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In the agro-food industry, there is an increasing demand for non-destructive, fast and cost-efficient methods for the objective determination of product quality. Several parameters influence the quality of a product, while the importance of each of these parameters may vary amongst people. Optical measurement techniques are often used because of their non-destructive nature. However, the robust determination of both chemical and physical quality attributes remains difficult. In this work, a non-destructive determination of quality was performed using laser scatter imaging. This technique allows to obtain more information on both the absorption and scattering of light, by retrieving spatial information. The bulk optical properties (bulk absorption coefficient, bulk scattering coefficient and scattering anisotropy factor) are used to characterize absorption and scattering properties.^ The absorption of light is related to the chemical composition of a product, like present pigments, water or sugars. Scattering of light is more related to the physical structure, possibly allowing to retrieve more information on physical quality attributes, like firmness, tenderness or porosity. Optical properties of light were derived from the obtained scatter images using a data-based modelling technique, possibly allowing a better prediction of product quality. This approach was tested in two case studies, apples and bovine meat, selected because of their economic importance for Belgium.First, a hyperspectral laser scatter imaging (HLSI) system was developed. A combination of a supercontinuum laser with monochromator was used to illuminate samples with monochromatic light in the 550 nm to 1000 nm range, while a CCD camera was used to take images of the diffuse reflectance glow spots.^ From this glow spots, a diffuse reflectance profile with the light intensity relative to the distance from the point illumination was obtained. Models were constructed to optimize the detector size and the source-detector distance, estimating different quality parameters of Braeburn apples. A detector size of 0.82 mm was found to be adequate for the estimation of all parameters, including the starch value, firmness, SSC and Streif index. Different source-detector distances were found to be of importance for predicting different quality traits. Photons exiting the sample closer to the point illumination, which have interacted less with the sample, were more important for SSC prediction, while the prediction of physical parameters like firmness relied on photons which had more interaction with the sample. Moreover, using variable selection, the wavelength regions of pigment and water absorption were found to be most informative.^ A classification of apples into ripeness classes based on these models was possible, with a misclassification rate of 12.5%. Nevertheless, these models still used mixed information, including both the effects of absorption and scattering.Using the double integrating spheres (DIS), the golden standard method for determining bulk optical properties (BOP), the interaction of light with both apple and bovine meat samples was studied. Both the apple skin and cortex were studied separately during maturation of bi-colored (Braeburn and Kanzi) and green cultivars (Greenstar). The bulk absorption coefficient µa of the skin showed features of anthocyanins at 550 nm, chlorophyll at 678 nm and water at 970 nm, 1200 nm and 1450 nm, while the µa of the cortex showed an overall lower absorption attributed to carotenoids, chlorophyll and water.^ During maturation of apples, an increase in the absorption by anthocyanins was observed in the red cultivar’s skin, while a decrease in absorption by chlorophyll was observed in the cortex. Both the bulk scattering coefficient µs and anisotropy factor g of the skin were significantly higher in comparison to the cortex. Both skin and cortex were found to be highly forward scattering with anisotropy factors above 0.9 in the entire wavelength range between 500 nm and 1850 nm. During maturation, no clear evolutions in the anisotropy factor were observed, while µs decreased in the fruit cortex. It was hypothesized that the shape and size of the scattering particles hardly changed during maturation.The DIS analysis showed changes in the optical properties of apple during maturation. This indicates that the non-destructive estimation of BOP could be beneficial in determining apple quality.^ To go from scatter images towards an estimation of the optical properties, a data-based model was used. To train this model, a set of optical phantoms with known optical properties was measured using the HLSI system. These diffuse reflectance measurements, in combination with the BOP from the DIS, were used as an input for a metamodel, linking the BOP to diffuse reflectance profiles. The metamodel showed a good performance for a set of validation phantoms, with an R2V of 0.9977 and 0.957 in combination with an RMSEV of 0.20 cm-1 and 3.21 cm-1, for respectively µa and µs’. Nevertheless, at wavelengths with extreme BOP values, the predictions were less accurate. The prediction of apple BOP showed an expected course for the µa spectra, with absorption features of anthocyanin, chlorophyll and water. However, an incomplete separation between µa and µs’ was obtained, as µs’ still showed some distinct absorption features.