The Design and Identification of Animal Lumpy Skin Disease Using Image Processing and Machine Learning
Nigeria has the largest livestock population in Africa. However, the productivity of the sector in Nigeria has multifaceted constraints; Lumpy Skin Disease is one of the major factors. Lumpy Skin Disease is known as a major risk to cattle production and has substantial impacts on livelihoods and food security especially for our country. Currently, the detection of Lumpy Skin Disease in our country is assessed manually. However, manual evaluation takes a significant amount of time and requires a trained professional and experienced person. Therefore, technology is needed to prevent animal disease epidemics. Automated detection of Animal Lumpy Skin Disease has advantages over the manual technique. Detection of Lumpy Skin Disease in Cows is developed in the literature. But Animal Lumpy skin disease has different classifications based on its severity. There is a need to further identify the different stages of Lumpy skin disease to know to what extent the animal is affected by lumpy skin disease. In this study, the Lumpy skin disease detection model is constructed using Convolutional Neural Network (CNN) for feature extraction and SVM for classification. CNN is the state of the art for deep feature extraction, hence we used it for feature extraction. The model used to detect and classify animal Lumpy Skin Disease skin diseases into Severe, Mild, and Normal. The dataset is collected from Livestock production offices and from the internet’s external image repository. After collecting data, Image augmentation, Image Preprocessing, and Image Segmentation techniques are applied to enhance image quality and identify regions of interest. During image preprocessing, the image is resized to 200×200. Gaussian filtering is applied to remove noise and Histogram equalization to balance the intensity of the image. The adaptive thresholding segmentation method is used to identify regions of interest. Out of the total 1740 image dataset, 80% is used for training and 20% for testing. Experimental results show that the SVM classifier outperforms RF(Random Forest) and Softmax classifiers. Quantitatively, an overall accuracy of 95.7% is achieved by using SVM classifier; on the other hand, RF achieves 87. 4% and the Softmax classifier achieves an accuracy 94.8%. Noise in the image is a challenging task for properly detecting the region of interest and hence we recommend as a way forward to use advanced noise removal techniques to improve image quality for proper segmentation and Lumpy skin disease detection.
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