A deep learning-based framework for identification of Sahiwal cow.

The discernible anatomical elements of cattle, commonly referred to as morphological characteristics or linear-type traits, play a pivotal role in the recognition of breed as well as the production estimation of cattle. This paper introduces a deep learning-based framework for the identification of Sahiwal cattle within herds by observing the morphological characteristics i.e., color and facial structure analysis. The proposed framework involves three distinct convolutional neural network (CNN) models: GoogleNet, InceptionV3, and ResNet50 for the identification of breeds out of Gir, Sahiwal and Tharparkar. The implementation of this framework is executed through MATLAB-2021, a robust computational tool. Furthermore, we curate an authentic dataset containing Sahiwal cow images sourced from the NCR North region of India, facilitating the comprehensive evaluation of our approach. Performance assessment encompasses diverse metrics, encompassing accuracy and confusion matrix analysis. This study contributes to the domain of livestock recognition, presenting a robust methodology forprecise Sahiwal cow identification, leveraging advanced deep learning techniques. [ABSTRACT FROM AUTHOR]