^ Nevertheless, at wavelengths with low absorption, the estimation of µs’ was according to expectations. In addition, the same evolutions in BOP as found with the DIS setup were also found with the non-destructive HLSI technique. Though, no clear relation was obtained between apple quality and the estimated BOP. Possibly, the low variability of both SSC and firmness during maturation, together with a high variability amongst apples from the same harvest day, could have complicated the prediction models. Moreover, still mostly information on the changes of apple pigments was used in the models.Next, two bovine muscles were measured using the DIS as well. Both the longissimus lumborum (LL) and the biceps femoris (BF) were considered, while the BF was further divided into an outer and inner part, due to two-toning. Clear absorption features of myoglobin, mainly oxymyoglobin at 544 nm and 582 nm, and water were found in the µa spectra.^ A higher absorption of myoglobin was found in the BF samples, while also a higher µs and a lower g was found in this muscle compared to the LL. The inner and outer BF showed significant scattering differences, possibly related to an increased degree of protein denaturation in the inner BF. During wet aging, when meat tenderness increases, a decrease in the measured g was noticed in both muscle samples.When measuring muscle samples using spatially resolved spectroscopy, anisotropic light scattering by the present muscle fibers should be accounted for. By measuring muscle samples with different initial fiber orientations, it was shown that muscle fibers can change the shape of the diffuse reflectance glow spots from circular to a rhombus shape. This effect was mainly present in the samples with the muscle fibers running parallel to the measurement surface. Moreover, the rhombus’ major axis orientation was related to the distance from the point of illumination.^ Close to the point illumination the major axis orientation was found to be perpendicular to the muscle fibers, while at larger distance a 90° shift occurred, aligning the major axis with the muscle fibers. In these samples with muscle fibers parallel to the measurement surface, the fiber orientation could be predicted based on the fitted rhombuses, with an R2P of 0.993 and RMSEP of 3.95°. These results show the importance of the 3D fiber orientation when measuring diffuse reflectance signals. Moreover, this 3D fiber orientation could possibly be determined using the obtained diffuse reflectance signals.The prediction of muscle BOP values using the metamodel also showed similarities with the DIS measurement. Clear absorption features of oxymyoglobin and water were present, while the absorption by metmyoglobin was observed as well, related to the ticker samples measured using HLSI.^ A thicker sample allows a gradient of myoglobin forms to exist, relative to different depths inside the sample. Again, larger µa and µs’ values were observed for the inner BF samples, while both the outer BF and LL samples showed lower values for µs’. During wet aging, a significant increase was obtained for the µs’ of the LL muscle. Moreover, due to the non-destructive nature of HLSI, measurements were possible through the plastic vacuum pack. Due to the lack of oxygen inside the vacuum pack, mainly deoxymyoglobin with an absorption peak around 557 nm was present. Measurements on the exact same sample during wet aging, through the vacuum pack, also showed an increase in µs’ of the LL muscle.^ As the changes in meat tenderness were most prominent in the LL muscle, it was suggested that the increase in meat tenderness could explain the observed increase in µs’ for this muscle.Finally, a limited number of wavelengths were selected to design a multispectral hand-held measurement device. Four laser diode modules emitting at wavelengths of 533.3 nm, 674 nm, 800.7 nm and 981.1 nm, were selected and mounted around a CCD camera. Using shutters, the laser light of the different modules was guided onto the sample sequentially. Again, a metamodel was built by measuring a set of liquid optical phantoms with a wide range of both µa and µs’. In validation, the metamodel showed an R2V of 0.9724 and 0.9377 in combination with an RMSEV of 0.56 cm-1 and 5.13 cm-1 for µa and µs’ respectively.^ However, for the prediction of apple and pear samples, with and without the skin, an incomplete separation between absorption and scattering properties was obtained, mainly at wavelengths with high absorption values. Nevertheless, the est

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The quality of fruit and vegetables is very important to producers, retailers and consumers. The consumer decides, based on the observed quality of the product, whether or not to buy the product. Next to the flavour and the texture of the fruit, also the appearance is a critical quality parameter. Nowadays, inspection of this appearance is achieved through visual inspection, although it is subjective, not very precise and prone to human errors. Therefore, an automation of this quality inspection would be beneficial. A promising technique for this automation is hyperspectral imaging, enabling the measurement of spectral information in all the pixels in an image.Although many researchers have demonstrated the added value of hyperspectral imaging for quality inspection of agrofood products, this technology was still rarely used in the agrofood industry at the start of this PhD research. One of the reasons was the acquisition time of spectral hypercubes which was typically high. However, this acquisition speed is considerably improved by companies like IMEC, that recently developed hyperspectral cameras with a high acquisition speed. The main reason was the large added v needed to build calibration models and the limited added value to justify the price. Therefore, in this PhD research, the focus was on improving the flexibility of hyperspectral imaging and, as the colour and colour distribution of fruit or vegetables is an important parameter to determine its quality and ripeness, on the development of