Mobile Based Expert System for Diagnosis of Cattle Skin Diseases With Image Processing Techniques

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Addis Ababa University


Cattle population is critical socioeconomic assets in a nation like Ethiopia where the society depends on farming and animal husbandry. However, there is huge loss of livestock population by a disease that undermines the efforts towards achieving food security and poverty reduction. Many expert systems have been developed for the diagnosis of cattle disease. The diagnosis starts by collecting information about symptoms, signs and other related issues. In most of them, this information is obtained from the person using text dialogue. Every person has different ways of expressing the same thing, which results, in the inconsistency of description lead to an incorrect diagnosis. To address this problem, we propose an approach for cattle disease diagnosis by integrating image processing using deep learning with an expert system. The proposed system has an expert system and an image processing component. The symptom identified by naked eyes are represented using image and its category is identified by the image processing component. The image processing component consists training and classification phase. In the training phase images collected from different source are preprocessed and feed to the classification model. The classification model used is a convolutional neural network with three convolutional and two fully connected layers. In the classification phase the trained model is used to classify the input images. The expert system have reasoning, knowledgebase and user interface component. The user interface allows communication between the system and the user. The knowledgebase contains information and facts required for diagnosis. The reasoning component reaches a final diagnosis conclusion based on classification results and other related information. The developed classification model trained on 3990 dataset collected from different sources. To increase the dataset we apply different augmentation techniques. We split the dataset into 90% for training and 10% for testing. The model classifies the input symptom image with 95 % accuracy. The entire system has been evaluated by veterinarians and people having cattle farming, the analysis shows that the system is effective to diagnosis cattle disease.



Cattle Disease Diagnosis, Expert System, Image Processing, Deep Learning, Convolutional Neural Network, Location Information