If your cat's sulking, your dog's whining or your rabbit's doing that strange thing with its paws again, you will recognise that familiar pang of guilt shared by most other pet owners. But for those who wish they knew just what was going on in the minds of their loyal companions, help may soon be at hand – thanks to the establishment of first scientific institution dedicated to empirically investigating the consciousness of animals. The Jeremy Coller Centre for Animal Sentience, based at the London School of Economics and Political Science (LSE), will begin its work on 30 September, researching non-human animals, including those as evolutionarily distant from us as insects, crabs and cuttlefish. One of its most eye-catching projects will be to explore how AI can help humans "speak" with their pets, the dangers of it going wrong – and what we need to do to prevent that happening. "We like our pets to display human characteristics and with the advent of AI, the ways in which your pet will be able to speak to you is going to be taken to a whole new level," said Prof Jonathan Birch, the inaugural director of the centre.

Precision livestock farming (PLF) has been transformed by machine learning (ML), enabling more precise and timely interventions that enhance overall farm productivity, animal welfare, and environmental sustainability. However, despite the availability of various sensing technologies, few datasets leverage multiple modalities, which are crucial for developing more accurate and efficient monitoring devices and ML models. To address this gap, we present MmCows, a multimodal dataset for dairy cattle monitoring. This dataset comprises a large amount of synchronized, high-quality measurement data on behavioral, physiological, and environmental factors. It includes two weeks of data collected using wearable and implantable sensors deployed on ten milking Holstein cows, such as ultra-wideband (UWB) sensors, inertial sensors, and body temperature sensors.

Tech expert Kurt Knutsson discusses how robots and drones are revolutionizing fruit farming with faster picking and smarter handling. Farming is undergoing a remarkable transformation thanks to cutting-edge technologies reshaping how fruit is picked and handled. While autonomous drones like Tevel's Flying Robots are already harvesting fruit globally, innovations like UC San Diego's GRIP-tape gripper represent the next frontier in gentle produce handling. Together, these advancements promise to make fruit production more efficient and precise, though one is a proven solution and the other is a glimpse into farming's future. GET SECURITY ALERTS & EXPERT TECH TIPS – SIGN UP FOR KURT'S'THE CYBERGUY REPORT' NOW Tevel's Flying Autonomous Robots (FARs) are redefining fruit harvesting by combining artificial intelligence with advanced computer vision.

The rapid growth of the global population and the continuous decline in cultivable land pose significant threats to food security. This challenge worsens as climate change further reduces the availability of farmland. Soilless agriculture, such as hydroponics, aeroponics, and aquaponics, offers a sustainable solution by enabling efficient crop cultivation in controlled environments. The integration of the Internet of Things (IoT) with smart precision farming improves resource efficiency, automates environmental control, and ensures stable and high-yield crop production. IoT-enabled smart farming systems utilize real-time monitoring, data-driven decision-making, and automation to optimize water and nutrient usage while minimizing human intervention. This paper explores the opportunities and challenges of IoT-based soilless farming, highlighting its role in sustainable agriculture, urban farming, and global food security. These advanced farming methods ensure greater productivity, resource conservation, and year-round cultivation. However, they also face challenges such as high initial investment, technological dependency, and energy consumption. Through a comprehensive study, bibliometric analysis, and comparative analysis, this research highlights current trends and research gaps. It also outlines future directions for researchers, policymakers, and industry stakeholders to drive innovation and scalability in IoT-driven soilless agriculture. By emphasizing the benefits of vertical farming and Controlled Environment Agriculture (CEA)-enabled soilless techniques, this paper supports informed decision-making to address food security challenges and promote sustainable agricultural innovations.

Game-changing technology figures to revolutionize weed control. Imagine a future where farming is not only more efficient but also cleaner and greener. That's exactly what Carbon Robotics is promising with its latest innovation, the LaserWeeder G2. This game-changing technology figures to revolutionize weed control by ditching chemicals altogether. It's a solution that's both good for the planet and great for farmers looking to reduce their environmental footprint.

The decline of bee and wind-based pollination systems in greenhouses due to controlled environments and limited access has boost the importance of finding alternative pollination methods. Robotic based pollination systems have emerged as a promising solution, ensuring adequate crop yield even in challenging pollination scenarios. This paper presents a comprehensive review of the current robotic-based pollinators employed in greenhouses. The review categorizes pollinator technologies into major categories such as air-jet, water-jet, linear actuator, ultrasonic wave, and air-liquid spray, each suitable for specific crop pollination requirements. However, these technologies are often tailored to particular crops, limiting their versatility. The advancement of science and technology has led to the integration of automated pollination technology, encompassing information technology, automatic perception, detection, control, and operation. This integration not only reduces labor costs but also fosters the ongoing progress of modern agriculture by refining technology, enhancing automation, and promoting intelligence in agricultural practices. Finally, the challenges encountered in design of pollinator are addressed, and a forward-looking perspective is taken towards future developments, aiming to contribute to the sustainable advancement of this technology.

Once upon a time, water flowed across the surface of Mars. Waves lapped against shorelines, strong winds gusted and howled, and driving rain fell from thick, cloudy skies. Mars is about half the diameter of Earth, and that's where things went wrong. The Martian core cooled quickly, soon leaving the planet without a magnetic field. This, in turn, left it vulnerable to the solar wind, which swept away much of its atmosphere. Without a critical shield from the sun's ultraviolet rays, Mars could not retain its heat.

In the past couple of years, regulators have been caught off guard again and again as tech companies compete to launch ever more advanced AI models. As it stands, it seems there's little anyone can do to delay or prevent the release of a model that poses excessive risks. Existing measures to mitigate AI risks aren't enough to protect us, so we need new approaches. One could be a kind of AI safety hotline tasked with expert volunteers. Read more about how the hotline could work.

Food production is a vital global concern and the potential for an agritech revolution through artificial intelligence (AI) remains largely unexplored. This paper presents a comprehensive review focused on the application of machine learning (ML) in agriculture, aiming to explore its transformative potential in farming practices and efficiency enhancement. To understand the extent of research activity in this field, statistical data have been gathered, revealing a substantial growth trend in recent years. This indicates that it stands out as one of the most dynamic and vibrant research domains. By introducing the concept of ML and delving into the realm of smart agriculture, including Precision Agriculture, Smart Farming, Digital Agriculture, and Agriculture 4.0, we investigate how AI can optimize crop output and minimize environmental impact. We highlight the capacity of ML to analyze and classify agricultural data, providing examples of improved productivity and profitability on farms. Furthermore, we discuss prominent ML models and their unique features that have shown promising results in agricultural applications. Through a systematic review of the literature, this paper addresses the existing literature gap on AI in agriculture and offers valuable information to newcomers and researchers. By shedding light on unexplored areas within this emerging field, our objective is to facilitate a deeper understanding of the significant contributions and potential of AI in agriculture, ultimately benefiting the research community.

Automatic weeding technologies have attained a lot of attention lately, because of the harms and challenges weeds are causing for livestock farming, in addition to that weeds reduce yields. We are targeting automatic and mechanical Rumex weeding in open pasture fields using light weight mobile field robot technologies. We describe a mobile weeding robot with GNSS navigation, 3D computer vision for weed detection, and a robot arm with a mechanical weeding tool. Our main contribution is showing the feasibility of light weight robot, sensor, and tool technologies in mechanical removal of weed seedlings.