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 45
- Downloads 0
Impact Assessment of Monsoon Precipitation on Groundwater Level in Lahore DistrictGEE Script
Introduction/Importance of Study: Precipitation is a crucial component of the global water cycle and a primary source of freshwater, with groundwater being vital for drinking water worldwide, especially in regions like...
Developing an Arabic-Urdu Ontology of Quranic Concepts: A Semantic Approach
An Arabic-Urdu ontology system dedicated to Quranic concepts represents a necessity for protecting the semantic value and making religious texts more accessible during Quranic study. Ontology-driven annotation tools sh...
Clinical Prediction of Female Infertility Through Advanced Machine Learning Techniques
Infertility in females implies failure by such women to conceive even after having at least one year of intercourse without using any contraceptives. Infertility can be caused by a variety of factors, including ovulati...
IoTin Developing the Smart Farming and Agricultural Technologies
Background: The Internet of Things (IoT) is streamlining processes in food and agriculture, especially in developing countries with agriculture-based economies. These countries stand to gain a lot from the IoT innovati...
Exploring Character-Based Stylometry Features Using Machine Learning for Intrinsic Plagiarism Detection in Urdu
Plagiarism detection in natural language processing (NLP) plays a crucial role in maintaining textual integrity across various domains, particularly for low-resource languages like Urdu. This study addresses the emergi...