COWS LUMPY VIRUS DETECTION USING DEEP LEARNING

COWS LUMPY VIRUS DETECTION USING DEEP LEARNING

1 Dr. SHANKARA GOWDA B B , 2 MARUTHI B M

[1] Associate Professor and HOD, Department of MCA, BIET, Davangere

[2] Student, Department of MCA, BIET, Davangere

ABSTRACT
Animal illness is now a widespread problem. sickness identification is essential because there are various sorts of sickness in creatures, and the opinion will be delivered in a timely manner. Cows with the Neethling infection develop lumpy skin complaints. The affection of these illnesses causes lasting harm to the cattle's skin. Reduced milk production, gravidity, poor growth, revocation, and, in severe cases, mortality, are the most common effects of the illness. We developed a deep learning-based architecture that can predict or detect disease. To discover the pathogen that causes lumpy skin problem, it is crucial to employ a deep literacy system. DenseNet- 121 is an efficient system for reading prints that is based on deep literacy.

This study shows that convolutional neural networks are capable of predicting LSDV in animals using photos and images alone. Images were divided into the LSDV and Non-LSDV classes using the specified deep learning model. Because there is currently no LSDV vaccination that can treat rather than control the virus, early and accurate viral identification can be an implicit mechanism for identifying and stopping the spread of the infection.(For instance, by dividing the Animals).

Keywords: Beast Lumpy Skin Complaint, Deep Literacy, Convolutional Neural Network, Densenet- 121, Image Processing, Transfer Learning.