• Kalash Lanjewar
• Dr. P. D. Khandait
• Sohit Patle
• Rajwanshi Meshram

The increasing prevalence of cow illnesses like Foot-and- disease of the mouth (FMD), along with the need to monitor healthy cows, poses significant challenges to livestock health and the agricultural economy. Traditional diagnostic methods are often costly, laborious and prone to human mistake, hence automated solutions are required. This study explores Convolutional Neural Networks (CNNs), a kind of deep learning, are used for classification. cow health conditions into three categories: healthy, LSD, and FMD. A diverse dataset of cow images was collected and preprocessed using resizing, data augmentation, normalisation, and resizing to improve model resilience. Several CNN designs, like as normalization, and data augmentation to enhance model robustness. Multiple CNN architectures, including DenseNet121, ResNet50V2, InceptionV3, The VGG16, VGG19, Xception, were assessed using ImageNet weights that had already been learnt. for Learning transfer. Fine-tuning involved max pooling, dense layers with ReLU activation, dropout layers for regularisation, and a The output layer of SoftMax for classification. The Adam optimizer, cross-entropy loss, and early stopping techniques were employed to avoid overfitting and maximise training. Accuracy, precision, recall, F1-score, and confusion matrices were used to evaluate the model's performance. Values indicate that CNN-based models effectively classify cow health conditions, distinguishing diseased cows from healthy ones, with transfer learning and data augmentation improving accuracy. The proposed system offers an efficient and scalable approach to automated cattle health monitoring, aiding early disease detection and better livestock management for farmers and veterinarians.

Convolutional Neural Networks (CNNs), DenseNet121, Image Processing, InceptionV3, ResNet50V2, VGG16, VGG19.

"ENHANCING CATTLE DEEP LEARNING-BASED DISEASE DETECTION AND CLASSIFICATION WITH STREAMLIT APPLICATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.h295-h302, March-2025, Available :http://www.jetir.org/papers/JETIR2503737.pdf

"ENHANCING CATTLE DEEP LEARNING-BASED DISEASE DETECTION AND CLASSIFICATION WITH STREAMLIT APPLICATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. pph295-h302, March-2025, Available at : http://www.jetir.org/papers/JETIR2503737.pdf