broiler Chickens Raised in Different Commercial Production System - Part Ii: Impact of Climate Change

A Machine Vision System for Detecting Fertile Eggs in the Incubation Industry

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