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تشخیص و دسته‌بندی تیپ گاو بر اساس انحراف انتهای ستون فقرات با استفاده از یادگیری ماشین | ||
| مهندسی بیوسیستم ایران | ||
| دوره 56، شماره 2، شهریور 1404، صفحه 51-67 اصل مقاله (2.3 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijbse.2025.398902.665603 | ||
| نویسندگان | ||
| محسن دانشمند وزیری1؛ عبداله ایمان مهر* 2؛ محسن حیدری سلطان آبادی3 | ||
| 1محقق، بخش تحقیقات فنی و مهندسی کشاورزی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان اصفهان، سازمان تحقیقات، آموزش و ترویج | ||
| 2استادیار پژوهش، بخش تحقیقات فنی و مهندسی کشاورزی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان اصفهان، سازمان تحقیقات، آموزش | ||
| 3دانشیار پژوهش، بخش تحقیقات فنی و مهندسی کشاورزی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان اصفهان، سازمان تحقیقات، آموزش | ||
| چکیده | ||
| در مزارع پرورش گاو شیری، عملیات یادگیری ماشینی می‌تواند بر اساس امتیازدهی وضعیت بدنی (BCS) با بهره‌گیری از ویژگی‌های استخراج‌شده از تصاویر برای شناسایی و طبقه‌بندی انواع گاوها مورد استفاده قرار گیرد. به‌طور خاص، الگوریتم‌های یادگیری ماشین می‌توانند انحنای ستون فقرات را اغلب با شناسایی نقاط کلیدی و برازش یک خط یا منحنی، تجزیه و تحلیل کنند تا بین نژادهای مختلف گاو تمایز قائل شوند و وضعیت آن را ارزیابی کنند. در این مطالعه، جهت تشخیص و طبقه‌بندی انواع تیپ گاو بر اساس وضعیت انحنای انتهای ستون فقرات (کفل) از مدل‌های یادگیری ماشین که در سال‌های اخیر به‌طور مکرر در علوم کامپیوتر مورد استفاده قرار گرفته‌اند، شامل SVM، KNN و CNN همراه با شبکه از پیش آموزش‌دیده مبتنی بر یادگیری عمیق Resnet50 به‌منظور افزایش موفقیت معماری‌ها استفاده شد. در هر یک از الگوریتم‌ها استخراج و ثبت و ادغام ویژگی‌های تصاویر برای تشخیص نوع تیپ گاوها انجام گرفت و در نهایت الگوریتم CNN از پیش آموزش‌دیده مبتنی بر یادگیری عمیق با بالاترین میزان دقت (93 درصد) توانست نوع تیپ گاو را درست تشخیص دهد. بنابراین می‌توان با تلفیق این سیستم پردازشی با مکانیزم تصویربرداری، امکان تشخیص و طبقه‌بندی گاوها را بر اساس انواع حالات و مشخصات بدنی در محیط‌های گاوداری در مدت زمان کوتاه‌تر، ساده‌تر و کاربرپسند فراهم ساخت. این رویکرد نیاز به استخراج دستی ویژگی‌های دام را حذف می‌کند، بکارگیری نیروی انسانی را کاهش می‌دهد و به‌دقت تشخیص بهبودیافته‌ای دست می‌یابد. | ||
| کلیدواژه‌ها | ||
| امتیازدهی وضعیت بدنی؛ انحنای کفل گاو؛ الگوریتم‌های یادگیری ماشین؛ شبکه عصبی اغتشاشی؛ یادگیری عمیق | ||
| عنوان مقاله [English] | ||
| Identifying and classifying cow types based on spinal end deviation using machine learning | ||
| نویسندگان [English] | ||
| mohsen daneshman vaziri1؛ Abdollah Imanmehr2؛ mohsen Heidarisoltanabadi3 | ||
| 1Researcher, Agricultural Engineering Research Department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan, Iran.. | ||
| 2Assistant professor, Agricultural Engineering Research Department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan, Iran. | ||
| 3Associated professor, Agricultural Engineering Research Department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan, Iran. | ||
| چکیده [English] | ||
| In dairy farms, machine learning operations can be used to identify and classify cow types based on body condition scoring (BCS) using features extracted from images. In particular, machine learning algorithms can analyze the curvature of the spine, often by identifying key points and fitting a line or curve, to distinguish between different breeds of cattle and assess their condition. In this study, machine learning models that have been frequently used in computer science in recent years, including SVM, KNN, and CNN, were used in conjunction with a pre-trained deep learning network Resnet50 to enhance the success of the architectures. In each of the algorithms, image features were extracted, registered, and merged to identify the type of cows, and finally, the pre-trained CNN algorithm based on deep learning was able to correctly identify the type of cow with the highest accuracy (93 percent). Therefore, by combining this processing system with the imaging mechanism, it is possible to identify and classify cows based on various states and physical characteristics in cattle environments in a shorter, simpler, and more user-friendly time. This approach eliminates the need for manual extraction of livestock features, reduces the use of human resources, and achieves improved recognition accuracy. | ||
| کلیدواژه‌ها [English] | ||
| Body condition scoring, cow rump curvature, machine learning algorithms, convolutional neural network, deep learning | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 25 تعداد دریافت فایل اصل مقاله: 24 |
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