Role of Computer Science (Artificial Intelligence) In Poultry Management

Patel, Hrishitva; Sana, Adil

doi:10.36418/dev.v3i12.250

Cited by 2 publications

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“…One such technology is precision nutrition, which involves customizing poultry diets based on individual bird characteristics and daily nutritional requirements [178]. By leveraging techniques like artificial intelligence and machine learning, researchers can develop predictive models that optimize riboflavin and other vitamin supplementation, potentially tailoring diets for each bird in a flock [179]. This approach can reduce waste, lower production costs, and enhance overall flock performance.…”

Section: Future Prospects and Research Avenuesmentioning

confidence: 99%

From Metabolism to Vitality: Uncovering Riboflavin’s Importance in Poultry Nutrition

Shastak,

Pelletier

2023

Animals

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Riboflavin, or vitamin B2, is indispensable for poultry, profoundly impacting their metabolic equilibrium, growth, and overall health. In a climate of increasing demand for poultry products and heightened production intensity, grasping the multifaceted roles of riboflavin in domestic fowl nutrition becomes paramount. This essential vitamin serves as a precursor to two vital coenzymes, flavin mononucleotide and flavin adenine dinucleotide, integral players in pivotal redox reactions and energy metabolism. Inadequate riboflavin levels translate into stunted growth, skeletal deformities, and compromised feed conversion efficiency, thereby adversely affecting poultry performance and bottom-line profitability. Riboflavin goes beyond its fundamental role, ameliorating nutrient utilization, facilitating protein synthesis, and augmenting enzyme activity, rightfully earning its epithet as the “growth-promoting vitamin”. Poultry’s reproductive success intricately hinges on riboflavin levels, dictating egg production and hatchability. It is imperative to note that riboflavin requirements exhibit variations among poultry species and distinct production phases, emphasizing the importance of judicious and balanced supplementation strategies. Aligning dietary recommendations with genetic advancements holds the promise of fostering sustainable growth within the poultry sector. Exploring the multifaceted aspects of riboflavin empowers researchers, nutritionists, and producers to elevate poultry nutrition and overall well-being, harmonizing with the industry’s evolving demands.

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Section: Future Prospects and Research Avenuesmentioning

confidence: 99%

From Metabolism to Vitality: Uncovering Riboflavin’s Importance in Poultry Nutrition

Shastak,

Pelletier

2023

Animals

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“…Recognizing the urgent need for modern and more sophisticated technologies, the agricultural sector is increasingly turning to AI [3][4][5]. As a result, the role of AI in poultry farming has gained significant prominence [6]. In the poultry industry, the application of image processing technologies has produced impressive results [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Towards Early Poultry Health Prediction through Non-Invasive and Computer Vision-Based Dropping Classification

Nakrosis,

Paulauskaite-Taraseviciene,

Raudonis

et al. 2023

Animals

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The use of artificial intelligence techniques with advanced computer vision techniques offers great potential for non-invasive health assessments in the poultry industry. Evaluating the condition of poultry by monitoring their droppings can be highly valuable as significant changes in consistency and color can be indicators of serious and infectious diseases. While most studies have prioritized the classification of droppings into two categories (normal and abnormal), with some relevant studies dealing with up to five categories, this investigation goes a step further by employing image processing algorithms to categorize droppings into six classes, based on visual information indicating some level of abnormality. To ensure a diverse dataset, data were collected in three different poultry farms in Lithuania by capturing droppings on different types of litter. With the implementation of deep learning, the object detection rate reached 92.41% accuracy. A range of machine learning algorithms, including different deep learning architectures, has been explored and, based on the obtained results, we have proposed a comprehensive solution by combining different models for segmentation and classification purposes. The results revealed that the segmentation task achieved the highest accuracy of 0.88 in terms of the Dice coefficient employing the K-means algorithm. Meanwhile, YOLOv5 demonstrated the highest classification accuracy, achieving an ACC of 91.78%.

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Role of Computer Science (Artificial Intelligence) In Poultry Management

Cited by 2 publications

References 0 publications

From Metabolism to Vitality: Uncovering Riboflavin’s Importance in Poultry Nutrition

From Metabolism to Vitality: Uncovering Riboflavin’s Importance in Poultry Nutrition

Towards Early Poultry Health Prediction through Non-Invasive and Computer Vision-Based Dropping Classification

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