A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution

Guo, Yangyang; Chai, Lilong; Aggrey, Samuel E.; Oladeinde, Adelumola; Johnson, Jasmine; Zock, Gregory

doi:10.3390/s20113179

Cited by 53 publications

(39 citation statements)

References 26 publications

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“…The integration of smart sensors and technologies in the broiler rearing processor helps producers to optimize and minimize production losses and improves monitoring of birds in real-time [ 37 , 38 , 39 ]. The present study provided valuable information about an automatic broiler feeding system that collaborates with the application and implementation of intelligent systems as a data management tool during the broiler production process, potentially contributing to precision livestock farming to monitor the welfare of birds.…”

Section: Discussionmentioning

confidence: 99%

Smart Feeding Unit for Measuring the Pecking Force in Farmed Broilers

Seber

Moura

Lima

et al. 2021

Animals

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Feeding is one of the most critical processes in the broiler production cycle. A feeder can collect data of force signals and continuously transform it into information about birds’ feed intake and quickly permit more agile and more precise decision-making concerning the broiler farm’s production process. A smart feeding unit (SFU) prototype was developed to evaluate the broiler pecking force and average feed intake per pecking (g/min). The prototype consisted of a power supply unit with a data acquisition module, management software connected to a computer for data storage, and a video camera to verify the pecking force during signal processing. In the present study, seven male Cobb-500 broilers were raised in an experimental chamber to test and commission the prototype. The prototype consisted of a feeding unit (feeder) with a data acquisition module (amplifier), with real-time integration for testing and intuitive operation with Catman Easy software connected to a computer to obtain and store data from signals. The sampling of average feed intake per pecking per broiler (g) was conducted during the first minute of feeding, subtracting the amount of feed provided per the amount of feed consumed, including the count of pecking in the first minute of feeding. An equation was used for estimating the average feed intake per pecking per broiler (g). The results showed that the average broiler pecking force was 1.39 N, with a minimum value of 0.04 N and a maximum value of 7.29 N. The average feed intake per pecking (FIP) was 0.13 g, with an average of 173 peckings per minute. The acquisition, processing, and classification of signals in the pecking force information were valuable during broilers’ feeding. The smart feeding unit prototype for broilers was efficient in the continuous assessment of feed intake and can generate information for estimating broiler performance.

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Section: Discussionmentioning

confidence: 99%

Smart Feeding Unit for Measuring the Pecking Force in Farmed Broilers

Seber

Moura

Lima

et al. 2021

Animals

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“…This study was conducted in two identical experimental broiler facilities on the Poultry Research Farm at the University of Georgia, Athens, USA. Unless otherwise stated, the experimental setup and data have been previously published in [ 19 ]. Briefly, six identical pens (measuring 1.84 L × 1.16 W m, 20 Cobb 500 broiler chickens/pen, the density was about 0.11 m 2 floor per bird) were monitored separately with a high definition (HD) camera (PRO-1080MSFB, Swann Communications, Santa Fe Springs, CA, USA) mounted on the ceiling (2.5 m above floor) to capture video (15 frame/s with the resolution of 1440 × 1080 pixels).…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, the first four weeks of chicken images were selected as research samples in this study. The method for target detection has been developed and published; see our other paper [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

“…Image processing technology distinguishes target and non-target areas through image information characteristics. We used image processing technology to detect the area of the broiler chicken in the video scene and further analyzed the distribution of the broiler chicken [ 19 ]. Although the target area could be detected through image processing technology, it was dependent on the information in the scene.…”

Section: Introductionmentioning

confidence: 99%

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A Machine Vision-Based Method Optimized for Restoring Broiler Chicken Images Occluded by Feeding and Drinking Equipment

Guo

Aggrey

Oladeinde

et al. 2021

Animals

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The presence equipment (e.g., water pipes, feed buckets, and other presence equipment, etc.) in the poultry house can occlude the areas of broiler chickens taken via top view. This can affect the analysis of chicken behaviors through a vision-based machine learning imaging method. In our previous study, we developed a machine vision-based method for monitoring the broiler chicken floor distribution, and here we processed and restored the areas of broiler chickens which were occluded by presence equipment. To verify the performance of the developed restoration method, a top-view video of broiler chickens was recorded in two research broiler houses (240 birds equally raised in 12 pens per house). First, a target detection algorithm was used to initially detect the target areas in each image, and then Hough transform and color features were used to remove the occlusion equipment in the detection result further. In poultry images, the broiler chicken occluded by equipment has either two areas (TA) or one area (OA). To reconstruct the occluded area of broiler chickens, the linear restoration method and the elliptical fitting restoration method were developed and tested. Three evaluation indices of the overlap rate (OR), false-positive rate (FPR), and false-negative rate (FNR) were used to evaluate the restoration method. From images collected on d2, d9, d16, and d23, about 100-sample images were selected for testing the proposed method. And then, around 80 high-quality broiler areas detected were further evaluated for occlusion restoration. According to the results, the average value of OR, FPR, and FNR for TA was 0.8150, 0.0032, and 0.1850, respectively. For OA, the average values of OR, FPR, and FNR were 0.8788, 0.2227, and 0.1212, respectively. The study provides a new method for restoring occluded chicken areas that can hamper the success of vision-based machine predictions.

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“…In poultry or livestock houses, environmental quality monitoring and animal imaging are important tasks for evaluating if animals have a good production environment and how their health and welfare are affected in commercial production system [30,31]. Traditional methods (e.g., installing sensors or cameras in fixed places) have limitations in selecting representative data collection locations due to the large size of the poultry house (e.g., 150 m length and 30 m wide) [32].…”

Section: Poultry House Uav Landing Simulationmentioning

confidence: 99%

Precision Landing Test and Simulation of the Agricultural UAV on Apron

Guo

Liu

et al. 2020

Sensors

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Unmanned aerial vehicle (UAV) has been used to assist agricultural production. Precision landing control of UAV is critical for application of it in some specific areas such as greenhouses or livestock/poultry houses. For controlling UAV landing on a fixed or mobile apron/platform accurately, this study proposed an automatic method and tested it under three scenarios: (1) UAV landing at high operating altitude based on the GPS signal of the mobile apron; (2) UAV landing at low operating altitude based on the image recognition on the mobile apron; and (3) UAV landing progress control based on the fixed landing device and image detection to achieve a stable landing action. To verify the effectiveness of the proposed control method, apron at both stationary and mobile (e.g., 3 km/h moving speed) statuses were tested. Besides, a simulation was conducted for the UAV landing on a fixed apron by using a commercial poultry house as a model (135 L × 15 W × 3 H m). Results show that the average landing errors in high altitude and low altitude can be controlled within 6.78 cm and 13.29 cm, respectively. For the poultry house simulation, the landing errors were 6.22 ± 2.59 cm, 6.79 ± 3.26 cm, and 7.14 ± 2.41cm at the running speed of 2 km/h, 3 km/h, and 4 km/h, respectively. This study provides the basis for applying the UAV in agricultural facilities such as poultry or animal houses where requires a stricter landing control than open fields.

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A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution

Cited by 53 publications

References 26 publications

Smart Feeding Unit for Measuring the Pecking Force in Farmed Broilers

Smart Feeding Unit for Measuring the Pecking Force in Farmed Broilers

A Machine Vision-Based Method Optimized for Restoring Broiler Chicken Images Occluded by Feeding and Drinking Equipment

Precision Landing Test and Simulation of the Agricultural UAV on Apron

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