Cattle Disease Prediction Using Artificial Intelligence

Abstract

In today’s world identifying cattle disease and providing proper treatments is a challenging task in the current medical sector. As it is difficult to identify thecattle disease in real time, we require a method topredict cattle disease and related patterns. There are so many research works on this topic. Most of the researchworks just presented the idea of cattle disease prediction. There are many works where implementation is done and many papers predicts cattle disease using efficient data science algorithms. Research works where implementation is done uses PYTHON language or R language as programming language for cattle disease prediction. As PYTHON language and R language supports all ready libraries to process training datasets and to predict cattle disease. Many papers use training datasets from www.kaggle.com, www.dataworld.com etc.Research works uses efficient algorithms for prediction, algorithms such as Naive Bayes algorithm, KNNclassifier, SVM classifier, Decision Tree classifier, Random Forest algorithm etc. Most of the papers got very good results of using these algorithms. So many works on this cattle disease and pattern prediction is done using data science techniques.

Smart livestock management: integrating IoT for cattle health diagnosis and disease prediction through machine learning

“…Nonlinear algorithms fared better than linear ones, illuminating the effects of stressors. Rao et al [3] in the meantime, presented a Python/R-based real-time cattle disease detection system that makes use of RF, Naïve Bayes, k-nearest neighbor (K-NN), support vector machine (SVM), and decision tree. It seeks to transform cow healthcare by bridging the comprehension gap in animal discomfort by linking symptoms to remedies.…”

Artificial Intelligence and its Application in the Prediction and Diagnosis of Animal Diseases: A Review

“…Many papers rely on training datasets sourced from platforms such as Kaggle and Data World. These research works utilize efficient prediction algorithms, including Random Forest, Decision Tree, SVM classifier, KNN classifier and Naïve Bayes algorithm to achieve accurate results (Rao et al, 2023).…”