A comparative study of cow species classification using deep learning techniques

Abstract

Deep learning replicate human brain to help the system to solve complex problem like identifying object. In this researched based project, we used deep learning to identify cow species. To know something, human often depend on the technology like object classification. People of today’s generation use software like google lens to identify the unknown. Among all domestic animals, Cow is a the most common and useful around us. Based on their species, they are useful to different need. Cow provides meat, milk and etc. So, by this research we used deep learning on a data set for identification of cow species. The date set is created by collecting photos using mobile phone and as accurate as possible. There is total of seven species and around two thousands of raw data. We used python to resized the data set in zip file as a part of data preprocessing. I used ResNet50, ResNet152, DenseNet121, and DenseNet201 and compare them to get the most accuracy. Among them DenseNet201 perform max and gave the accuracy of 97.35. According to my background study, this result is maximum on cow spices of local area. My research will inspire the new coming researcher to work agriculture sector. The research finding will contribute in the future digital firming. Using a collection of photos, this study investigated the effectiveness of transfer learning approaches for the classification of seven different cow species. The goal of the study was to classify cow species with the best possible accuracy.