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Abstract

Abstract

Agriculture and animal husbandry play an important role in the way of life around the world, highlighting the
importance of timely diagnosis and treatment. Development of disease detection system in cows using deep learning
represents a significant milestone in veterinary medicine and livestock management. By leveraging advanced deep
learning algorithms, this system has demonstrated remarkable capabilities in accurately identifying and classifying diseases in cows based on visual symptoms extracted from images. As the existing systems have shown promising results, there are several avenues for future enhancements that can further elevate its effectiveness and impact. Firstly, expanding the dataset to include wider range of diseases prevalent in cows can enhance the system’s ability to detect and classify broader spectrum of health issues. Additionally including multi modal data sources such as genetic, environmental and behavioral data can provide more comprehensive insights into animal health and improve disease prediction accuracy. Implementing real-time monitoring capabilities and alerts can enable immediate detection of disease outbreaks, facilitating proactive intervention by farmers and veterinarians. Continuous model training using incoming data can ensure that the system remains adaptive to evolving disease patterns. Early detection is crucial for all living beings, the urgency in animal health surpasses even that of human welfare. Convolutional neural networks (CNNs) are known for their ability to extract important features from images and provide a promising approach to computer vision field. This paper presents a novel method based on CNN to predict cattle diseases by analyzing and classifying input images. Using a specially designed CNN, the system increases disease identification accuracy. In addition, after the disease is detected, the system can facilitate treatment by providing users with detailed information about nearby doctors and hospitals. With an accuracy rate of 83%, the system represents a step forward in veterinary medicine and disease prevention and detection.

Figures

Figure 1
Figure 1: Proposed disease detection system.
Figure 2
Figure 2: Cow images.
Figure 3
Figure 3: Data distribution
Figure 4
Figure 4: Accuracy graph.
Figure 5
Figure 5: Loss graph.
Figure 6
Figure 6: Login page.
Figure 7
Figure 7: Registration page
Figure 8
Figure 8: Upload image interface
Figure 9
Figure 9: Upload image interface
Figure 10a
Figure 10a: Detection of lumphy.
Figure 10b
Figure 10b: Normal cow skin.

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