Zahid Ur Rahman, Mohd Shahrimie Mohd Asaari, Haidi Ibrahim
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引用次数: 0
Abstract
Deep learning has revolutionized animal farming by enabling automated health monitoring, behavior analysis, and livestock management. This review examines the application of key deep learning architectures, including convolutional neural networks (CNNs), You Only Look Once (YOLO), memory-based neural networks (MBNNs), and generative adversarial networks (GANs), in various aspects of animal farming. These models have demonstrated success in tasks such as real-time livestock detection, disease prediction, activity monitoring, and animal identification. However, challenges such as occlusion, data scarcity, small training datasets, environmental variability, and imbalanced data remain significant barriers to model reliability and scalability. By analyzing one hundred seventeen articles following PRISMA guidelines, this review highlights recent advancements, identifies research gaps, and discusses solutions such as image augmentation, synthetic data generation, and domain adaptation. The findings emphasize the potential of deep learning to enhance precision farming while addressing critical challenges. Finally, future research directions are proposed to improve model generalization, integration with IoT-based monitoring systems, and real-time decision-making for sustainable and intelligent livestock management.
深度学习通过实现自动化健康监测、行为分析和牲畜管理,彻底改变了畜牧业。本文综述了关键深度学习架构的应用,包括卷积神经网络(cnn), You Only Look Once (YOLO),基于记忆的神经网络(MBNNs)和生成对抗网络(gan),在动物养殖的各个方面。这些模型已经在诸如实时牲畜检测、疾病预测、活动监测和动物识别等任务中证明了成功。然而,诸如遮挡、数据稀缺、小训练数据集、环境可变性和数据不平衡等挑战仍然是模型可靠性和可扩展性的重大障碍。通过分析遵循PRISMA指南的117篇文章,本综述强调了最近的进展,确定了研究差距,并讨论了诸如图像增强、合成数据生成和域适应等解决方案。研究结果强调了深度学习在解决关键挑战的同时提高精准农业的潜力。最后,提出了未来的研究方向,以提高模型的泛化,与基于物联网的监测系统的集成,实时决策的可持续和智能畜牧业管理。
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.