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Review: Milking robot utilization, a successful precision livestock farming evolution
Automatic milking systems (AMS), one of the earliest precision livestock farming developments, have revolutionized dairy farming around the world. While robots control the milking process, there have also been numerous changes to how the whole farm system is managed. Milking is no longer performed in defined sessions; rather, the cow can now choose when to be milked in AMS, allowing milking to be distributed throughout a 24 h period. Despite this ability, there has been little attention given to milking robot utilization across 24 h. In order to formulate relevant research questions and improve farm AMS management there is a need to determine the current knowledge gaps regarding the distribution of robot utilization. Feed, animal and management factors and their interplay on levels of milking robot utilization across 24 h for both indoor and pasture-based systems are here reviewed. The impact of the timing, type and quantity of feed offered and their interaction with the distance of feed from the parlour; herd social dynamics, climate and various other management factors on robot utilization through 24 h are provided. This novel review draws together both the opportunities and challenges that exist for farm management to use these factors to improved system efficiency and those that exist for further research.
Journal Article
Monitoring cow activity and rumination time for an early detection of heat stress in dairy cow
The aim of this study was to explore the use of cow activity and rumination time by precision livestock farming tools as early alert for heat stress (HS) detection. A total of 58 Italian Friesian cows were involved in this study during summer 2015. Based on the temperature humidity index (THI), two different conditions were compared on 16 primiparous and 11 multiparous, to be representative of three lactation phases: early (15–84 DIM), around peak (85–154 DIM), and plateau (155–224 DIM). A separate dataset for the assessment of the variance partition included all the cows in the herd from June 7 to July 16. The rumination time (RT2h, min/2 h) and activity index (AI2h, bouts/2 h) were summarized every 2-h interval. The raw data were used to calculate the following variables: total daily RT (RTt), daytime RT (RTd), nighttime RT (RTn), total daily AI (AIt), daytime AI (AId), and nighttime AI (AIn). Either AIt and AId increased, whereas RTt, RTd, and RTn decreased with higher THI in all the three phases. The highest decrease was recorded for RTd and ranged from 49 % (early) to 45 % (plateau). The contribution of the cow within lactation phase was above 60 % of the total variance for AI traits and a share from 33.9 % (for RTt) to 54.8 % (RTn) for RT traits. These observations must be extended to different feeding managements and different animal genetics to assess if different thresholds could be identified to set an early alert system for the farmer.
Journal Article
Examining the Adoption of Drones and Categorisation of Precision Elements among Hungarian Precision Farmers Using a Trans-Theoretical Model
This article discusses the use of drones in Hungary and considers their future penetration, based on the responses to a nationally representative 2021 questionnaire among 200 large-scale farmers engaged in precision farming and in crop production. Both the applied trans-theoretical model (with ordinal logit regression model) and the questionnaire design are suitable for comparison with the results of a similar survey in Germany. In this study, similar results were found for farm size, age, main job and education, but the evidence that higher education in agriculture has the largest positive effect on the use of drones is a novelty. The frequency values obtained for adopting precision technology elements are not fully suitable for classification due to interpretational shortcomings. The use of drones within precision technologies is no longer negligible (17%), but is nevertheless expected to grow significantly due to continuous innovation and the selective application of inputs. The state could play a major role in future uptake, particularly in the areas of training and harmonisation of legislation.
Journal Article
In-Depth Development of a Versatile Rumen Bolus Sensor for Dairy Cattle
Precision agriculture and the increasing automation efforts in animal husbandry requires continuous and complex monitoring of the animals. Rumen bolus sensors, which are cutting-edge pieces of technology and a rapidly developing research field, present an exceptional opportunity for monitoring the health status, physiological parameters, and estrus of the animals. The objective of this paper is to provide a comprehensive overview of the development process of a new sensor development. We address the issues of conceptual design, an overview of applicable sensor modalities, mechanical design, power supply design, applicable hardware solutions, applicable communication solutions and finally the sensor detection algorithms proved in field tests. In conclusion, we present a summary of the current opportunities in the field and provide an analysis of the foreseeable trends.
