Operational evaluation of an optical sensor for the automatic in-line estimation of total mixed ration fibre length and particle size in a mixing wagon

The optimal management of cattle nutrition promotes animal health and welfare, increases livestock farms’ productivity and competitiveness, and enhances environmental sustainability practices. Animal feeding operations play a crucial role as many factors can drive the theoretical ration formulated by nutritionists far from the one the animals ingest. Precision feeding technologies (e.g., NIR sensors on the milling cutter of the chopper-mixer wagon; computer vision systems installed in the mixing tank) may allow for accurate and real-time analysis of the chemical and physical properties of total mixed ration (TMR) ingredients, reducing errors during its preparation and distribution. This work compares the physical quality and the length of the fibre of the TMR resulting from the chopping-mixing process of a conventional mixing wagon, one machine-learning-assisted mixing wagon and an automatic feeding system under actual operating conditions. Between October 2021 and November 2022, TMR sampling occurred on four dairy farms and one fattening bulls farm in Northern Italy, specifically in the Brescia, Cremona, and Mantua districts. TMR samples underwent particle size analysis using the Penn State Particle Separator (PSPS) method and, once in the laboratory, moisture analysis and fibre length measurement. Concerning TMR particle size analysis, the PSPS method revealed that the machine learning-assisted mixing wagon provided TMR with physical features comparable to that from ordinarily run mixing wagons. At the same time, the automatic feeding system resulted in TMR with finer particle size, following the farmers’ choice not to use long-stemmed forages. Regarding fibre length, only the TMR resulting from the operator-based mixing wagon aligned with the targeted fibre length of 5 cm, while the AFS and the ML-assisted mixing resulted in higher fibre lengths. Overall, the use of computer vision (CV) systems is helpful for the consistency of the TMR and represents a valuable solution for animal farming, particularly when employing low- or inexperienced operators. Further studies are, however, needed to improve the training of the with elements that can replicate the operator experience.