ABSTRACT
The growth performance and blood indices of medicated and non-medicated broiler chickens have been the subject of this research coupled with a paucity of comparative information on what can actually happen to broiler chickens if not medicated when reared under humid tropical conditions. One hundred unsexed day-old broilers were randomly and equally allotted into two treatment groups of TM (medicated) and TN (non-medicated) in a completely randomized design each treatment with five replicates having ten birds each. The birds were reared on deep litter system for 56 d which was divided into two phases of 28 d each (starter and finisher), during which data were collected with respect to daily feed intake, final body weight, body weight gained (BWG), mortality rate while blood analysis was carried out on 28th and 56th d for starter and finisher phases, respectively. Non-medicated group served as control. Feed conversion ratio (FCR) and feed conversion efficiency (FCE), were later calculated. Data collected were subjected to analysis of variance statistically. There was no significant difference between the medicated and non-medicated broilers for daily feed intake, final body weight, and BWG and also for the blood parameters investigated at starter phase. However, at finisher phase, no significant difference was observed in the daily feed intake of Tm and Tn but there was significant (Pâ <â 0.05) difference in the final body weight, BWG, FCR, FCE, and mortality rate between the two groups. Birds on Tm attained higher weight significantly (Pâ <â 0.05) than those on TN. BWG, FCR, and FCE followed the same trend and also the mortality rate. White blood cells count of TN was higher significantly (Pâ <â 0.05) than TM while TM birds recorded higher packed cell volume, red blood counts, and hemoglobin concentration (Hb) significantly (Pâ <â 0.05) than TM birds. Effect of medication was much noticeable at finisher phase as it improved the growth rate though mortality rate was close to that of TN group. These results suggest that broilers can be produced free of medication with good feeding without loss of birds while the growth rate can be enhanced with the use of prebiotics and prebiotics.
ABSTRACT
Feces is one quick way to determine the health status of the birds and farmers rely on years of experience as well as professionals to identify and diagnose poultry diseases. Most often, farmers lose their flocks as a result of delayed diagnosis or a lack of trustworthy experts. Prevalent diseases affecting poultry birds may be quickly noticed from image of poultry bird's droppings using artificial intelligence based on computer vision and image analysis. This paper provides description of a dataset of both healthy and unhealthy poultry fecal imagery captured from selected poultry farms in south-west of Nigeria using smartphone camera. The dataset was collected at different times of the day to account for variability in light intensity and can be applied in machine learning models development for abnormality detection in poultry farms. The dataset collected is 19,155 images; however, after preprocessing which encompasses cleaning, segmentation and removal of duplicates, the data strength is 14,618 labeled images. Each image is 100 by 100 pixels size in jpeg format. Additionally, computer vision applications like picture segmentation, object detection, and classification can be supported by the dataset. This dataset's creation is intended to aid in the creation of comprehensive tools that will aid farmers and agricultural extension agents in managing poultry farms in an effort to minimize loss and, as a result, optimize profit as well as the sustainability of protein sources.
ABSTRACT
Population expansion and rising consumer demand for nutrient-dense meals have both contributed to an increase in the consumption of animal protein worldwide. A significant portion of the meat and eggs used for human consumption come from the poultry industry. Early diagnosis and warning of infectious illnesses in poultry are crucial for enhancing animal welfare and minimizing losses in the breeding and production systems for poultry. On the other hand, insufficient techniques for early diagnosis as well as infectious disease control in poultry farms occasionally fail to stop declining productivity and even widespread death. Individual physiological, physical, and behavioral symptoms in poultry, such as fever-induced increases in body temperature, abnormal vocalization due to respiratory conditions, and abnormal behavior due to pathogenic infections, frequently represent the health status of the animal. When birds have respiratory problems, they make strange noises like coughing and snoring. The work is geared towards compiling a dataset of chickens that were both healthy and unhealthy. 100 day-old poultry birds were purchased and split into two groups at the experimental site, the poultry research farm at Bowen University. For respiratory illnesses, the first group received treatment, whereas the second group did not. After that, the birds were separated and caged in a monitored environment. To eliminate extraneous sounds and background noise that might affect the analysis, microphones were set a reasonable distance away from the birds. The data was gathered using 24-bit samples at 96 kHz. For 65 days, three times per day (morning, afternoon, and night) of audio data were continually collected. Food and water are constantly provided to the birds during this time. During this time, the birds have constant access to food and water. After 30 days, the untreated group started to sound sick with respiratory issues. This information was also noted as being unhealthy. Chickens' audio signals were recorded, saved in MA4, and afterwards converted to WAV format. This dataset's creation is intended to aid in the design of smart technologies capable of early detection and monitoring of the status of birds in poultry farms in a continuous, noninvasive, and automated way.
