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Lumpy skin disease diagnosis in cattle: A deep learning approach optimized with RMSProp and MobileNetV2
Sheikh Muhammad Saqib1, Muhammad Iqbal1, Mohamed Tahar Ben Othman2
1Institute of Computing and Information Technology, Gomal University, Dera Ismail Khan, Pakistan.
Plos One
|August 5, 2024
Summary
A new deep learning method accurately detects lumpy skin disease (LSD) in cattle using MobileNetV2. This approach achieves 95% accuracy, offering a significant advancement for livestock health management and disease control.
Area of Science:
- Veterinary Medicine
- Computer Science
- Artificial Intelligence
Background:
- Lumpy skin disease (LSD) poses a significant threat to cattle health and productivity worldwide.
- Effective disease management is crucial to prevent substantial economic losses in the livestock industry.
Purpose of the Study:
- To develop and evaluate a deep learning model for accurate detection of lumpy skin disease in cattle.
- To assess the performance of the MobileNetV2 model with the RMSprop optimizer for LSD diagnosis.
Main Methods:
- A deep learning approach was implemented using the MobileNetV2 architecture.
- The RMSprop optimizer was utilized to train the model on a dataset of cattle images.
- The model was tested on images of healthy and LSD-affected cattle.
Main Results:
- The proposed deep learning model achieved an accuracy of 95% in identifying lumpy skin disease.
- The system demonstrated superior performance compared to existing benchmarks, with a 4-10% improvement.
- The results indicate high efficacy in distinguishing between healthy and diseased cattle.
Conclusions:
- The MobileNetV2-based deep learning approach shows significant potential for revolutionizing cattle skin disease diagnosis.
- This methodology offers a promising tool for enhancing disease management strategies in cattle farming.
- The study highlights the value of AI in improving livestock health surveillance and control.
