IMAGE BASED BREED RECOGNITION FOR CATTLE IN INDIA USING DEEP LEARNING

This project presented an AI-based image recognition system for identifying cattle breeds in India using deep learning techniques. The proposed system integrates image preprocessing, Flask-based web application technology, and the MobileNetV2 model to analyze cattle images and predict the corresponding breed. This approach helps reduce dependency on manual breed identification and provides a faster and more convenient solution for farmers, researchers, and livestock professionals. The system was implemented with an image preprocessing module that resizes uploaded images to 224 × 224 pixels and prepares them for classification. The trained MobileNetV2 model extracts important visual features and generates probability scores for different cattle breeds. The breed with the highest probability is selected as the final prediction, along with a confidence score and relevant breed information. One of the key advantages of the proposed system is its ability to operate in offline mode. The trained model is stored locally, allowing cattle breed prediction without requiring an active internet connection. This makes the system particularly useful in rural and remote agricultural areas where network connectivity may be limited. The offline mode also reduces network-related delays and keeps the image processing and prediction within the local system. The experimental output demonstrates that the system can successfully identify the predicted breed and display the breed name, confidence score, uploaded image, and breed information through the application interface. The integration of image processing and deep learning provides an efficient approach for automated cattle breed recognition. Despite its advantages, the system has certain limitations. The prediction performance may be influenced by image quality, lighting conditions, background variations, and the diversity of the training dataset. The current model is also limited to the cattle breeds included in the trained dataset and may require additional training to recognize a wider range of breeds. Future work can focus on improving the system by using larger and more diverse cattle image datasets and advanced deep learning techniques to improve classification accuracy. Additional breed information, image-based feature analysis, and improved confidence estimation can also be incorporated. The system can further be enhanced with mobile application support, local result storage, and expanded offline functionality. In conclusion, the proposed system provides a cost-effective, efficient, and accessible solution for automated cattle breed recognition. Its ability to perform predictions in offline mode, combined with deep learning-based image analysis, makes it suitable for practical applications in livestock management, particularly in areas with limited internet connectivity.

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