Beyond the Spectrum: Unleashing the Potential of Infrared Radiation in Poultry Industry Advancements.

The poultry industry is dynamically advancing production by focusing on nutrition, management practices, and technology to enhance productivity by improving feed conversion ratios, disease control, lighting management, and exploring antibiotic alternatives. Infrared (IR) radiation is utilized to improve the well-being of humans, animals, and poultry through various operations. IR radiation occurs via electromagnetic waves with wavelengths ranging from 760 to 10,000 nm. The biological applications of IR radiation are gaining significant attention and its utilization is expanding rapidly across multiple sectors. Various IR applications, such as IR heating, IR spectroscopy, IR thermography, IR beak trimming, and IR in computer vision, have proven to be beneficial in enhancing the well-being of humans, animals, and birds within mechanical systems. IR radiation offers a wide array of health benefits, including improved skin health, therapeutic effects, anticancer properties, wound healing capabilities, enhanced digestive and endothelial function, and improved mitochondrial function and gene expression. In the realm of poultry production, IR radiation has demonstrated numerous positive impacts, including enhanced growth performance, gut health, blood profiles, immunological response, food safety measures, economic advantages, the mitigation of hazardous gases, and improved heating systems. Despite the exceptional benefits of IR radiation, its applications in poultry production are still limited. This comprehensive review provides compelling evidence supporting the advantages of IR radiation and advocates for its wider adoption in poultry production practices.

An efficient segmentation model for abnormal chicken droppings recognition based on improved deep dual-resolution network.

The characteristics of chicken droppings are closely linked to their health status. In prior studies, chicken droppings recognition is treated as an object detection task, leading to challenges in labeling and missed detection due to the diverse shapes, overlapping boundaries, and dense distribution of chicken droppings. Additionally, the use of intelligent monitoring equipment equipped with edge devices in farms can significantly reduce manual labor. However, the limited computational power of edge devices presents challenges in deploying real-time segmentation algorithms for field applications. Therefore, this study redefines the task as a segmentation task, with the main objective being the development of a lightweight segmentation model for the automated monitoring of abnormal chicken droppings. A total of 60 Arbor Acres broilers were housed in 5 specific pathogen-free cages for over 3 wk, and 1650 RGB images of chicken droppings were randomly divided into training and testing sets in an 8:2 ratio to develop and test the model. Firstly, by incorporating the attention mechanism, multi-loss function, and auxiliary segmentation head, the segmentation accuracy of the DDRNet was enhanced. Then, by employing the group convolution and an advanced knowledge-distillation algorithm, a lightweight segmentation model named DDRNet-s-KD was obtained, which achieved a mean Dice coefficient (mDice) of 79.43% and an inference speed of 86.10 frames per second (FPS), showing a 2.91% and 61.2% increase in mDice and FPS compared to the benchmark model. Furthermore, the DDRNet-s-KD model was quantized from 32-bit floating-point values to 8-bit integers and then converted to TensorRT format. Impressively, the weight size of the quantized model was only 13.7 MB, representing an 82.96% reduction compared to the benchmark model. This makes it well-suited for deployment on the edge device, achieving an inference speed of 137.51 FPS on Jetson Xavier NX. In conclusion, the methods proposed in this study show significant potential in monitoring abnormal chicken droppings and can provide an effective reference for the implementation of other agricultural embedded systems.

The characteristics of chicken droppings are closely related to their health. In this study, we developed a lightweight segmentation model for chicken droppings and evaluated its inference speed on the edge device with limited computational power. The results showed that the proposed model exhibits significant potential in the early warning of abnormal chicken droppings, which can help producers implement interventions before disease outbreaks, thereby avoiding great economic losses. Additionally, the model optimization and compression processes proposed in this study can provide an effective reference for the implementation of other embedded systems.

Effects of light color and intensity on discrimination of red objects in broilers.

