پایش سلامت دام با مدل‌سازی رفتار نشخوار بر اساس اطلاعات مبتنی بر شتاب‌سنج

تعاملات رفتاری حیوان با محیط باید به‌طور مداوم نظارت گردد تا مدیریت دقیق با موفقیت اجرا شوند. نشخوار نوعی رفتار دام است که از آن به‌عنوان شاخص سلامت و آسایش دام استفاده می‌شود. کاهش نشخوار دام نشانگر تغییراتی در بدن حیوان است. پایش فرآیند نشخوار کمک می‌‌کند تا بیماری‌هایی مثل کتوز، ورم پستان و غیره را پیش از بروز علائم تشخیص داده و سامانه‌های مدیریت دقیق، امکان نظارت و مدیریت هر دام را به‌صورت مجزا فراهم می‌کنند. با این روش می‌توان نشخوار گاو را پیوسته اندازه‌گیری کرده و از بروز مشکل مطلع شد. مولفه اصلی این سامانه‌ها، مدل‌های تحلیلی هستند که به‌طور خودکار داده‌های حسگر را به دسته‌های مختلف رفتاری تبدیل می‌کنند. هدف از این مطالعه، تفکیک رفتار نشخوار گاو با استفاده از تحلیل داده‌های به‌دست‌آمده از حسگر طراحی‌شده در آزمایشگاه کشاورزی دقیق دانشگاه تبریز و بررسی اثر موقعیت‌های مختلف نصب بود. الگوریتم رگرسیون لجستیک برای آموزش و توسعه مدل طبقه‌بند در نظر گرفته شد. پس از ارزیابی فاکتورهای مختلف و مدل‌سازی مربوطه، نهایتا 4 فاکتور بهینه از آرایه‌های 50تایی انتخاب شدند. ارزیابی مدل طبقه‌بند نهایی با به‌کارگیری شاخص‌های حساسیت، صحت و f-score به‌ترتیب مقادیر 0.88، 0.94 و 0.91 را نتیجه داد که نشان از دقت بالای مدل بود. پس از تعیین مدل نهایی، ارزیابی میدانی سامانه با اعمال مدل در حسگر طی سه روز انجام شد. نتایج حاصل از آزمون نهایی مدل در مقایسه با مشاهدات بصری نشان از توانایی تشخیص 89.47% و سازگاری بالای برآورد سامانه با مشاهدات میدانی بود.

monitoring livestock health by modeling rumination behavior according to accelerometer-based information

introductionthe livestock sector excels in the production of dairy and meat products. these products, serving as vital sources of animal protein, hold a significant position in household diets. the significance of these two products in the food basket has heightened awareness around animal health. regularly tracking rumination time serves as a vital and insightful measure to obtain information about the rest and overall health of an animal. this information enables prompt intervention for health or nutritional issues, allowing for earlier management adjustments and veterinary care to effectively combat the onset of disease. in the past, rumination was usually monitored through visual observation by on-site staff or through videos recorded by cameras installed on the livestock. nowadays, the growing scale of livestock farms makes it impractical to effectively monitor the animals individually. the traditional method of visual observation demands the continuous presence of livestock professionals and is extremely time-consuming. currently, sensors and digital technologies have become important tools for accurate animal husbandry, enabling real-time monitoring of rumination. a review of the research in the field of precision animal husbandry shows that many efforts are being made to develop precision monitoring sensors to overcome the mentioned problems. continuous and automatic monitoring of animal behavior through sensors can offer valuable insights into nutrition, reproduction, health, and overall well-being of dairy cows.materials and methodsin this research, an accelerometer-based sensor was developed and used in the precision agriculture laboratory of tabriz university, iran. the sensor was installed in three different positions on the cow’s body to collect data. important factors were calculated from the raw data, and the modeling was done using the logistic regression method. the logistic regression model was trained to distinguish rumination from the other cow’s behaviors. the developed model was evaluated using the receiver operating characteristic (roc) curve, and three other evaluation criteria: precision, sensitivity, and f-score. finally, the performance of the final model and sensor was evaluated in the field for a few days.results and discussionafter calculating the evaluation criteria for different calculation factors, four optimal factors were finally selected from the 50 arrays. muzzle mode was found to be the best place to install the sensor. logistic regression was the best modeling method for binary classification between rumination and other behaviors. the evaluation criteria of the model in the proposed sensor are the highest, and the values of sensitivity 88%, accuracy 94%, and f-score 91% were obtained through logistic regression analysis. the final test results of the model revealed that the sensor demonstrated an impressive detection capability of 89.47%. furthermore, the developed system exhibited strong alignment with the actual field observations, highlighting its effectiveness and reliability. finally, the results of the current study were compared with other studies in the literature.conclusionthis study investigated recording and monitoring rumination behavior using an accelerometer, which can help prevent financial losses in cattle farms. after examining different mounting locations of the sensor, it was found that the muzzle position provided more accurate detections than the other mounting locations. the final model was created using the statistical factors and the calculation of the evaluation criteria. the results showed that the proposed model provided more correct diagnoses and achieved the optimal solution.acknowledgmentwe would like to express our gratitude to the khalat poushan cattle farming complex of the university of tabriz, iran, its professors and staff for supporting this project, and for their commitment to promote animal husbandry science.