Enhancing LSD Image Classification Techniques A Literature Review on Classification Techniques
Abstract
With an emphasis on lumpy skin disease (LSD) in cattle and other skin conditions, this paper provides an extensive literature review on current developments in disease detection approaches for livestock. Through the use of ensemble models, hybrid systems, preprocessing approaches, feature ex- traction strategies, and Convolutional Neural Networks (CNNs), researchers have significantly increased the efficiency and accu- racy of diagnostic procedures. The review summarizes the results of several studies, stressing the advantages and disadvantages of various strategies and offering information on how well they work in agricultural contexts. Integration of domain expertise with im- age analysis, employing ensemble models to improve robustness, and investigating pretreatment methods for data improvement are some of the important subjects covered. This research adds to the ongoing efforts to protect animal welfare and agricultural productivity while revolutionizing disease detection procedures by analyzing the most recent advancements in livestock health monitoring.
Keywords:
Hybrid Systems, Preprocessing ApproachesPublished
Issue
Section
License
Copyright (c) 2024 International Journal on Emerging Research Areas

This work is licensed under a Creative Commons Attribution 4.0 International License.
All published work in this journal is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
How to Cite
Similar Articles
- Asha Joseph, Deep Learning for Cyber Threat Detection , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
You may also start an advanced similarity search for this article.