Poultry are sensitive to red objects, such as comb and blood on the body surface, likely inducing injurious pecking in flocks. Light is an important factor that affects the pecking behavior of poultry. A wooden box was built to investigate the effects of Light Emitting Diode (LED) light color (warm white and cold white) and intensity (5 and 50 lux) of background light on the discrimination of red objects in broilers. A piece of red photographic paper (Paper 1) was used to simulate a red object and paired with another piece of paper (Paper 2 to 8) with a different color. Bigger number of the paired paper indicated greater color difference. The experiment consisted of three phases: adaptation, training, and test. In the adaptation phase, birds were selected for the adaptation to reduce the stress from the box. In the training phase, birds were trained to discriminate and peck at Paper 1 when paired with Paper 8 under one type of background light. Twenty-three birds were tested when the paired paper was changed from Paper 7 to 2. Each pair of paper included 12 trials for every bird, and response time to peck and proportion of choices of Paper 1 in the last 10 trials were collected. The results showed that broilers tested under 5 lux light had longer response times than broilers tested under 50 lux light (P < 0.05). When Paper 1 was paired with paper 7, broilers tested under warm white light had lower proportion of choices of Paper 1 than those tested under cold white light (P < 0.05). Color difference had a significant effect on response time of broilers (P < 0.05). Moreover, the proportion of choices of Paper 1 decreased to 50% (chance-level performance) when color of the paired paper was gradually similar to Paper 1. Conclusively, rearing broilers in warm white rather than cold white light with appropriate light intensity should be recommended to reduce damaging pecking behavior in broiler production.

Poultry are sensitive to red objects, such as comb and blood on the body surface, likely inducing injurious pecking in flocks. We built a wooden box to investigate the effects of light color (reddish and bluish) and intensity (5 and 50 lux) of background light on the discrimination of red objects in broilers. A piece of red photographic paper (Paper 1) was used and paired with another piece of paper (Paper 2 to 8) with a different color. Every bird was trained to discriminate and peck at Paper 1 when paired with Paper 8 under one type of background light. Then, Paper 8 was changed from Paper 7 to 2. Response time to peck and proportion of choices of Paper 1 were collected. We found that broilers under 5 lux light had longer response times than under 50 lux light. Broilers under reddish light had lower proportion of choices than under bluish light. Moreover, color difference had a significant effect on the response time and the proportion of choices. Conclusively, rearing broilers under reddish rather than bluish light with appropriate intensity should be recommended to reduce damaging pecking behavior in broiler production.

A reliable and low-cost deep learning model integrating convolutional neural network and transformer structure for fine-grained classification of chicken Eimeria species.

Chicken coccidiosis is a disease caused by Eimeria spp. and costs the broiler industry more than 14 billion dollars per year globally. Different chicken Eimeria species vary significantly in pathogenicity and virulence, so the classification of different chicken Eimeria species is of great significance for the epidemiological survey and related prevention and control. The microscopic morphological examination for their classification was widely used in clinical applications, but it is a time-consuming task and needs expertise. To increase the classification efficiency and accuracy, a novel model integrating transformer and convolutional neural network (CNN), named Residual-Transformer-Fine-Grained (ResTFG), was proposed and evaluated for fine-grained classification of microscopic images of seven chicken Eimeria species. The results showed that ResTFG achieved the best performance with high accuracy and low cost compared with traditional models. Specifically, the parameters, inference speed and overall accuracy of ResTFG are 1.95M, 256 FPS and 96.9%, respectively, which are 10.9 times lighter, 1.5 times faster and 2.7% higher in accuracy than the benchmark model. In addition, ResTFG showed better performance on the classification of the more virulent species. The results of ablation experiments showed that CNN or Transformer alone had model accuracies of only 89.8% and 87.0%, which proved that the improved performance of ResTFG was benefit from the complementary effect of CNN's local feature extraction and transformer's global receptive field. This study invented a reliable, low-cost, and promising deep learning model for the automatic fine-grain classification of chicken Eimeria species, which could potentially be embedded in microscopic devices to improve the work efficiency of researchers and extended to other parasite ova, and applied to other agricultural tasks as a backbone.

Effects of dietary inclusion of chromium propionate on growth performance, intestinal health, immune response and nutrient transporter gene expression in broilers.

