Supporting the development and adoption of automatic lameness detection systems in dairy cattle : effect of system cost and performance on potential market shares

Most automatic lameness detection system prototypes have not yet been commercialized, and are hence not yet adopted in practice. Therefore, the objective of this study was to simulate the effect of detection performance (percentage missed lame cows and percentage false alarms) and system cost on the potential market share of three automatic lameness detection systems relative to visual detection: a system attached to the cow, a walkover system, and a camera system. Simulations were done using a utility model derived from survey responses obtained from dairy farmers in Flanders, Belgium. Overall, systems attached to the cow had the largest market potential, but were still not competitive with visual detection. Increasing the detection performance or lowering the system cost led to higher market shares for automatic systems at the expense of visual detection. The willingness to pay for extra performance was (sic)2.57 per % less missed lame cows, (sic)1.65 per % less false alerts, and (sic)12.7 for lame leg indication, respectively. The presented results could be exploited by system designers to determine the effect of adjustments to the technology on a system's potential adoption rate

Development of a stereovision-based technique to measure the spread patterns of granular fertilizer spreaders

Centrifugal fertilizer spreaders are by far the most commonly used granular fertilizer spreader type in Europe. Their spread pattern however is error-prone, potentially leading to an undesired distribution of particles in the field and losses out of the field, which is often caused by poor calibration of the spreader for the specific fertilizer used. Due to the large environmental impact of fertilizer use, it is important to optimize the spreading process and minimize these errors. Spreader calibrations can be performed by using collection trays to determine the (field) spread pattern, but this is very time-consuming and expensive for the farmer and hence not common practice. Therefore, we developed an innovative multi-camera system to predict the spread pattern in a fast and accurate way, independent of the spreader configuration. Using high-speed stereovision, ejection parameters of particles leaving the spreader vanes were determined relative to a coordinate system associated with the spreader. The landing positions and subsequent spread patterns were determined using a ballistic model incorporating the effect of tractor motion and wind. Experiments were conducted with a commercial spreader and showed a high repeatability. The results were transformed to one spatial dimension to enable comparison with transverse spread patterns determined in the field and showed similar results

User-centric design of automatic lameness detection in dairy cattle

Lameness is an important health problem causing severe welfare deteriorations and economic losses up to € 53 per cow per year in dairy cattle. Timely detection and treatment can help to minimize economic losses and preserve cow welfare. Current visual detection methods are labor intensive and subjective, and require training to allow detection of subtle changes in a cow’s gait. As a result, the problem is underestimated in practice, and lameness is often detected in a late stage when losses have already run high. Due to further intensification in the dairy sector, less time will be available to spend on monitoring individual animals in the future, implying that more objective and less time consuming methods are desirable. Automatic lameness detection systems can be a solution, and may enable early detection and treatment, but no truly cost-efficient systems are currently available on the market. The development and market introduction of existing prototypes is being held up by the unknown economic value and maximum investment cost of such systems, and the lack of knowledge on the potential adoption rate and farmers’ preferences concerning lameness detection performance and system cost. Promising results have been reported, but prototypes are often costly and some are difficult to implement in existing dairy barns, whereas their detection performance still seems insufficient for use in practice. Therefore, in this PhD research, user-centric design criteria for further development of existing prototypes into market-ready lameness detection systems were derived. It was investigated which factors influence economic value, and how this economic value can be quantified for specific farms and systems. Farmers’ preferences for the detection performance and cost of an automatic lameness detection system were investigated using a choice-experiment. Simultaneously, the effect of providing extra information on lameness and its consequences to the farmer was investigated. The gathered information was used to define how system developers could use this information to further develop existing prototypes, and to get an idea on the current adoption potential of automatic lameness detection in the Flemish dairy sector. An attempt was made to implement the derived design criteria in a walkover pressure mat by lowering the system cost and spatial requirements to increase the easy with which the system can be implemented in practice. In addition, new automatically measured gait variables that describe cow gait were derived from this sensor and used in new, improved individual lameness detection algorithms. Analysis of the economic value indicated several knowledge gaps that impede accurate economic value calculations. Especially the effect of early detection and treatment on the economic losses caused by lameness and the unknown system lifespan were important unknown drivers for economic value. System-specific and farm-specific information was incorporated to account for the fact that system cost and detection performance as well as the herd size can influence the economic value of a lameness detection system substantially. Automatic lameness detection systems proved capable of generating positive economic values, but the assumptions made to estimate the economic value should be kept in mind. The choice experiment led to the conclusion that dairy farmers prefer systems that miss few lame cows with a low number of false alarms for a low cost. Systems capable to indicate which leg is lame were preferred over systems that did not have this feature. Flemish dairy farmers were willing to pay more for a system with better detection performance. In general, visual detection was still preferred over automatic detection, except for those farmers who already have experience with automatic estrus detection systems. It was concluded that the detection performance should be sufficiently high for farmers to consider investing in an automatic lameness detection system. Providing extra information on lameness influenced farmers’ preferences positively, implying that sensitizing actions can improve the future adoption rate of automatic lameness detection systems. Also, the adoption can be supported by making systems cheaper, and by improving their detection performance. It was concluded that the Gaitwise sensor can be shortened from 4.88 to 3.28 meter to decrease the system cost and increase the ease of implementation in existing dairy barns. The sensor resolution can be lowered without affecting the lameness detection performance to reduce system cost further, leading to an estimated total cost reduction of 83 %. New variables describing how cows distribute their weight in time, and how within-stance times change as a result of lameness were derived. The new variables indeed differed between non-lame and lame cows, implying that they can be interesting to use in lameness detection algorithms. Finally, a new monitoring setup was built, and daily automatic measurements were executed with a walkover pressure mat (Gaitwise) to allow for the development of new detection algorithms with higher detection performance. The influence of environmental factors that affect cow gait, such as darkness and slipperiness of the walking surface, was reduced as much as possible. In a first step, a detection model based on group thresholds was developed, resulting in a still insufficient detection performance with a sensitivity of 36.9 % and specificity of 86.9 %. Cows were often distracted during measurements, implying that gait patterns of non-lame and lame cows could not easily be differentiated. In a second step, cow gait was monitored individually, but due to many missing and failed measurements resulting in a measurement success rate of 27.6 %, it was not possible to develop well-working individual detection algorithms. Nevertheless, suggestions were formulated to improve sensor implementation and data collection in the future to allow for better individual monitoring. Suggestions included keeping the number of obstacles and distractions as low as possible, and motivating cows to walk at a sufficiently high pace. Future research could use the presented results to support further development and adoption of automatic lameness detection systems in practice. Drivers for economic value should be investigated further to allow for more accurate estimations of the economic value, which can subsequently be used to define development goals. The economic value can be increased by lowering system cost and improving detection performance, and by integration of the used technologies with other health monitoring systems. However, future research should also use the presented results to investigate which preventive and other lameness-reducing measures should be incorporated in good lameness management, and whether further development is still feasible for all existing automatic lameness detection system prototypes.status: publishe

