Predicting dairy cattle heat stress using machine learning techniques

Abstract

The objectives of the study were to use a heat stress scoring system to evaluate the severity of heat stress on dairy cows using different heat abatement techniques. The scoring system ranged from 1 to 4, where 1 = no heat stress; 2 = mild heat stress; 3 = severe heat stress; and 4 = moribund. The accuracy of the scoring system was then predicted using 3 machine learning techniques: logistic regression, Gaussian naïve Bayes, and random forest. To predict the accuracy of the scoring system, these techniques used factors including temperature-humidity index, respiration rate, lying time, lying bouts, total steps, drooling, open-mouth breathing, panting, location in shade or sprinklers, somatic cell score, reticulorumen temperature, hygiene body condition score, milk yield, and milk fat and protein percent. Three different treatments, namely, portable shade structure, portable polyvinyl chloride pipe sprinkler system, or control with no heat abatement, were considered, where each treatment was replicated 3 times with 3 second-trimester lactating cows. Results indicate that random forest outperformed the other 2 methods, with respect to both accuracy and precision, in predicting the sprinkler group's score. Both logistic regression and random forest were consistent in predicting scores for control, shade, and combined groups. The mean probability of predicting non-heat-stressed cows was highest for cows in the sprinkler group. Finally, the logistic regression method worked best for predicting heat-stressed cows in control, shade, and combined. The insights gained from these results could aid dairy producers to detect heat stress before it becomes severe, which could decrease the negative effects of heat stress, such as milk loss.

Performance of Holstein cows subjected to different cooling sessions during subtropical summer

“…The THI is normally used to summate the intensity of heat stress on dairy cows [ 14 ]. The high THI values (>84) showed that the cows were under moderate heat stress during the month of August and September.…”

“…The increased standing time for the CNT group suggested that the cows were benefitting from the cooling effects of the sprinklers. Whereas the 2CS increased in standing time suggested increase in exposing more body surface area for heat abatement to reduce heat load [ 14 , 22 ]. The decreased standing time in the 4CS consequently increased lying time could be associated with reduced heat load.…”

Rule Discovery in Milk Content towards Mastitis Diagnosis: Dealing with Farm Heterogeneity over Multiple Years through Classification Based on Associations

“…Management of dairy farms through machine learning-based analysis of milking features has received increased attention in recent years [4,6,8,[22][23][24][25][26][27][28]. The next step is globalisation of a machine-learning based expert system from milking parameters.…”

Recent Advances on Early Detection of Heat Strain in Dairy Cows Using Animal-Based Indicators: A Review

“…PS has been developed and refined as a numerical scale to grade the obvious respiratory dynamics and behaviors [59,88,123,124]. A recent study utilized a score consisting of RR and various panting characteristics to assess heat strain in grazing dairy cows [133]. This score is very similar to PS, using a 0 to 4 scale, where 0 represents that the cow is under no heat strain and 4 represents that the cow is moribund.…”