This article cites format
Guo Yangyang, Du Shuzeng, Qiao Yongliang, Liang Dong. Research and Application Progress of Deep Learning in Smart Livestock Breeding[J]. Smart Agriculture, 2023, 5(1): 52-65. doi:10.12133/j.smartag.SA202205009
GUO Yangyang, DU Shuzeng, QIAO Yongliang, LIANG Dong. Advances in the Applications of Deep Learning Technology for Livestock Smart Farming[J]. Smart Agriculture, 2023, 5(1): 52-65. doi:10.12133/j.smartag.SA202205009
Challenges and prospects of deep learning in smart livestock breeding
Large-scale, standardized, intelligent and precise healthy breeding and management is the general trend of animal husbandry. In recent years, although large-scale and standardized breeding has been rapidly improved, the level of intelligent and refined management of most cattle farms in China is still in the initial stage, and the overall degree of informatization and automation is not high. The combination of deep learning technology and animal husbandry can strengthen the ability of remote information perception in the process of animal husbandry, obtain information on animal growth status and breeding environment, and monitor animal health in real time, so as to accurately manage animals. Animal husbandry involves environmental monitoring, facility layout, information collection and transmission, nutrient supply, and perception of animal physiological and psychological changes, etc., and deep learning models have developed to lightweight structures to achieve efficient and high-precision information processing while occupying less storage space. At present, deep learning technology has been used in animal target detection, individual recognition, body condition evaluation, weight estimation, behavior detection and other tasks. However, deep learning still needs to be further developed in terms of model lightweight, generalization, and combination with intelligent equipment such as mechanical equipment and robots to meet the actual feeding and management needs, and the main challenges are as follows:
(1) Deep learning models often rely on a large number of labeled data samples, which often have limitations when generalized to new datasets or other types of animals, and in complex feeding environments, the labeling of domestic animal image and video data is time-consuming and labor-intensive (such as animal body condition scores and small behavior change labeling). It is still challenging to combine semi-supervised or few-shot learning to improve the generalization ability of deep learning models, realize the perception and analysis of livestock physiological habitats, and build a real-time all-weather intelligent monitoring and analysis system. At present, image enhancement methods are often used for style transfer and image generation to achieve sample expansion, but there are still differences with the real breeding environment. In addition, further exploration is needed to determine whether the study based on small samples can be applied to animal husbandry.
(2) The unified cooperation and harmonious development of people, equipment and breeding animals. The application of intelligent equipment in animal husbandry can improve production efficiency and liberate labor, but it still needs to be explored in theory and practice to ensure animal welfare while facilitating the operation and management of animal husbandry personnel, so as to ultimately improve the overall breeding efficiency and management level. Among them, intelligent equipment is used in animal husbandry production, according to different needs, there is diversity of intelligent equipment, and the refined operation of intelligent equipment is also one of the challenges faced by intelligent animal husbandry.
(3) Deep integration of big data, deep learning technology and animal husbandry. With the development of deep learning, Internet of Things, and sensor technology, the quantity and quality of animal information data have been greatly improved. Formulating unified and efficient data standards for the animal husbandry industry, enhancing the security and maintainability of data, combining deep learning and intelligent computing to analyze and process big data, and applying intelligent technology to major core issues such as disease prevention and control, precision feeding, environmental control and good product breeding, will play a significant role in promoting the development of smart animal husbandry. However, the layout of data acquisition equipment, the real-time transmission and communication of data, the efficient and accurate performance of algorithms, and how to correlate monitoring results with animal health information are challenging.
(4) Interpretability and safety challenges faced by artificial intelligence technology represented by deep learning models in the field of aquaculture. Interpretability refers to what the model learns from the data (expressed in a way that is understandable to humans) that leads to the final decision, and what factors are used to make judgments and how to make the final decision. However, this "end-to-end" decision-making model leads to extremely weak explanatory nature of deep learning models. This is also the reason why a large number of people still tend to apply traditional statistical models with high interpretability in the case of such high accuracy of deep learning. In terms of security, it refers to the artificial intelligence technology represented by deep neural networks, which has many security defects due to its complex algorithms, numerous parameters, and massive data-driven characteristics. With the development of smart animal husbandry and the wide promotion of artificial intelligence technology, it is closely related to the management and economy of the breeding industry, and these potential safety hazards are likely to completely explode at a certain node. Therefore, the research on the safety of artificial intelligence technology has become particularly important.
In summary, deep learning technology has been gradually applied to tasks such as automatic identification and health monitoring of livestock, but the monitoring model needs to be further optimized to achieve real-time monitoring and application in multiple scenarios. In addition, there are many challenges that need to be overcome in practical application environments, such as the generalization performance and robustness of the model under different growth stages and different animal breeds. In order to promote the development of animal husbandry, it is necessary to develop and build an intelligent monitoring system based on the actual situation of animal husbandry, so as to promote the development of animal husbandry.
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