Precision livestock farming (PLF) offers a strategic solution to enhance the management capacity of large animal groups, while simultaneously improving profitability, efficiency, and minimizing environmental impacts associated with livestock production systems. Additionally, PLF contributes to optimizing the ability to manage and monitor animal welfare while providing solutions to global grand challenges posed by the growing demand for animal products and ensuring global food security. By enabling a return to the “per animal” approach by harnessing technological advancements, PLF enables cost-effective, individualized care for animals through enhanced monitoring and control capabilities within complex farming systems. Meeting the nutritional requirements of a global population exponentially approaching ten billion people will likely require the density of animal proteins for decades to come. The development and application of digital technologies are critical to facilitate the responsible and sustainable intensification of livestock production over the next several decades to maximize the potential benefits of PLF. Real-time continuous monitoring of each animal is expected to enable more precise and accurate tracking and management of health and well-being. Importantly, the digitalization of agriculture is expected to provide collateral benefits of ensuring auditability in value chains while assuaging concerns associated with labor shortages. Despite notable advances in PLF technology adoption, a number of critical concerns currently limit the viability of these state-of-the-art technologies. The potential benefits of PLF for livestock management systems which are enabled by autonomous continuous monitoring and environmental control can be rapidly enhanced through an Internet of Things approach to monitoring and (where appropriate) closed-loop management. In this paper, we analyze the multilayered network of sensors, actuators, communication, networking, and analytics currently used in PLF, focusing on dairy farming as an illustrative example. We explore the current state-of-the-art, identify key shortcomings, and propose potential solutions to bridge the gap between technology and animal agriculture. Additionally, we examine the potential implications of advancements in communication, robotics, and artificial intelligence on the health, security, and welfare of animals.

Smart devices have entered all spheres of modern living, from monitoring the steps we walk to managing refrigerator inventory, ushering in the dawn of a new urban experience. The kitchen is the heart of the home; a place to share, care for and nurture the family unit, but also a place seeing the greatest impact from the introduction of smart devices. The smart sensing and remote control capability of smart appliances have enabled great physical convenience for users but have had less impact on cognitive conveniences. While such devices can sense what they are working with, they fail to understand who they are working for, leaving much of the burden of trivial planning and decision making to humans with less personalized services. Hence, we introduce TupperwareEarth, a knowledge-based ontological semantic network for the “Internet of Kitchen Things” with the aim of reducing physical as well as cognitive loads of humans in cooking tasks. Also, we present a testbed for exploring kitchen innovation and validating the effectiveness of TupperwareEarth that combines intelligent kitchen storage containers, Smart Tupperware, and existing smart kitchen appliances through an Internet of Things network and a user-friendly front-end interface, Tuppy. Using this testbed, the quantitative user studies show a 33% reduction in average food preparation time and qualitative user surveys show that 75% of the users observed a significant reduction in cognitive loads, thereby validating the cognitive conveniences granted by TupperwareEarth.

Sensors in and around the environment becoming ubiquitous has ushered in the age of smart animal agriculture which has the potential to greatly improve animal health and productivity. The data gathered from sensors dwelling in animal agriculture settings have made farms a part of the IoT space leading to active research in developing efficient communication methodologies for farm networks. This study focuses on the first hop of farm networks where data from inside the body of animals is communicated to a node dwelling outside the body. Novel experimental methods are used to calculate the channel loss at sub-GHz frequencies (100–900 MHz) to characterize the in-body to out-of-body (IBOB) communication channel in large animals. A first-of-its-kind 3D bovine modeling is done with computer vision techniques for detailed morphological features of the animal body to perform Finite Element Method based Electromagnetic simulations. The results of the simulations are experimentally validated to build a complete channel modeling methodology for IBOB animal-body-communication. The 3D bovine model is made available publicly on GitHub. The results illustrate that an IBOB communication channel is realizable from the rumen to the collar of ruminants with $\leq \text{90 dB}$ path loss at sub-GHz frequencies making communication feasible. The developed methodology has been illustrated for ruminants but can also be used for other IBOB studies. An efficient communication architecture can be formed using the channel modeling technique illustrated for IBOB communication in animals paving the way for the design and development of future smart animal agriculture systems.

Ruminant livestock production faces increasing pressure to reduce environmental impacts while maintaining productivity and food security. This comprehensive review examines current strategies and emerging technologies for enhancing environmental sustainability in ruminant systems. The review synthesizes recent advances across four interconnected domains: genetic and genomic approaches for breeding environmentally efficient animals, rumen microbiome manipulation, nutritional strategies for emission reduction, and precision management practices. Specifically, genetic and genomic strategies demonstrate significant potential for long-term sustainability improvements through selective breeding for feed efficiency, methane reduction, and enhanced longevity. Understanding host–microbe interactions and developing targeted interventions have also shown promising effects on optimizing fermentation efficiency and reducing methane production. Key nutritional interventions include dietary optimization strategies that improve feed efficiency, feed additives, and precision feeding systems that minimize nutrient waste. Furthermore, management approaches encompass precision livestock farming technologies including sensor-based monitoring systems, automated feeding platforms, and real-time emission measurement tools that enable data-driven decision making. Integration of these approaches through system-based frameworks offers the greatest potential for achieving substantial environmental improvements while maintaining economic viability. In addition, this review identifies key research gaps including the need for standardized measurement protocols, long-term sustainability assessments, and economic evaluation frameworks. Future directions emphasize the importance of interdisciplinary collaboration, policy support, and technology transfer to accelerate adoption of sustainable practices across diverse production systems.

In nature, prehensile tails serve as versatile and essential appendages for animals, facilitating both grasping and enhanced mobility. Although existing robotic tails effectively contribute to mobility across a range of behaviors, they lack versatile object-grasping capabilities. Inspired by these biological capabilities, a soft robotic prehensile tail is presented that uniquely integrates object manipulation with dynamic mobility enhancement for quadrupedal robots. This robotic tail offers a threefold stiffness variation and achieves large-angle bending (${\sim} 636^{\circ }$) at the tail tip, thereby enabling secure and adaptable grasping. By adjusting its stiffness, the tail can conform to various shapes in a soft state and lift objects of different weights in a stiff state, demonstrating versatile grasping. The stiffened tail reliably supports the robot's body load (e.g., when hanging on a rod) and facilitates rapid, precise dynamic adjustments. Moreover, a novel synergy is revealed whereby grasped objects increase the tail's inertial effects, thereby enhancing the robot's dynamic capabilities during rapid maneuvers—a unique feature that transforms manipulation tasks into mobility advantages.