A Model for Early Detection of Lumpy Skin Disease in Cattle Using Ensemble Technique | [Pakistan Journal of Zoology • 2025]

Author(s):

1. Saqib Ali Khaskheli: Information Technology Centre, Sindh Agriculture University, Tandojam

2. Muhammad Yaqoob Koondhar: Information Technology Centre, Sindh Agriculture University, Tandojam

3. Zulfikar Ahmed Maher: Information Technology Centre, Sindh Agriculture University, Tandojam

4. Gul Bahar Khaskheli: Information Technology Centre, Sindh Agriculture University, Tandojam

5. Azhar Ali Khaskheli: Department of Computer Science, Virtual University of Pakistan

Abstract:

Lumpy skin disease in cattle is endemic disease showing a threat to overall livestock industries. In the past several traditional practices have been used to analyse these types of diseases such as poly chain reaction (PCR), clinical practices, laser, photonics technologies etc. Although these types of technologies were very eficient and efective but have some flaws such as they are expensive, vey time consuming, costly for large area farms and demand continuous human observation and engagement. The present study was planned to overcome these flaws by introducing a new method by designing a model for earlier detection of lumpy skin disease in cattle using ensemble techniques. The model was trained and tested with dataset, which detects the lumpy skin disease. The dataset was collected from 3 diferent districts of Sindh province (Tando Muhammad Khan, Tando Allahyar and Matiari) and consisted of 500 images among which 75% was used for training purpose while remaining 25% for testing. The collected data was further processed with image pre-processing techniques, to enhance the quality of images and to detect the region of interest. The model categorized the cattle into “normal”, “high” and “severe” stages based on their physical conditions and temperature value. The experimental result showed that, the ensemble technique achieved around 86% accuracy.

Page(s): 1797-1801

DOI: 10.17582/journal.pjz/20230810102300

Published: Journal: Pakistan Journal of Zoology, Volume: 57, Issue: 4, Year: 2025

Keywords:

Random Forest , Machine learning , Logistic Regression , Image Processing , Support Vector Machine , Decision Tree , eolgtLumpy skin disease

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