Farm-specific economic value of automatic lameness detection systems in dairy cattle: From concepts to operational simulations

Although prototypes of automatic lameness detection systems for dairy cattle exist, information about their economic value is lacking. In this paper, a conceptual and operational framework for simulating the farm-specific economic value of automatic lameness detection systems was developed and tested on 4 system types: walkover pressure plates, walkover pressure mats, camera systems, and accelerometers. The conceptual framework maps essential factors that determine economic value (e.g., lameness prevalence, incidence and duration, lameness costs, detection performance, and their relationships). The operational simulation model links treatment costs and avoided losses with detection results and farm-specific information, such as herd size and lameness status. Results show that detection performance, herd size, discount rate, and system lifespan have a large influence on economic value. In addition, lameness prevalence influences the economic value, stressing the importance of an adequate prior estimation of the on-farm prevalence. The simulations provide first estimates for the upper limits for purchase prices of automatic detection systems. The framework allowed for identification of knowledge gaps obstructing more accurate economic value estimation. These include insights in cost reductions due to early detection and treatment, and links between specific lameness causes and their related losses. Because this model provides insight in the trade-offs between automatic detection systems' performance and investment price, it is a valuable tool to guide future research and developments.status: publishe

Supporting the Development and Adoption of Automatic Lameness Detection Systems in Dairy Cattle: Effect of System Cost6 and6 Performance on Potential Market Shares

Most automatic lameness detection system prototypes have not yet been commercialized, and are hence not yet adopted in practice. Therefore, the objective of this study was to simulate the effect of detection performance (percentage missed lame cows and percentage false alarms) and system cost on the potential market share of three automatic lameness detection systems relative to visual detection: a system attached to the cow, a walkover system, and a camera system. Simulations were done using a utility model derived from survey responses obtained from dairy farmers in Flanders, Belgium. Overall, systems attached to the cow had the largest market potential, but were still not competitive with visual detection. Increasing the detection performance or lowering the system cost led to higher market shares for automatic systems at the expense of visual detection. The willingness to pay for extra performance was  2.57 per % less missed lame cows,  1.65 per % less false alerts, and  12.7 for lame leg indication, respectively. The presented results could be exploited by system designers to determine the effect of adjustments to the technology on a system’s potential adoption rate.status: publishe

