Streamlining the acquisition and analysis of digital images for the assessment of horn fly abundance.

Horn flies are one of the most prevalent ectoparasites on pasture raised cattle. Studies have shown reasonable evidence of a genetic basis for horn fly attraction in beef cattle. Current methods for assessing fly abundance-related phenotypes (e.g., subjective assessment and image-based counts) suffer from limited accuracy and logistic difficulties. Previous attempts to assess horn fly abundance using image-based methods were hindered by several factors including weather conditions, distance and angle between the image taker and the animal, and lighting during image acquisition. The aim of this study was to address these limitations through the development of practical guidance to optimize image acquisition and to develop an adaptive image analysis algorithm to enhance the accuracy of image-based assessment of fly abundance. Images of beef cattle on pasture over multiple horn fly seasons (late spring to early fall) were collected. For each animal, the best image (where the entire side of the animal was in view) was selected and cropped from the withers to the hooks and from the chest floor to the udder. For every image (n = 314), all the flies were manually counted using the ImageJ software. Several image enhancement and analysis techniques including adaptive thresholding, pixel region connection, and pixel classification were used. The images were then clustered into three quality classes based on quality metrics computed using several algorithms from the image processing toolbox in MATLAB (BRISQUE, NIQE, and PIQE). BRISQUE and NIQE use distortions of natural scene statistics to compute quality scores. PIQE uses image features and models of human perception to compute quality scores. Pearson correlation, mean squared error (MSE), and the coefficient of determination (R² ) were used to evaluate the concordance between the manual (ground truth) and the image predicted counts. Across the different scenarios, the Pearson correlation and R2 ranged between 0.21 to 0.67 and 0.04 and 0.45, respectively (Table 1). When the images were first clustered in quality classes, all the assessment metrics showed a substantial increase (Table 2). In fact, the Pearson correlation ranged between 0.47 to 0.80 using the NIQE. When the images were classified as low or medium quality by BRISQUE, the correlation was greater (0.74 to 0.79) compared with NIQE (0.47 to 0.70) and PIQE (0.69 to 0.74). Similar patterns were observed for the other assessment criteria (MSE and R2 ). These results seem to indicate that NIQE, BRISQUE, or a combination of the two algorithms could be used to assess image quality and determine the appropriate combination of image parameters to maximize the predictive ability. The NIQE method could be used as a filter to detect low quality images that are unsuitable for counting and then BRISQUE could be used on the remaining images. [ABSTRACT FROM AUTHOR]