The intent of the present study was to evaluate the effect of chromium (Cr3+) as chromium propionate on growth performance, organ index, immune response, intestinal morphology and nutrient transporter gene expression in broilers. One-day-old broiler chicks (n = 756) were divided into six experimental groups of 126 chicks; each group was further divided into 7 replicates (18 chicks/replicate). All birds were offered corn-soybean diets supplemented with Cr3+ at 0, 0.10, 0.15, 0.20, 0.25 or 0.30 mg/kg. Dietary inclusion of Cr3+ at various levels yielded significantly better growth performance and organ index in birds. Similarly, antibody titre against Newcastle disease and avian influenza H5 at various ages was found to be significantly higher in birds that received 0.15 mg/kg Cr3+ in the diet. Significant results with respect to villus height (VH), crypt depth (CD) and VH:CD were observed in all groups that received Cr3+ in the diet compared to control. Moreover, it was observed that different levels of Cr3+ supplementation of the diet also increased the expression of the nutrient transporter genes SGLT1, GLUT2, rBAT and CAT1 in broilers. The findings of the present study suggest that dietary inclusion of Cr3+ at various levels may have beneficial effects on growth performance, immunity, intestinal morphology and nutrient transporter gene expression in broilers. Supplementation of the diet with Cr3+ at a level of 0.15 mg/kg could yield better performance in broiler production.

Critical analysis of excessive utilization of crude protein in ruminants ration: impact on environmental ecosystem and opportunities of supplementation of limiting amino acids-a review.

Protein quality plays a key role than quantity in growth, production, and reproduction of ruminants. Application of high concentration of dietary crude protein (CP) did not balance the proportion of these limiting amino acids (AA) at duodenal digesta of high producing dairy cow. Thus, dietary supplementation of rumen-protected AA is recommended to sustain the physiological, productive, and reproductive performance of ruminants. Poor metabolism of high CP diets in rumen excretes excessive nitrogen (N) through urine and feces in the environment. This excretion is usually in the form of nitrous oxide, nitric oxide, nitrate, and ammonia. In addition to producing gases like methane, hydrogen carbon dioxide pollutes and has a potentially negative impact on air, soil, and water quality. Data specify that supplementation of top-limiting AA methionine and lysine (Met + Lys) in ruminants' ration is one of the best approaches to enhance the utilization of feed protein and alleviate negative biohazards of CP in ruminants' ration. In conclusion, many in vivo and in vitro studies were reviewed and reported that low dietary CP with supplemental rumen-protected AA (Met + Lys) showed a good ability to reduce N losses or NH3. Also, it helps in declining gases emission and decreasing soil or water contamination without negative impacts on animal performance. Finally, further studies are needed on genetic and molecular basis to explain the impact of Met + Lys supplementation on co-occurrence patterns of microbiome of rumen which shine new light on bacteria, methanogen, and protozoal interaction in ruminants.

Considering choline as methionine precursor, lipoproteins transporter, hepatic promoter and antioxidant agent in dairy cows.

During the transition period, fatty liver syndrome may be caused in cows undergo negative energy balance, ketosis or hypocalcemia, retained placenta or mastitis problems. During the transition stage, movement of non-esterified fatty acids (NEFA) increases into blood which declines the hepatic metabolism or reproduction and consequently, lactation performance of dairy cows deteriorates. Most of studies documented that, choline is an essential nutrient which plays a key role to decrease fatty liver, NEFA proportion, improve synthesis of phosphatidylcholine, maintain lactation or physiological function and work as anti-oxidant in the transition period of dairy cows. Also, it has a role in the regulation of homocysteine absorption through betaine metabolite which significantly improves plasma α-tocopherol and interaction among choline, methionine and vitamin E. Many studies reported that, supplementation of rumen protected form of choline during transition time is a sustainable method as rumen protected choline (RPC) perform diverse functions like, increase glucose level or energy balance, fertility or milk production, methyl group metabolism, or signaling of cell methionine expansion or methylation reactions, neurotransmitter synthesis or betaine methylation, increase transport of lipids or lipoproteins efficiency and reduce NEFA or triacylglycerol, clinical or sub clinical mastitis and general morbidity in the transition dairy cows. The purpose of this review is that to elucidate the choline importance and functions in the transition period of dairy cows and deal all morbidity during transition or lactation period. Furthermore, further work is needed to conduct more studies on RPC requirements in dairy cows ration under different feeding conditions and also to elucidate the genetic and molecular mechanisms of choline in ruminants industry.