a method to determine the colour of tomatoes in every pixel. In this way, a fast and contact-free method could be obtained. These concepts were elaborated for vine tomatoes, which were used as a model case containing different challenges, as they have a curved and very glossy surface and vine tomatoes consist of different components, which can vary in quality considerably.First, a semi-supervised algorithm was developed to segment hyperspectral images. It consists of three steps. First, several unsupervised algorithms are tested to split a spectral hypercube in a predefined number of comparable classes. By an operator, based on a visual inspection of the segmentation, the best split is determined. Based on this selection, from each class, a number of pixels is selected using a selection algorithm, which are used as input to develop a supervised segmentation model. This supervised model is used to segment newly measured spectral hypercubes of the same cultivar. Spectra of badly segmented spectral hypercubes are then used to augment the training set and to obtain a more robust model. After training with 10 new spectral hypercubes, the best result was achieved using a Partial Least Squares – Discriminant Analysis (PLS-DA), using a first derivative preprocessing. Applied on 5 additional spectral hypercubes, an overall accuracy on pixel level of 96.95 % for vine tomatoes and 98.52 % for table grapes was achieved. After the initial segmentation of spectral hypercubes in its present classes, the quality of fruit and vegetables can be determined.Another important quality parameter of vine tomatoes is the ripeness. Ripeness of tomatoes is linked to the colour, as during ripening the concentration of chlorophyll decreases while the concentration of lycopene increases, which results in a colour change from green to red. To measure the average colour and the variability present in each tomato, the colour of each pixel in the segmented tomatoes was determined. Therefore, two different methods are compared. The first method is by using the calculations developed by CIE to determine the L*a*b*-values. This method was suited for flat, matte samples, but in the case of vine tomatoes, which are curved and very glossy, the results were not accurate. Therefore, a databased method is presented. This method was suited to determine the hue-angle (R² = 0.95), the a*-value (R² = 0.93) and the L*-value (R² = 0.86) accurately. However, the disadvantage of this method is that it can only be used to determine the colour of comparable tomatoes.Next, it was investigated if the colour of a batch of tomatoes during storage could be predicted based on a measurement shortly after harvest. First, a model was built to describe the variability of a batch of tomatoes during storage. Therefore, mixed effects modelling was used. To describe the colour evolution accurately, 2 random effects were needed. As an analytical solution in this case is very difficult, a data-driven approach was developed. By using the algorithm to determine the hue of tomatoes, the hue in every pixel of the tomato in the image could be accurately determined. Next, a distribution was fitted over all the results of each tomato. The mean and skewness of this distribution was determined and used, together with the time after harvest and the time at which the colour should be known, as input in a multiple linear regression. This made it possible to predict the hue of tomatoes at a certain moment in the near future, until 10 days after harvest, with an R² of more than 0.80 and a RMSE of less than 9°.High quality products need to be free of defects. As a large variability in surface defects can occur, training a method to detect all possible defects is very difficult. Therefore, an algorithm was developed to detect defects on tomatoes by training a model to know good quality tomatoes. Defects are detected as outlying from this good quality. Resulting from an analysis of the spectra of the different tomatoes, the wavelength range between 700 nm and 985 nm was selected and an area-normalization was used as preprocessing. Then, the preprocessed spectra of each individual tomato was used as input of a Principal Component Analysis (PCA), so defective areas could be detected based on a combination of Hotelling’s T²-values and Q-residuals, evaluated by comparing a local difference against a threshold value. After optimisation of this threshold, the algorithm is able to detect puncture damage, but the detection of bruises and cutting damage is more difficult. It could be observed that specular reflections had a large influence on the detection result. By using a cross-polarized hyperspectral setup, it was possible to reduce this effect, reducing the numbers of false-positive detections.The algorithms described above are an important step towards a more easy, intuitive application of hyperspectral imaging. To improve the results achieved by the defect detection algorithm, the initial segmentation should be optimised. It is important that the number of misclassifications resulting from the semi-supervised segmentation algorithm is as low as possible, as these are an important source of misclassifications during the defect detection. Next, methods which are able to account for the curvature of the products and the presence of specular reflections should be developed, as they lead to low accuracies achieved when measuring the colour using the equations described by CIE. A possible solution that should be investigated, is the application of the cross-polarised illumination to reduce the effect of specular reflections on the colour calculations. To ensure that the developed algorithms are robust, they should also be tested on different products, like grapes or cranberries.

