Organ Segmentation in Poultry Viscera Using RGB-D

Interindividual distances and orientations of laying hens under 8 stocking densities measured by integrative deep learning techniques

Interindividual distances and orientations of laying hens provide quantitative measures to calculate and optimize space allocations for bird flocks. However, these metrics were often measured manually and have not been examined for different stocking densities of laying hens. The objectives of this study were to 1) integrate and develop several deep learning techniques to detect interindividual distances and orientations of laying hens; and 2) examine the 2 metrics under 8 stocking densities via the developed techniques. Laying hens (Jingfen breed, a popular hen breed in China) at 35 wk of age were raised in experimental compartments at 8 different stocking densities of 3,840, 2,880, 2,304, 1,920, 1,646, 1,440, 1,280, and 1,152 cm2•bird-1 (3-10 hens per compartment, respectively), and cameras on the top of the compartments recorded videos for further analysis. The designed deep learning image classifier achieved over 99% accuracy to classify bird's perching status and excluded frames with bird perching to ensure that all birds analyzed were on the same horizontal plane, reducing calculation errors. The YOLOv5m oriented object detection model achieved over 90% precision, recall, and F1 score in detecting birds in compartments and can output bird centroid coordinates and angles, from which interindividual distances and orientations were calculated based on pairs of birds. Laying hens maintained smaller minimum interindividual distances in higher stocking densities. They were in an intersecting relationship with conspecifics for over 90% of the time. The developed integrative deep learning techniques and behavior metrics provide animal-based measurement of space requirement for laying hens.

Measuring haemolysis in cattle serum by direct UV-VIS and RGB digital image-based methods

A simple, rapid procedure is required for the routine detection and quantification of haemolysis, one of the main sources of unreliable results in serum analysis. In this study, we compared two different approaches for the rapid determination of haemolysis in cattle serum. The first consisted of estimating haemolysis via a simple direct ultraviolet–visible (UV–VIS) spectrophotometric measurement of serum samples. The second involved analysis of red, green, blue (RGB) colour data extracted from digital images of serum samples and relating the haemoglobin (Hb) content by means of both univariate (R, G, B and intensity separately) and multivariate calibrations (R, G, B and intensity jointly) using partial least squares regression and artificial neural networks. The direct UV–VIS analysis and RGB-multivariate analysis using neural network methods were both appropriate for evaluating haemolysis in serum cattle samples. The procedures displayed good accuracy (mean recoveries of 100.7 and 102.1%, respectively), adequate precision (with coefficients of variation from 0.21 to 2.68%), limit of detection (0.14 and 0.21 g L^–1, respectively), and linearity of up to 10 g L^–1.

Learn to Train: Improving Training Data for a Neural Network to Detect Pecking Injuries in Turkeys

This study aimed to develop a camera-based system using artificial intelligence for automated detection of pecking injuries in turkeys. Videos were recorded and split into individual images for further processing. Using specifically developed software, the injuries visible on these images were marked by humans, and a neural network was trained with these annotations. Due to unacceptable agreement between the annotations of humans and the network, several work steps were initiated to improve the training data. First, a costly work step was used to create high-quality annotations (HQA) for which multiple observers evaluated already annotated injuries. Therefore, each labeled detection had to be validated by three observers before it was saved as "finished", and for each image, all detections had to be verified three times. Then, a network was trained with these HQA to assist observers in annotating more data. Finally, the benefit of the work step generating HQA was tested, and it was shown that the value of the agreement between the annotations of humans and the network could be doubled. Although the system is not yet capable of ensuring adequate detection of pecking injuries, the study demonstrated the importance of such validation steps in order to obtain good training data.

Effect of Age on the Immune and Visceral Organ Weights and Cecal Traits in Modern Broilers

Simple Summary

Currently, due to the high developments achieved in the poultry industry especially in genetics, management, nutrition, health, and animal welfare, modern broilers reach slaughter weight at an earlier age, which in turn has brought about notable changes in the morphophysiology of these birds. The following research proposes to determine the effect of age on visceral and immune organ weight, cecal pH, and cecal lactic acid bacteria in Ross 308^® broilers, up to 10 days old. It was concluded that the immune and visceral organs increase their absolute and relative weight according to age and on days 9 and 10 the highest growth rate of the organs was found, furthermore, the colonization of the cecal lactic acid bacteria is established before 10 days of life (as the most critical stage), although with variable changes for intestinal pH. The correlation showed, in addition, a significant association between the organs evaluated, as well as for the cecum relative weight and the cecal lactic bacteria count. These results could contribute to updating knowledge on immunological activity, cecal microbiology, and the functioning of the digestive system, as well as for the development of new nutritional requirements and the optimization of dietary formulations.

Abstract

This study aimed to determine the effect of age on the immune and visceral organ weights and cecal traits in modern broilers. 200 male Ross^® 308 broilers were randomly selected, then 20 broilers were slaughtered every day (up to 10 days old) after six hours of fasting. All the organs measured had a progressive increase in absolute weight as the days progressed, apart from the spleen, which decreased its absolute weight on day 5, even though on day 10 it showed the highest values. Moreover, the small intestine relative weight increased from the fourth to the ninth day and was correlated (p ≤ 0.05) with the relative weight of the proventriculus, gizzard, small intestine, and cecum, although without statistical association with the of the heart. There was a correlation between the cecum relative weight and the cecal lactic acid bacteria, and between the primary lymphoid organs. The pH (from 5.74 to 7.40) and cecal lactic acid bacteria (from 6.11 to 8.79 log 10 CFU/g) changed according to the age of the broilers. The results could contribute to the understanding of the physiology and intestinal microbiology of the first 10 days old of modern broilers, which is crucial to improve the genetic expression of these animals.

Application of optical technologies in the US poultry slaughter facilities for the detection of poultry carcase condemnation

1. This article reviews the studies on optical technologies for automated poultry carcase inspection, discusses challenges and potential solutions in their real-time applications in poultry slaughter facilities. 2. Over the past few decades, extensive research has been underway to develop an optical technology-based machine vision system for automated inspection of poultry carcases and viscera. Such an automated technology will not only aid in carcase inspection to maximise food safety, but it will also support the U.S. New Poultry Inspection System's aim to foster innovation in poultry processing as well as increase line speed. 3. Many earlier studies based on visible and near-infrared spectroscopy showed promise, but could not be implemented successfully in an on-line poultry processing plant. Currently, multi- and hyper-spectral imaging-based machine vision systems have shown promising outcomes. 5. The critical hurdles for real-time application of automated imaging technology in poultry carcase inspection include high-speed processing lines, slaughter facilities environment and variation in broiler rearing practices. Therefore, further improvement in imaging and machine vision technologies based on physiochemical properties on poultry carcases, the establishment of more technology friendly inspection station, and an integrated data management for different rearing practices are essential to overcome those hurdles.