Treffer: An automated image quality assessment method for dairy applications by multi-threshold analysis with parallel computing and computer vision.

Most image recognition and object detection models fail to perform with more than 90% accuracy and precision due to a lack of automated processes in image quality assessment (IQA). An image-based dataset may often contain poor-quality images that are not always feasible to identify manually based on visual judgment. This study aimed to develop an automated IQA method using parallel computing and a computer vision approach with GPU integration to identify high-quality images of dairy cattle that can be used to develop a prediction model based on biometric traits in the future. The images were collected from a commercial dairy farm in Victoria, Australia. Four key features, namely, structural similarity index (SSI), image gradient (g), image entropy (E), and Laplacian (L) filter, were considered in developing the IQA model. The features were mathematically calculated by MATLAB from reference images, with mean values of those images considered as thresholds to meet the IQA criteria. In addition, the images were rated one to five using a star system, with high-quality images rated as five-star images. The final IQA model was able to produce highly accurate outcomes for both reference-based (internal) and reference-free (independent) validations. The internal validation produced accuracy = 0.99 and precision = 0.97, whereas the independent validation demonstrated accuracy = 0.95 and precision = 0.93. This study has investigated the development and validation of the IQA approach for a future cattle recognition model in dairy farms. [ABSTRACT FROM AUTHOR]

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