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Due to the growing world population, the agricultural sector needs more productive and more energy-efficient agricultural machinery in order to adequately address the growing demand for food and biomass. Therefore, the sector significantly invests in the optimization of agricultural machinery. Historically, the optimization of agricultural machinery was done by trial and error. Design improvements of agricultural machinery are still often based on the experience and the insights of engineers and farmers. To test whether an adjustment has a positive effect, a prototype is developed and validated during field tests. This optimization method, however, has some disadvantages. Developing and constructing a prototype is costly. Moreover, prototypes can only be validated in field conditions during the growing season. These two factors oblige agricultural machinery manufacturers to opt for small adaptations with a high success rate. The current generation of agricultural machinery is, therefore, the result of decades of evolution. Now that computing power increases, another opportunity to improve the design of agricultural machinery presents itself. Models and simulations facilitate the optimization of machines. However, an accurate virtual crop model is missing. A simulation model that accurately describes the interactions between individual crop stems and the interactions between crop stems and machine components could be used to improve the design of stem processing agricultural machinery. In this thesis, such a simulation model was developed for the processing of crop stems in a baler. It has previously been shown that the Discrete Element Method (DEM) can be used to simulate and optimize particulate processes. For this, two requirements need to be met. Realistic particle geometries and realistic deformation models are required to obtain accurate simulation results. The virtual crop stems, therefore, need to be compressible and bendable. Also the frictional and tensional properties need to be realistic. A first step in the development of the DEM simulation model included an analysis of the bending behaviour of crop stems. It was observed that there are two phases during bending: ovalisation and buckling. The forces that occur during ovalisation result in a flattening of the cross-section of the stem and this reduces the bending resistance. This process continues until the maximum force has been reached and the stem buckles. Buckling is associated with a strong reduction of the resistance to bending. The influence of the stem diameter, the thickness of the stem wall and the presence of a core-rind structure were examined for wheat and barley stems. All were found to affect the bending behaviour significantly.The acquired knowledge was used to develop a data based bending model for flexible particles (i.e. crop stems) in DEM. The influences of the stem length, the support distance and the number of segments which make up the virtual stem, were examined. The same data based method was also used for developing a compression model for virtual stems in DEM. For this purpose, the interactions between individual stems and the interaction between a stem and a plate were studied and modelled. The models were successfully validated by comparing bulk compression simulations and measurements. For this purpose 250 stems were compressed in a box by the movement of a plunger. To study the influence of friction on the processing of stems, measurements were performed on the stem level. The measured coefficients of friction were significantly lower than those found in the literature, which have been measured on a bulk level. The influence of friction on bulk compression was evaluated and it was found that a small change in the coefficient of friction at stem level has a significant effect on the bulk behaviour. The last stem parameter that was studied was the tensional stiffness. Stem measurements were again performed for this purpose. The force was found to increase linearly with increasing deformation up to the point where the stem broke. A linear tensional model was therefore implemented in DEM. Afterwards, the influence of the tensional resistance on the bulk deformation behaviour was examined. The effects of the tensional model parameters were found to be very limited. Therefore, the model parameters of the tensional model were selected in such a way that the computation time was minimized.The effect of strain rate on the force-deformation behaviour at the stem level was studied using a pendulum device. However, no significant effects could be observed for the tests at low and high speed with the used set-up. When the stem properties were measured and after they were modelled in DEM, the influence of the stem variability (e.g. the variability in physical and mechanical properties) on the bulk deformation behaviour was determined. To this end, simulations were performed with different degrees of variability. As a validation, bulk compression tests