Classification of Foot and Mouth Diseases (FMD) in Cattle using DenseNet-CBAM
DOI:
https://doi.org/10.56480/jln.v6i1.1641
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Keywords:
Cattle, CBAM, DenseNet, Diseases, FMDAbstract
Foot-and-mouth disease (FMD) poses a serious threat to Indonesia's livestock sector as it impacts the productivity and distribution of livestock, particularly cattle. Early detection of FMD symptoms still relies on visual methods that are inaccurate and time-consuming. This study aims to develop an automatic classification system based on the DenseNet-CBAM model to detect FMD symptoms in cattle through digital images. The dataset used was obtained from the Bojonegoro District Livestock and Fisheries Service, consisting of 180 images, and expanded through augmentation to 2,000 images. The preprocessing process included determining the region of interest (ROI), augmentation, data splitting, and resizing the images to 150x150 pixels. The model architecture combines DenseNet169 with the Convolutional Block Attention Module (CBAM) to enhance the model's focus on important spatial features. Model evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The best results were obtained with a data split configuration of 70:15:15, a batch size of 16, and 50 epochs, achieving an accuracy of 94% and average precision, recall, and f1-score values of 0.94. This study demonstrates that the combination of DenseNet and CBAM is effective for the automatic early detection of PMK.
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