Machine learning classification of breeding protocol descriptions from Canadian Holsteins

Abstract

Dairy farmers are motivated to ensure cows become pregnant in an optimal and timely manner. Although timed artificial insemination (TAI) is a successful management tool in dairy cattle, it masks an animal's innate fertility performance, likely reducing the accuracy of genetic evaluations for fertility traits. Therefore, separating fertility traits based on the recorded management technique involved in the breeding process or adding the breeding protocol as an effect to the model can be viable approaches to address the potential bias caused by such management decisions. Nevertheless, there is a lack of specificity and uniformity in the recording of breeding protocol descriptions by dairy farmers. Therefore, this study investigated the use of 8 supervised machine learning algorithms to classify 1,835 unique breeding protocol descriptions from 981 herds into the following 2 classes: TAI or other than TAI. Our results showed that models that used a stacking classifier algorithm had the highest Matthews correlation coefficient (0.94 ± 0.04, mean ± SD) and maximized precision and recall (F1-score = 0.96 ± 0.03) on test data. Nonetheless, their F1-scores on test data were not different from 5 out of the other 7 algorithms considered. Altogether, results presented herein suggest machine learning algorithms can be used to produce robust models that correctly identify TAI protocols from dairy cattle breeding records, thus opening the opportunity for unbiased genetic evaluation of animals based on their natural fertility.

A Combination of OBIA and Random Forest Based on Visible UAV Remote Sensing for Accurately Extracted Information about Weeds in Areas with Different Weed Densities in Farmland

“…Here, metrics such as OA (presented in Equation ( 1)) and the F1-score (presented in Equation ( 2)) are used to evaluate the classification. The OA is the ratio of test points correctly classified to all test points, and the F1-score can be viewed as a weighted average of the precision and recall of the model, which takes into account both the precision and recall of the classification model [59,60]. By comparing the real category of the samples with the model's predicted results, the results can be classified into the following four cases: true positive (TP), where the predicted value of weeds is consistent with the real value; false positive (FP), where the actual situation is the non-weeds but is incorrectly predicted as weeds; false negative (FN), where the predicted value of non-weeds is consistent with the real value; true negative (TN), where the actual situation is the weeds but is incorrectly predicted as non-weeds.…”

Recording of calf diseases for potential use in breeding programs: a case study on calf respiratory illness and diarrhea

“…However, this flexibility can lead to different codes indicating the same condition or protocols (Kelton et al 1998;Wenz and Giebel 2012;Lynch et al 2021). Awareness that these different codes are used in on-farm recording is important as this may require machine learning methods to combine codes or may otherwise influence genetic evaluations as demon-strated for fertility traits (Lynch et al 2021;Alcantara et al 2022). Gonzalez-Peña et al (2019) similarly reported the need to combine "RESP" and "PNEU" records for genetic evaluations of calf wellness traits in the United States.…”