Body weight prediction of crossbred beef cattle through the image processing and machine learning algorithms

Abstract

For the beef cattle system, one of the most valuable information is the body weight that can be linked to animal growth and performance. The bidimensional sensors area is the cheapest technology among all sensors used as a tool to extract information that can be applied to machine learning to predicts value phenotype. This study aimed to predict body weight using video image analysis with simple bidimensional equipment, from the dorsal view of crossbreed beef cattle (1⁄2 Angus x 1⁄2 Nellore) in a finishing system, applying different frame information and machine learning algorithms. The experimental procedures were performed at Federal University of Viçosa. A total of 40 crossbreed steers (1⁄2 Angus x 1⁄2 Nellore) were used, averaging 8 months of age at the beginning of the feedlot trial, and 291.7±23.8 kg and 517.42±54.8kg of initial and final body weight, respectively. The data collection occurred from September (12 Month) to December/2021 (15 month). Body weight (BW) was collected using an automatic Intergado company drink fountain/scale and the video images were collected using cameras Intelbras from the animals’ dorsal view. Three approaches were tried for segmentation of the animals’ dorsal images, however, their color characteristics did not allow do this automatically, so were used ImageJ software to manually do the delimitations, extracting 8 Shape Descriptors. For regression were used 6 machine learning algorithms, Ridge, Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (ENET), Multiple Linear Regression (MLR), Adaboost (ADAB) and Random Forest (RF) to construct predictive models, the dataset was split in 70:30 for training and test. The regularizations RIDGE and MLR without AGE as a predictor had similar performance. The AGE addition improved all algorithms, the best metrics results were for ENET and ENET using AGE for a dataset with 5 Frames information (5F) R2=0.68 and 0.76, respectively. Thus, the use of bidimensional sensor in the dorsal view can predict the BW of crossbreed (1⁄2 Angus x 1⁄2 Nellore). Keywords: Correlation. Image Processing Regularization