An Intelligent System for Sheep Disease Diagnosis and Treatment Using Knowledge-Based Techniques

—The developing interest in a productive and precise analysis of sheep sicknesses has prompted the improvement of an expert system to help veterinarians and ranchers pursue informed choices. This paper presents the plan and execution of a custommade expert system for the knowledge-based determination and treatment of sheep infections. The system uses a standard-based approach, coordinating expert information as legitimate predicates and induction rules, to analyze regular infections influencing sheep and suggest fitting medicines. The system's knowledge base is built from information about the domain, like signs, symptoms, and environmental factors. This system aims to support proactive health management in sheep farming, reduce the need for immediate veterinary intervention, and improve disease diagnosis efficiency by simulating expert decision-making processes. The execution in Prolog exhibits the attainability and flexibility of the framework in authentic situations. The users evaluated the system with an accuracy rate of diagnosis of 89%, farmer feedback with an 87% success rate in treatment recommendation, and 91% matched with the human experts in identifying common sheep diseases. These advancements have the potential to completely transform the management of sheep's health, thereby enhancing the welfare and global agricultural productivity. Future systems may incorporate machine learning models to enhance rule-based techniques and increase diagnostic precision and flexibility. Through the examination of past data and the identification of trends, these systems have the potential to forecast disease epidemics or offer more accurate diagnoses. Mobile platforms and IoT technologies could make real-time livestock health monitoring possible, relieving farmers of manually entering data. Wearable technology could monitor physiological parameters, and environmental sensors could offer more information about the weather, temperature, and sanitization. Future systems may combine sensor-based analysis and image recognition to reduce reliance on human observation to identify visible disease signs, such as lesions or aberrant behavior