Advances in Artificial Intelligence for Multi-Class Detection of Cattle Skin Diseases: Data Constraints and Real-World Applications

Abstract

Lumpy skin disease, as well as dermatophytosis, papillomatosis, and mange, are cattle skin diseases that
are very problematic to the livestock health, livestock productivity, and the economy of the cattle farm
all over the world. These diseases are also not easily diagnosed early due to the visual similarity of many
dermatological conditions, and the traditional methods of diagnosis often involve lab tests and expert
knowledge of the veterinarian, which cannot be easily obtained by rural farmers. Over the past few years,
the use of artificial intelligence (AI) and machine learning (ML) methods has become one of the possible
solutions to automated livestock disease diagnostics, especially the use of image-based diagnostic systems.
The review examines the present developments in AI-based approaches to detecting cattle skin diseases,
especially deep learning models, ML classification approaches, and predictive ML models applied to
the detection of livestock health status. The paper also analyses the clinical presentation of significant
cattle skin diseases and assesses the weaknesses of the traditional diagnostic techniques. Moreover, the
review outlines several important issues that influence the advancement and implementation of AI-based
detection systems, such as insufficient availability of data, the problem of model generalizability, and real-
life limitations of farms. The results indicate the increasing potential of AI technologies in enhancing the
process of disease detection and livestock management, and the necessity of strong multi-classification
models, standard datasets, and effective deployment schemes. According to these issues, it will be necessary
to handle these challenges to create trustworthy AI-based diagnostic tools that can provide stable livestock
farming and improve the early disease detection rates in cattle herds.