# Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture
## 1 Introduction  
Agricultural production is a crucial means for humans to meet the demand for food and agricultural products. It also serves as the foundation for the development of human society. Some automated equipment and technologies have been implemented in agricultural production [1]; for example, harvesters and automatic watering systems. However, due to the diversity and specificity of agricultural tasks, despite the advancements in modern technology driving agricultural automation, the level of automation varies depending on crops and regions. Manual labor still remains a crucial component of production in certain regions and for certain crops. However, with a shortage of human resources and low labor efficiency, traditional manual labor methods no longer suffice to meet the demands of modern agricultural production [2]. Hence, the imperative to integrate intelligent technologies and innovations to enhance the efficacy and quality of agricultural production has grown more pronounced. The incorporation of intelligent technologies, including artificial intelligence and the Internet of Things, into agricultural practices has significantly contributed to the optimization of production efficiency, reduction of operational costs, and augmentation of both yield and quality of agricultural products [3]. In the realm of agricultural production, areas of paramount concern encompass crop growth and yield, soil and environmental conditions, the quality of agricultural products, and the pursuit of sustainable development [4]. The physical labor behaviors carried out in the field, including activities such as sowing, fertilizing, spraying, plowing, weeding, etc., play a pivotal role in fosteringoptimal crop growth and achieving high-quality agricultural output. Consequently, the establishment of relevant datasets contributes to the development and improvement of automation technologies that are gradually replacing labor-intensive operations. These datasets will support research in both academia and practical applications, promote the development of agricultural robots and intelligent systems for automatically identifying agricultural workers' labor behaviors, and provide effective information for traceability systems, allowing consumers to understand the entire process of agricultural product growth. The intelligent analysis of these physical labor behaviors holds substantial significance in enhancing and streamlining physical labor processes. Such analyses contribute to heightened production efficiency, the optimization of resource utilization [5], the implementation of sophisticated business management practices [6], and the assurance of both crop growth and product quality. This is of significant importance for driving agricultural enterprises toward digitized and standardized management of farm workers, further promoting the transformation of traditional agriculture toward automation and smart farming. Currently, the monitoring of manual labor behavior in agricultural production predominantly depends on surveillance videos and intelligent analysis technology for the observation and analysis of labor activities conducted by farmers throughout the production process. Meanwhile, the processing of surveillance videos primarily hinges on manual operations [7]. Nevertheless, when confronted with substantial video data, this processing approach proves sluggish and inefficient, and incapable of facilitating all-weather monitoring. Consequently, enhancing the recognition capabilities of physical labor behaviors in the field and fortifying the guidance and supervision of farmers' labor [8] stand as pivotal facets in the advancement of smart agriculture aiming to attain the objective of refined management. The evolution of deep learning within the domain of human behavior recognition can be traced back to approximately 2010. Preceding the ascent of deep learning, human behavior recognition predominantly depended on conventional computer vision techniques.