The increase in precision agriculture has promoted the development of picking robot technology, and the visual recognition system at its core is crucial for improving the level of agricultural automation. This paper reviews the progress of visual recognition technology for picking robots, including image capture technology, target detection algorithms, spatial positioning strategies and scene understanding. This article begins with a description of the basic structure and function of the vision system of the picking robot and emphasizes the importance of achieving high-efficiency and high-accuracy recognition in the natural agricultural environment. Subsequently, various image processing techniques and vision algorithms, including color image analysis, three-dimensional depth perception, and automatic object recognition technology that integrates machine learning and deep learning algorithms, were analyzed. At the same time, the paper also highlights the challenges of existing technologies in dynamic lighting, occlusion problems, fruit maturity diversity, and real-time processing capabilities. This paper further discusses multisensor information fusion technology and discusses methods for combining visual recognition with a robot control system to improve the accuracy and working rate of picking. At the same time, this paper also introduces innovative research, such as the application of convolutional neural networks (CNNs) for accurate fruit detection and the development of event-based vision systems to improve the response speed of the system. At the end of this paper, the future development of visual recognition technology for picking robots is predicted, and new research trends are proposed, including the refinement of algorithms, hardware innovation, and the adaptability of technology to different agricultural conditions. The purpose of this paper is to provide a comprehensive analysis of visual recognition technology for researchers and practitioners in the field of agricultural robotics, including current achievements, existing challenges and future development prospects.

In a paper published in Journal of Geo-information Science, a group of researchers pioneered a new paradigm by leveraging large language models (LLMs) in constructing typhoon disaster knowledge graphs (KGs), transforming fragmented data into structured disaster intelligence. Simultaneously, these KGs are fused into LLMs to achieve intelligent knowledge services, advancing contextualized and intelligent disaster response systems.

Team from the First Affiliated Hospital of Jinan University and Southern Medical University, have developed an innovative CTP scanning protocol and a deep learning model that can generate the vital blood flow maps needed to assess stroke patients. This work has proofed the proposed low radiation dose imaging program can slash radiation exposure by over 80% compared to current methods. This innovation promised to make stroke diagnosis safer and more accessible, particularly for vulnerable patients.

Symposia will be a focal point of ESA’s upcoming Annual Meeting in Baltimore, Maryland, Aug. 10-15, addressing the frontiers of ecological research, wetland restoration in the Chesapeake Bay Watershed, deep soil carbon storage and much more.

A breast scan for detecting cancer takes less than a minute using an experimental system that combines photoacoustic and ultrasound imaging, according to a study in IEEE Transactions on Medical Imaging. The system does not require painful compression like mammography. In tests, it produced clear, artificial intelligence-powered 3D images of common breast cancer subtypes such as Luminal A, Luminal B and Triple-Negative Breast Cancer.