AI in agriculture leverages technologies like machine learning, computer vision, and data analytics to optimize farming practices, crop management, and resource utilization. It enables tasks such as automated monitoring, disease detection, yield prediction, and precision farming, leading to increased efficiency, improved productivity, and sustainable agriculture practices.
Researchers propose TwinPort, a cutting-edge architecture that combines digital twin technology and drone-assisted data collection to achieve precise ship maneuvering in congested ports. The approach incorporates a recommendation engine to optimize navigation during the docking process, leading to enhanced efficiency, reduced fuel consumption, and minimized environmental impact in smart seaports.
Scientists are using automated wildlife sensors and artificial intelligence (AI) over the next four years to demonstrate the effectiveness of agri-environment and peatland restoration schemes in improving biodiversity.
A recent study presents a revolutionary framework that utilizes a multi-channeled dense neural network (DNN) to predict temperature and relative humidity values in a greenhouse cultivating strawberries. The framework determines optimal sensor locations without the need for relocation, providing accurate estimations of micro-climate conditions.
A groundbreaking study presents a framework that leverages computer vision and artificial intelligence to automate the inspection process in the food industry, specifically for grading and sorting carrots. By incorporating RGB and depth information from a depth sensor, the system accurately identifies the geometric properties of carrots in real-time, revolutionizing traditional grading methods.
The integration of AIoT and digital twin technology in aquaculture holds the key to revolutionizing fish farming. By combining real-time data collection, cloud computing, and AI functionalities, intelligent fish farming systems enable remote monitoring, precise fish health assessment, optimized feeding strategies, and enhanced productivity. This integration presents significant implications for the industry, paving the way for sustainable practices and improved food security.
The integration of artificial intelligence (AI) is transforming the battle against food waste and propelling the transition towards a circular economy. By leveraging AI technologies, such as advanced analytics and machine learning, various applications are being developed to optimize food manufacturing, distribution networks, and waste management processes. These AI-driven solutions enhance decision-making, enable efficient resource utilization, and support recycling and upcycling initiatives.
The study explores the potential of using visual ChatGPT, a visual language model, in remote sensing tasks. It highlights the model's capabilities in image analysis and manipulation, including scene classification, edge detection, and image segmentation.
Researchers explore the catastrophic risks of advanced AI development and provide strategies to mitigate them, including addressing malicious use, managing AI races, handling organizational risks, and controlling rogue AIs through safety measures and proactive measures.
West Virginia University researchers have secured a $175,000 grant from the Richard King Mellon Foundation to combat the threat of multiflora rose, an invasive species affecting native plants in over 40 states. Leveraging drones equipped with sensors and machine learning technology, the project aims to develop software for detecting and treating multiflora rose and other invasives, potentially reducing herbicide usage and costs while promoting native species.
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