Journal Article
Animal board invited review: precision livestock farming for dairy cows with a focus on oestrus detection
Dairy cows are high value farm animals requiring careful management to achieve the best results. Since the advent of robotic and high throughput milking, the traditional few minutes available for individual human attention daily has disappeared and new automated technologies have been applied to improve monitoring of dairy cow production, nutrition, fertility, health and welfare. Cows milked by robots must meet legal requirements to detect healthy milk. This review focuses on emerging technical approaches in those areas of high cost to the farmer (fertility, metabolic disorders, mastitis, lameness and calving). The availability of low cost tri-axial accelerometers and wireless telemetry has allowed accurate models of behaviour to be developed and sometimes combined with rumination activity detected by acoustic sensors to detect oestrus; other measures (milk and skin temperature, electronic noses, milk yield) have been abandoned. In-line biosensors have been developed to detect markers for ovulation, pregnancy, lactose, mastitis and metabolic changes. Wireless telemetry has been applied to develop boluses for monitoring the rumen pH and temperature to detect metabolic disorders. Udder health requires a multisensing approach due to the varying inflammatory responses collectively described as mastitis. Lameness can be detected by walk over weigh cells, but also by various types of video image analysis and speed measurement. Prediction and detection of calving time is an area of active research mostly focused on behavioural change.
Journal Article
Comparing State-of-the-Art Deep Learning Algorithms for the Automated Detection and Tracking of Black Cattle
Effective livestock management is critical for cattle farms in today’s competitive era of smart modern farming. To ensure farm management solutions are efficient, affordable, and scalable, the manual identification and detection of cattle are not feasible in today’s farming systems. Fortunately, automatic tracking and identification systems have greatly improved in recent years. Moreover, correctly identifying individual cows is an integral part of predicting behavior during estrus. By doing so, we can monitor a cow’s behavior, and pinpoint the right time for artificial insemination. However, most previous techniques have relied on direct observation, increasing the human workload. To overcome this problem, this paper proposes the use of state-of-the-art deep learning-based Multi-Object Tracking (MOT) algorithms for a complete system that can automatically and continuously detect and track cattle using an RGB camera. This study compares state-of-the-art MOTs, such as Deep-SORT, Strong-SORT, and customized light-weight tracking algorithms. To improve the tracking accuracy of these deep learning methods, this paper presents an enhanced re-identification approach for a black cattle dataset in Strong-SORT. For evaluating MOT by detection, the system used the YOLO v5 and v7, as a comparison with the instance segmentation model Detectron-2, to detect and classify the cattle. The high cattle-tracking accuracy with a Multi-Object Tracking Accuracy (MOTA) was 96.88%. Using these methods, the findings demonstrate a highly accurate and robust cattle tracking system, which can be applied to innovative monitoring systems for agricultural applications. The effectiveness and efficiency of the proposed system were demonstrated by analyzing a sample of video footage. The proposed method was developed to balance the trade-off between costs and management, thereby improving the productivity and profitability of dairy farms; however, this method can be adapted to other domestic species.
Journal Article
Research Trends in Artificial Intelligence
Stay informed about recent trends and groundbreaking research driving innovation in the AI-IoT landscape. AI, a simulated form of natural intelligence within machines, has revolutionized various industries, simplifying daily tasks for end-users. This book serves as a handy reference, offering insights into the latest research and applications where AI and IoT intersect. The book includes 12 edited chapters that provide a comprehensive exploration of the synergies between AI and IoT. The contributors attempt to address engineering opportunities and challenges in different fields. Key Topics: AI and IoT in Smart Farming: Explore how these technologies enhance crop yield and sustainability, revolutionizing agricultural practices. AIoT (Artificial Intelligence of Things): Understand the amalgamation of AI and IoT and its applications, particularly focusing on smart cities and agriculture. Smart Healthcare and Predictive Disease Analysis: Uncover the crucial role of AI and IoT in early disease prediction and improving healthcare outcomes. Applications of AI in Various Sectors: Explore how AI contributes to sustainable development, sentiment analysis, education, autonomous vehicles, fashion, virtual trial rooms, and more. Each chapter has structured sections with summaries and reference lists, making it an invaluable resource for researchers, professionals, and enthusiasts keen on understanding the potential and impact of these technologies in today's rapidly evolving world.
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