Lameness and gait irregularities are significant concerns in equine health management, affecting performance, welfare, and economic value. Traditional observational methods rely on subjective expert assessments, which can lead to inconsistencies in detecting subtle or early-stage lameness. While AI-based approaches have emerged, many require multiple sensors, force plates, or video systems, making them costly and impractical for field deployment. In this applied research study, we present a stride-level classification system that utilizes a single inertial measurement unit (IMU) and a one-dimensional convolutional neural network (1D CNN) to objectively differentiate between sound and lame horses, with a primary focus on the trot gait. The proposed system was tested under real-world conditions, achieving a 90% session-level accuracy with no false positives, demonstrating its robustness for practical applications. By employing a single, non-intrusive, and readily available sensor, our approach significantly reduces the complexity and cost of hardware requirements while maintaining high classification performance. These results highlight the potential of our CNN-based method as a field-tested, scalable solution for automated lameness detection. By enabling early diagnosis, this system offers a valuable tool for preventing minor gait irregularities from developing into severe conditions, ultimately contributing to improved equine welfare and performance in veterinary and equestrian practice.

Siemens has been selected by Lancashire based Hoofcount Ltd., the revolutionary manufacturer of market-leading footbaths for cows, to provide critical data-gathering capabilities that will have a significant impact on animal health and welfare, helping farmers to become more operationally efficient. Siemens' data capture and control technologies are helping farmers make informed decisions to better tackle lameness, a major problem in dairy herds. Hoofcount is utilising Siemens' expertise in combining the real and digital worlds using LOGO, an intelligent logic module for small automation projects in industrial settings. LOGO manages the control of the key mechanisms of Hoofcount's footbaths, such as chemical and water pumps, and animal feed controls. LOGO controllers allow Hoofcount to track the cows that go through its footbaths and provide valuable data on the herd, which can then be displayed on an HMI screen for visualisation.

MILKSURE PART 1 A training course for farmers to safeguard residue-free milk – Have you recently suffered a bulk milk residue failure?

Artificial intelligence and detection of lameness in dairy cattle will change dairy farming. An early detection method for cow lameness (hoof disease), a major disease of dairy cattle, has now been developed from images of cow gait with an accuracy of 99 percent or higher by applying human gait analysis. This technique allows early detection of lameness from cow gait, which was previously difficult. It is hoped that a revolution in dairy farming can be achieved through detailed observation by AI-powered image analysis. Dairy farmers are busy with routines such as cleaning cowsheds, milking, and feeding, so it's very difficult to determine the condition of cows.

Hoof health is an important aspect of proper dairy cattle care. Injuries and illnesses of hooves, called'lameness', if left untreated, will lead not only to declining quantity and quality of dairy products, but also to life-threatening disease. Thus, its early detection is very important. Indicators for lameness are manifested in back arch and gait patterns of cows. Methods for finding lameness by detecting back arch had been studied; however, that method was effective in detecting moderate to severe lameness. This group established a method for the early detection of lameness from cow gait images with an accuracy of 99% or higher by using their own human gait analysis technique.

Cows and robots go together. Throw in facial recognition software, and it's the perfect trifecta. This is because cows are happier when they are not around people, since they identify humans as predators. Using facial recognition software to count a herd, or signal when a cow is sick or injured or not eating, is another way to keep humans out of the pastures as much as possible and keep cows happier and more productive. "No prey animal never wants to see a predator. The less they see the happier they are. A cow doesn't know what a robot is, so they aren't scared of it," said David Hunt, co-founder of Cainthus, a company digitizing agricultural practices, speaking at an Alltech conference in Lexington, Ky.

At one of the recent conferences we've discusses the possibility to apply artificial intelligence to agriculture and the great benefits of it. Through numerous investigations it was found out that economics can make a significant improvement after moving from theoretical artificial intelligence concept to its practical application in the agriculture niche. Actually, what an artificial intelligence is? The machine learning involves computers' ability to learn from a great variety of calculations and data in order to finally provide an appropriate decision. One of our experts specified a number of agricultural applications that can be used for technology purposes.