Обзор исследований и технологий, применимых для цифровизации процесса оценки экстерьера животных в мясном и молочном животноводстве

Для повышения эффективности животноводства ученые разрабатывают информационные и коммуникационные технологии, направленные на цифровизацию процесса оценки экстерьера животных. Этот обзор должен улучшить понимание этапов разработки систем, применимых в цифровизации процесса оценки экстерьера животных и использующих машинное зрение и нейросети глубокого обучения. Поиск был сосредоточен на нескольких вопросах: системы машинного зрения; обучающие наборы данных; системы сбора изображений; модели глубокого обучения; нейросети для обучения; параметры производительности и оценки систем. Машинное зрение является инновационным решением благодаря сочетанию датчиков и нейросетей, реализуя бесконтактный способ оценки условий содержания скота, поскольку камеры могут заменить наблюдения человеком. Используются два подхода к получению трехмерных изображений для задач цифровизации в животноводстве: съемка животных с помощью одной 3D-камеры, закрепленной в одном месте, и съемка с разных точек с использованием нескольких 3D-камер, которые фиксируют изображения животных и отдельные части их тел, таких как вымя. Особенности, извлекаемые из изображений, называемые дорсальными чертами, используются в качестве входных данных для моделей. В изученных публикациях использовались различные модели глубокого обучения, включая CNN, DNN, R-CNN и SSD в зависимости от задачи. Аналогично и нейросети, такие как EfficientNet, ShapeNet, DeepLabCut и RefineDet, были использованы для мониторинга здоровья животных, тогда как GoogleNet, AlexNet, NasNet, CapsNet, LeNet и ERFNet в основном используются для целей идентификации.

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