Synergizing CNN and Random Forest for Accurate Cattle Disease Identification

The current research focuses on the essential undertaking of multiclass classification in the context of diagnosing skin disorders in cattle. It aims to provide a thorough assessment of a complete model. The introduction underscores the need of rapid illness diagnosis in veterinary practises. The aim of this study is to construct a robust model that can successfully identify a wide range of skin diseases in cattle with high accuracy. The precision values, which range from 88.12% to 97.57%, serve as indicators of the model's proficiency in accurately predicting distinct classes. The model's ability to make accurate predictions across several categories is highlighted by its overall accuracy of 91.56%, indicating a high level of competency and reliability. The research use a comprehensive dataset and incorporates sophisticated machine learning methods to improve the detection of diseases. The attainment of a high level of precision and accuracy plays a significant role in enhancing the dependability of the model in real-world scenarios, hence enabling timely intervention and treatment. The results indicate that the model that was created has potential for effective use in actual situations, offering a significant resource for veterinarians in the management and treatment of skin disorders in cattle. This research makes a valuable contribution to the continuous endeavours in using machine learning techniques to enhance cattle health and emphasises the potential for further breakthroughs in disease identification strategies.