A machine vision system for early detection and prediction of sick birds: A broiler chicken model

Okinda, Cedric; Lu, Mingzhou; Liu, Longshen; Nyalala, Innocent; Muneri, Caroline; Wang, Jintao; Zhang, Hailin; Shen, Mingxia

doi:10.1016/j.biosystemseng.2019.09.015

Cited by 96 publications

(75 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Computer vision has been widely used in various processes of different poultry production systems. It includes automation of the house management, behavior, and welfare [11,[29][30][31][32][33][34][35][36][37][38][39][40], disease detection [28,[41][42][43][44][45][46][47], weight measurement [27,[48][49], slaughtering process [50][51], carcass quality [52][53][54][55], and egg examination [56][57][58][59][60][61][62][63][64][65]. On the other hand, computer vision also popular on other livestock monitoring, such as pig [73][74][75][76][77][78][79][80], sheep or cattle …”

Section: Overview Of Computer Vision In Poultry Farmmentioning

confidence: 99%

A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

et al. 2021

View full text Add to dashboard Cite

Section: Overview Of Computer Vision In Poultry Farmmentioning

confidence: 99%

A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

et al. 2021

View full text Add to dashboard Cite

“…Also, for some applications, there is an improvement of image processing since, within 3D images, there is less noise due to light and background conditions and it is easier to use the distance to the camera as a threshold. Regarding the uses of depth sensors, there are many examples of successful applications including tracking of fish within a tank (79), identification of landmarks on animal shape with consecutive modeling of gait (72,99), body condition score (78), sickness detection (84), and the estimation of many other body measurements that will be discussed below.…”

Section: Evaluation Of Animal Surface and Related Traits Using 3d Cammentioning

confidence: 99%

“…In chickens, 3D sensors have also been used to identify small modifications in the animal surface that is related to head and tail positioning (84). In this study, in which animals were challenged with the Newcastle disease virus, it was possible to identify alterations in the animal shape and behavior 6 days after the inoculation.…”

Section: Evaluation Of Animal Surface and Related Traits Using 3d Cammentioning

confidence: 99%

Image Analysis and Computer Vision Applications in Animal Sciences: An Overview

2020

View full text Add to dashboard Cite

Computer Vision, Digital Image Processing, and Digital Image Analysis can be viewed as an amalgam of terms that very often are used to describe similar processes. Most of this confusion arises because these are interconnected fields that emerged with the development of digital image acquisition. Thus, there is a need to understand the connection between these fields, how a digital image is formed, and the differences regarding the many sensors available, each best suited for different applications. From the advent of the charge-coupled devices demarking the birth of digital imaging, the field has advanced quite fast. Sensors have evolved from grayscale to color with increasingly higher resolution and better performance. Also, many other sensors have appeared, such as infrared cameras, stereo imaging, time of flight sensors, satellite, and hyperspectral imaging. There are also images generated by other signals, such as sound (ultrasound scanners and sonars) and radiation (standard x-ray and computed tomography), which are widely used to produce medical images. In animal and veterinary sciences, these sensors have been used in many applications, mostly under experimental conditions and with just some applications yet developed on commercial farms. Such applications can range from the assessment of beef cuts composition to live animal identification, tracking, behavior monitoring, and measurement of phenotypes of interest, such as body weight, condition score, and gait. Computer vision systems (CVS) have the potential to be used in precision livestock farming and high-throughput phenotyping applications. We believe that the constant measurement of traits through CVS can reduce management costs and optimize decision-making in livestock operations, in addition to opening new possibilities in selective breeding. Applications of CSV are currently a growing research area and there are already commercial products available. However, there are still challenges that demand research for the successful development of autonomous solutions capable of delivering critical information. This review intends to present significant developments that have been made in CVS applications in animal and veterinary sciences and to highlight areas in which further research is still needed before full deployment of CVS in breeding programs and commercial farms.

show abstract

“…There is a lot of wind to beat the wind flow generated by the fan. However, this result is an ideal result for chicks aged 15-21 days, especially on temperature monitoring, while the ideal humidity is also ideal for chickens in general [26].…”

mentioning

confidence: 99%

“…Overall monitoring data obtained the mean difference and the percentage difference between the mean of 3.30 ºC; 2.61%; and 13.4 ºC; 3.95% for temperature and humidity respectively compared to data obtained from standard measuring instruments. The monitoring device used is a DHT11 sensor with a tolerance limit for the difference in temperature and humidity of 2 ºC and 5%, respectively [26]. Based on this information, the measurement by monitoring device in the humidity aspect can still be tolerated and is considered accurate.…”

mentioning

confidence: 99%

The Design of A Telegram IoT-based Chicken Coop Monitoring and Controlling System

Adinegoro¹,

Habbani

Karimah

et al. 2020

JPSE

View full text Add to dashboard Cite

Less ideal chicken coop condition is one reason for the low productivity of chickens in Indonesia. Based on this condition, breeders need monitoring and control devices that can be used easily anywhere and anytime. In this study, the monitoring and control devices based on the Internet of Things (IoT) were successfully designed using the ESP8266 microcontroller. The monitoring system used a DHT11 temperature sensor while the controller used a bulb and fan as the actuator. Monitoring data and control commands were displayed via Telegram. Coop testing was carried out with three different treatments-the light was on, the fan was on, and both were on-resulting in temperature and humidity measurement data ranging from 24–32 ºC and 61–78%, respectively. Therefore, this monitoring and control device can prevent the conditions of the chicken coop that are ideal less so that chickens' productivity increases.

show abstract

A machine vision system for early detection and prediction of sick birds: A broiler chicken model

Cited by 96 publications

References 38 publications

A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

Image Analysis and Computer Vision Applications in Animal Sciences: An Overview

The Design of A Telegram IoT-based Chicken Coop Monitoring and Controlling System

Contact Info

Product

Resources

About