A deep learning-based cow behavior recognition scheme for improving cattle behavior modeling in smart farming

Farming and animal husbandry applications are improvised with the implication of machine learning and artificial intelligence in recent years. The precise estimation, recommendations, and performances are the prime reason for the technology implication. Owing to the modern agricultural and animal cultures, this article introduces an innovative Behavior Recognition and Computation Scheme (BRCS) for predicting cow behaviors. The information from the swallowed microchip is processed based on the observed animal action that is used for the forecast. Considering the information to be rectilinear, the distractions and distribution patterns (data) are augmented in identifying and forecasting its behavior. The proposed scheme identifies the patterns using a deep recurrent learning paradigm recurrently. This pattern is distinguished for idle and non-idle observations for improving the prediction accuracy. Distinguished data patterns are mapped for the consecutive time and observation data in classifying abnormalities. The proposed scheme's performance is validated using the metrics accuracy, precision, computing time, and mean error.