Scientists from the Marine Biological Association and the University of Plymouth have revisited turn-of-the-century forecasts about the many and varied threats they thought were likely to face the world’s shorelines in 2025. Their new study highlights that many of their forecasts were correct, either in whole or in part, while others haven’t had the impacts that were envisaged at the time. They have also charted some of the other threats to have emerged and/or grown in significance since their original work, with notable examples including global plastic pollution, ocean acidification, extreme storms and weather, and light and noise pollution.

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.

The azuki bean beetle is a common pest of stored beans and peas. Researchers at Kyushu University have found that when beetles infected with Wolbachia bacteria are exposed to elevated temperature and carbon dioxide they tend to produce larger eggs to enhance the survivability of their offspring. Interestingly, these larger eggs gave rise only to male larvae.

A new study  reveals that oil extracted from black soldier fly larvae (BSFL) has potent anti-inflammatory effects on immune cells. The research found that a modified version of the oil can suppress harmful inflammatory signals while supporting metabolic health. The findings offer exciting potential for sustainable, natural feed additives that support animal immunity and reduce reliance on synthetic drugs.