were performed. It was observed that a limited number of stem measurements can be sufficient to obtain accurate DEM simulations. As more stems are measured and as the stem database becomes larger, the accuracy of the simulations increases. However, the accuracy gained by measuring additional stems decreases with an increasing crop database. A statistical method was therefore presented to determine the minimum number of stem measurements needed to obtain accurate DEM simulations. When the behaviour of crop stems was fully characterized and modelled and after insights were obtained on the influence of stem variability, DEM simulations were performed regarding the processing of crop stems by the rotor of a large square baler. First, a method was developed to create virtual swaths. Scalability was demonstrated with these swaths. This reduced the computation time of thesimulations. Again, friction was found to have a significant impact on the crop processing as a higher coefficient of friction led to a higher energy consumption. When stems are damaged, less energy is required for their processing. Also, the feed rate was found to have an influence. The energy demand increased as more stems were processed simultaneously. Finally, the shape of the swath was also found to have a major impact on the required torque. An evenly filled swath was found to require less processing energy than an unevenly filled swath. In a final step, the filling of the pre-compression chamber was also simulated and successfully validated with stationary measurements. An increasing swath mass, the presence of the trip sensors (determining when the the pre-compression chamber is full) and a reduced rotor speed were found to have a negative impact on the required energy. The crop flow in the simulations was visually compared to the crop flow in the measurements. A high-speed camera was used for this purpose. The crop flows were found to be similar. However, a quantitative analysis should be performed to confirm this. The simulation model is now ready for the optimization of the design of the pick-up and feeding sections of the agricultural baler. The knowledge that was gained in this dissertation is more broadly applicable and could, for example, also be used for optimizing sections of the combine and the forage harvester. However, more research is required to accurately simulate nodes, leafs and ears. The influence of the strain rate and the number of stem measurements required to obtain accurate DEM simulations of crops should also be studied in more detail. Modelling the cutting and breaking of stems would have a positive effect on the accuracy and the applicability of the simulations. Since in many processes air currents have a major impact, a coupling with a CFD software is also necessary.

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Goede vruchtbaarheid van de melkkoe en oestrusdetectie zijn in de melkveesector van economisch belang. Daarom is er de afgelopen jaren uitgebreid onderzoek uitgevoerd naar sensortechnologie en -automatisatie in de vruchtbaarheidsmonitoring. Het monitoren van progesteron is hierbij een methode met potentieel omwille van zowel het beeld dat progesteron geeft van de fysiologische achtergrond van de vruchtbaarheid als de mogelijkheid tot meting in de melk. Monitoring van de vruchtbaarheid op basis van progesteron ging tot nu toe altijd via het modelleren van progesterondata. Recent heeft Adriaens, I. een model met dit doel ontwikkeld dat enkele nadelen die vorige modellen hadden, te boven komt. In deze thesis is er bekeken of er mogelijkheden zijn tot het verbeteren van dit nieuwe model door het incorporeren van invloedsfactoren op het progesterongehalte. Meer specifiek wordt de mogelijke invloed bekeken van tussenmelktijd, vetgehalte en melkgift. Deze studie is uitgevoerd met behulp van progesteronmetingen in de melk bij elke melking. Om het effect van deze invloedsfactoren te bekijken zijn een aantal mogelijke relaties tussen het progesterongehalte in de melk en deze invloedsfactoren bestudeerd. Voor de tussenmelktijd is nog geen duidelijke relatie met het progesterongehalte in de melk aangetoond. Het vetgehalte vertoont hierbij een positieve correlatie met het progesterongehalte in de melk. Van de melkgift kan de relatie met het progesterongehalte eveneens niet eenduidig aangetoond worden. Er zit in melkgift en vetgehalte echter wel potentieel om na verder onderzoek toch in de modellering van oestrale cycli opgenomen te worden. Omwille van het belang van meer traditionele oestrusdetectiemethodes in de melkveehouderij zijn in een tweede deel visuele tochtobservaties vergeleken met progesterondata en is de invloed van stress op oestrussymptomen bestudeerd. Hierbij kon er geen coherente relatie afgeleid worden tussen de modelparameters en de visuele tochtscores. De mate van stress die aanwezig is geweest, had daarnaast ook geen duidelijke invloed op oestrussymptomen.

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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.