TY - JOUR
T1 - Latest Trend and Challenges in Machine Learning– and Deep Learning–Based Computational Techniques in Poultry Health and Disease Management
T2 - A Review
AU - Shwetha, V.
AU - Maddodi, B. S.
AU - Laxmi, Vijaya
AU - Kumar, Abhinav
AU - Shrivastava, Sakshi
N1 - Publisher Copyright:
© 2024 Shwetha V. et al.
PY - 2024
Y1 - 2024
N2 - To determine the fock’s economic worth, free-range chicken growers must determine the gender, bird movement, behavior, disease detection, and lameness of the chickens. However, because of the complex environmental background and the fuctuating chicken population, it is difcult for farmers to efectively and properly measure those characteristics. Manual estimation is also inaccurate and time-consuming because probable identifcation occurs in their life cycle. Terefore, the industry benefts from automated systems that can produce fndings quickly and precisely in managing health and diseases. Te advancement of machine learning (ML)– and deep learning (DL)–based algorithms are boons for poultry health and disease management. Tis study reviews the literature using ML and DL techniques in prediction, classifcation, and disease detection in various metrics, namely, poultry health and disease management. We have considered the research article published from 2010 to 2023 in this study, which uses ML-and DL-based computation techniques in poultry welfare metrics such as gender identifcation, tracking of poultry, analysis of broiler chicken behavior, detection of poultry diseases, lameness and broiler weight, and stress monitoring. In addition, this review explores the most recent developments, difculties, strategies, and databases used in image preprocessing feature extraction and classifcation. Te review addresses these challenges and discusses the approaches and techniques researchers employ to tackle them in the feld of poultry management and disease detection.
AB - To determine the fock’s economic worth, free-range chicken growers must determine the gender, bird movement, behavior, disease detection, and lameness of the chickens. However, because of the complex environmental background and the fuctuating chicken population, it is difcult for farmers to efectively and properly measure those characteristics. Manual estimation is also inaccurate and time-consuming because probable identifcation occurs in their life cycle. Terefore, the industry benefts from automated systems that can produce fndings quickly and precisely in managing health and diseases. Te advancement of machine learning (ML)– and deep learning (DL)–based algorithms are boons for poultry health and disease management. Tis study reviews the literature using ML and DL techniques in prediction, classifcation, and disease detection in various metrics, namely, poultry health and disease management. We have considered the research article published from 2010 to 2023 in this study, which uses ML-and DL-based computation techniques in poultry welfare metrics such as gender identifcation, tracking of poultry, analysis of broiler chicken behavior, detection of poultry diseases, lameness and broiler weight, and stress monitoring. In addition, this review explores the most recent developments, difculties, strategies, and databases used in image preprocessing feature extraction and classifcation. Te review addresses these challenges and discusses the approaches and techniques researchers employ to tackle them in the feld of poultry management and disease detection.
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U2 - 10.1155/2024/8674250
DO - 10.1155/2024/8674250
M3 - Review article
AN - SCOPUS:85215289341
SN - 2090-7141
VL - 2024
JO - Journal of Computer Networks and Communications
JF - Journal of Computer Networks and Communications
M1 - 8674250
ER -