Chinese Agronomy and the Development of Agronomy Concepts

Abstract

Current gesture recognition systems primarily focus on identifying gestures within a predefined set, leaving a gap in connecting these gestures to interactive GUI elements or system functions (e.g., linking a 'thumb-up' gesture to a 'like' button). We introduce GestureGPT, a novel zero-shot gesture understanding and grounding framework leveraging large language models (LLMs). Gesture descriptions are formulated based on hand landmark coordinates from gesture videos and fed into our dual-agent dialogue system. A gesture agent deciphers these descriptions and queries about the interaction context (e.g., interface, history, gaze data), which a context agent organizes and provides. Following iterative exchanges, the gesture agent discerns user intent, grounding it to an interactive function. We validated the gesture description module using public first-view and third-view gesture datasets and tested the whole system in two real-world settings: video streaming and smart home IoT control. The highest zero-shot Top-5 grounding accuracies are 80.11% for video streaming and 90.78% for smart home tasks, showing potential of the new gesture understanding paradigm.CCS Concepts: • Human-centered computing → User interface management systems; • Computing methodologies → Natural language processing.

Traditional farm tools observed from an ecological and health perspective

“…After thousands of years of development, a rich experience in farming methods, agricultural instruments, animal and plant domestication and so on had been gained, forming China's farming culture. However, compared with Europe at the same time, China's traditional agriculture was behind by a long way during the Qing Dynasty (Zeng, 2012). Europe had developed modern agriculture due to the progress in natural sciences; that is, ‘agricultural science and technology based on and guided by modern Western sciences, plus agricultural institutions and management systems under the capitalist system’ (Wei, 2012: 3), which greatly exceeded other contemporary systems in agricultural output and efficiency.…”

Automatically Extracting Rubber Tree Stem Shape from Point Cloud Data Acquisition Using a B-Spline Fitting Program

“…The key to guaranteeing quality and production is to precisely control the tapping depth by fitting the tap to the contour of the rubber tree. The main reason is that a deeper tapping depth will cause dead bark to become diseased and decrease the economic life of the rubber tree, while a shallower depth cannot destroy enough laticiferous tubing and reduces the production of natural rubber [5][6][7]. However, the coarse contours of rubber trees are too complicated to fit using existing equipment, which mainly relies on mechanical deformation and leads to low precision and undesirable effects during the tapping operation [8][9][10].…”