Farm-specific economic value of automatic lameness detection systems in dairy cattle : from concepts to operational simulations

Although prototypes of automatic lameness detection systems for dairy cattle exist, information about their economic value is lacking. In this paper, a conceptual and operational framework for simulating the farm-specific economic value of automatic lameness detection systems was developed and tested on 4 system types: walkover pressure plates, walkover pressure mats, camera systems, and accelerometers. The conceptual framework maps essential factors that determine economic value (e.g., lameness prevalence, incidence and duration, lameness costs, detection performance, and their relationships). The operational simulation model links treatment costs and avoided losses with detection results and farm-specific information, such as herd size and lameness status. Results show that detection performance, herd size, discount rate, and system lifespan have a large influence on economic value. In addition, lameness prevalence influences the economic value, stressing the importance of an adequate prior estimation of the on-farm prevalence. The simulations provide first estimates for the upper limits for purchase prices of automatic detection systems. The framework allowed for identification of knowledge gaps obstructing more accurate economic value estimation. These include insights in cost reductions due to early detection and treatment, and links between specific lameness causes and their related losses. Because this model provides insight in the trade-offs between automatic detection systems' performance and investment price, it is a valuable tool to guide future research and developments

Development of an open-source, low-cost and adaptable 3D accelerometer for monitoring animal motion

Lameness is, after mastitis and reduced fertility, the third most important health problem in dairy cows. Lameness causes a significant decrease in milk production, a reduced fertility, a higher culling rate and severely deteriorates animal welfare. To help farmers in detecting lame cows, ILVO has developed the GAITWISE system which consists of a six-meter long pressure sensitive mat to detect the positions of the legs with respect to time as well as the relative pressure exerted by the legs when cows walk over it. In order to determine whether a cow is lame, measured variables (e.g. stride length, abduction, asymmetry in relative force) are compared with the previous values of the same cow by a cow specific model. To validate the measurements from the GAITWISE, an open-source, low-cost and adaptable prototype 3D accelerometer is developed. The goal is to detect the motion of the cow’s legs using accelerometers and gyroscopes in x, y and z-direction in a way that data can be obtained online in order to reconstruct the motion of a cow’s legs. For the first prototype the single-board microcontroller Arduino Nano with an Atmega 328, an open source platform, serves as the processing unit, while MPU-6050 (InvenSense, CA, USA) is used to get the raw tri-axis acceleration and angular velocity data. The MPU-6050 is designed for low power, low cost and high performance requirements of wearable sensors. The unit has a digital output and communicates with the Arduino platform through I2C. An ultra-low power module (NRF24L01+, Nordic Semiconductor, Trondheim, Norway) is used to create a network that allows to receive the data online using the 2.4 GHz ISM band. The newly developed sensor is user programmable, allowing one to choose between different ranges and resolutions for the measurement of the acceleration (±2g, ±4g, ±8g or ±16g) and the angular velocity (±250°/s, ±500°/s, ±1000°/s or ± 2000°/s). An Arduino Nano with a high power NRF24L01+ module and a rechargeable battery is placed on the neckband of the animal as an extra node in the network to amplify the signal. The receiver module contains also an Arduino Nano and a high power NRF24L01+ module and is connected to a computer to collect and process all the received data. The price for the sensor on the legs (one per leg) will be approximately € 20.5, the neckband transmitter and the receiver around € 26 and € 17 respectively, which adds up to a total of € 125. First tests showed that the prototype is able to measure accelerations with an error below 2.5 % and gyroscope values with an error below 0.3 %. A preliminary test with a walking cow showed that the sensor was able to record the acceleration and angular velocity of the cow’s legs. Moreover, the designed sensor is open source and low-cost. The main drawback of our first prototype is that after 24hrs the battery needs recharging. To be used in future tests with walking cows a smaller version of the sensor that is attached to the legs, will be designed. Therefore the Pro Mini version of the Arduino microcontroller will be used with a NRF24L01+ low power transceiver and a rechargeable battery

Comparison of different spread pattern determination techniques

Traditionally, the performance of fertilizer spreaders is assessed using a row of collection trays aligned perpendicular to the driving direction of the tractor. For precise calibration of the spreader this technique, however, does not provide adequate insight into the spreading process since particle distributions are measured in only one spatial dimension. In this paper, two different two dimensional spread pattern determination techniques (SPDT) were tested, each consisting of a sampling method and a matching interpolation algorithm. Tests were executed under similar conditions with three commonly used types of fertilizer (CAN, NPK, KCl) with different physical properties. Results were compared with the traditional technique. The differences found illustrate the importance of using an adequate SPDT to compare spread patterns