Machine Learning in Livestock Management: A Systematic Exploration of Techniques and Outcomes
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 4
Abstract
This Systematic Literature Review (SLR) examines the growing field of leveraging Machine Learning (ML) to improve livestock productivity. Through a meticulous analysis of peer-reviewed articles, the study categorizes research into key domains such as disease detection, feed optimization, and reproductive management. Various ML algorithms, including supervised, unsupervised, and reinforcement learning, are evaluated for their efficacy in enhancing herd health and management. The review also addresses the role of diverse data sources, such as sensor technologies and electronic health records, and discusses the socio-economic and ethical implications of ML adoption in livestock farming. Insights into scalability, economic viability, and future research directions contribute to a comprehensive understanding of the current background and pave the way for sustainable and technologically advanced livestock management practices. This review serves as a valuable resource for researchers, practitioners, and policymakers in shaping the future of precision agriculture in improving livestock productivity.
Authors and Affiliations
Muhammad Qasim1, Rabia Tehseen1, Muhammad Farrukh Khan, Shahan Yamin Siddiqui, Nusratullah Tauheed, Maham Mehr Awan
Keywords
Related Articles
- EP ID EP760586
- DOI -
- Views 16
- Downloads 0
Image Compression Exploration using Discrete Wavelets Transform Families and Level
This analysis paper is based on Discrete Wavelets Transform (DWT) for image compression using wavelets families and levels. The DW transforms the image or data into frequency components that match its resolution scale...
Enhancing Cardiovascular Disease Risk Prediction Using Resampling and Machine Learning
Cardiovascular Disease (CVD) remains a critical health concern around the globe, requiring precise risk prediction approaches for timely intervention. The primary motive of this study is to enhance CVD risk prediction...
AI-Powered Detection: Implementing Deep Learning for Breast Cancer Prediction
Breast cancer remains a critical global health issue, affecting millions of women worldwide. According to the World Health Organization (WHO), there were 2.3 million new cases and 685,000 deaths from breast cancer in 2...
Digital Twins and Engineering Education: Current Status
This paper presents a comprehensive review of the use of Digital twins in engineering education among various disciplines. A total of 83 research papers were analyzed, spanning the last decade from 2012 to 2022. Almost...
Design of a High Gain Dual Band Patch Antenna with T Slot Ground Structure for Millimeter Wave Communication Applications
This paper introduces a novel design approach for achieving high gain, dual-band operation, and enhanced bandwidth in a microstrip patch antenna tailored for 5G applications. The antenna operates at the millimeter-wave...