Validation of a Stereo Vision System to Estimate Total Mixed Ration Volume and Feeding Behavior of Dairy Cattle
Creators: name: Flinders, McKinley Noelle
Publisher: name: Purdue University Graduate School
Year: 2024
Subjects: classificationCode: 401412
subject: Precision engineering
classificationCode: 300302
subject: Animal management

description: Consistent intake and feeding behavior records collected on a per-cow basis are useful measures for optimization of feed efficiency, production, and ultimately, resource and economic sustainability of dairy operations. However, current methods for collection are often labor-intensive and impractical to maintain for both individual- and group-housed cows. Across the dairy industry, total mixed rations (TMR) are fed to promote balanced nutrient intake and satisfy evolving energy requirements. TMR intake is an extensively investigated phenotype of dairy cattle and is known to be highly variable due to both intrinsic and extrinsic determinants, which can include composition and palatability of offered TMR, intensity of environmental stressors, and biological aspects of the individual animal. Reductions in TMR intake negatively impact health and production; thus, industry demand has heightened for precise intake monitoring systems. Cyber-physical systems that employ cameras as a sensing device are proposed solutions to ambiguity in existing feeding strategies. Prior studies have demonstrated the efficacy of camera systems to monitor other phenotypes of dairy cattle including body condition, locomotion and gait, social interaction, and early detection of negative health events. In this study, an OAK-D PoE stereo vision camera system was employed to estimate volume of TMR and monitor feeding behavior in a dynamic barn environment. The system leveraged open-source Python software to measure relative depth in near real time and autonomously estimate the amount of TMR present in a feed bunk. Image data were processed to generate a point cloud for which volume of TMR was estimated at a rate of approximately 50 estimates/min. Two experiments were conducted in which mass, volume, and density of TMR, as well as feeding behavior (exclusive to Exp. 2) were manually recorded to be compared to volume estimates of TMR output by the camera system. In Exp. 1, diet type (high-density vs. low-density; HD and LD, respectively), lighting (10,000 Lm vs. existing barn lighting; on vs. off, respectively), and shape of offered TMR (undisturbed vs. simulated post-meal bout; no divot vs. divot, respectively) were assessed for impact on system accuracy across five intervals of known TMR volume. In Exp. 2, system volume estimates were evaluated over time when a cow was present and exhibiting normal feeding behavior. The system accurately estimated volume of TMR across evaluated conditions in Exp. 1, despite significance of the divot condition. As TMR disappeared over time in Exp. 2, system volume estimates decreased with a similar pattern. When the cow was removed and measured TMR volume was unchanged at 2 h collection timepoints in Exp. 2, system volume estimates also remained unchanged and consistent. Post-collection of replicates in Exp. 2, frequency and duration of meal bout events were estimated based on differences in volume when cows were eating. Estimated frequency and durations were similar to manually recorded data and indicated feasibility of behavioral monitoring as an opportunity for further system development. Prior studies have integrated machine learning approaches for refinement of camera monitoring systems and mitigation of reported environmental impact on accurate quantification of TMR volume. Further development of the current system through integration of machine learning applications will improve accuracy and industry applicability as an automated feed bunk management tool for collection of TMR intake and behavioral data on a per-cow basis.