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Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods. However, these methods are scattered over different studies impeding the selection and application of the feasible approaches. This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. We identified and analyzed the advances in the corresponding attention mechanism-based deep learning (At-DL) methods. A systematic literature review was performed to identify the trends in publications, publishers, improved DL methods, data types used, attention types used, overall accuracies achieved using At-DL methods, and extracted the current research directions, weaknesses, and open problems to provide insights and recommendations for future studies. For this, five main research questions were formulated to extract the required data and information from the literature. Furthermore, we categorized the papers regarding the addressed RS image processing tasks (e.g., image classification, object detection, and change detection) and discussed the results within each group. In total, 270 papers were retrieved, of which 176 papers were selected according to the defined exclusion criteria for further analysis and detailed review. The results reveal that most of the papers reported an increase in overall accuracy when using the attention mechanism within the DL methods for image classification, image segmentation, change detection, and object detection using remote sensing images.

Although there is a plenitude of scientific literature describing the losses in productivity that are caused by infections of cattle with gastrointestinal (GI) nematodes and the liver fluke (Fasciola hepatica), only few attempts have been made to convert these production losses to an economic cost. In addition, these economic assessments typically estimate the global cost of infection in a specific region. The value of such studies is limited when the purpose is to offer economic guidance in making decisions at the farm-level. The objectives of this paper were 1) to develop a tool for the veterinarian/herd advisor to estimate the herd-specific costs of GI nematode and liver fluke infections in dairy herds; 2) to apply the tool on Belgian dairy herds and 3) to evaluate the user experiences from a selected number of veterinarians. The developed tool consists of a standardized spreadsheet model with 6 steps: (1) identification of animals 'at risk' of production-limiting helminth infections; (2) assessment of the helminth infection status of the herd by measurement of serum pepsinogen and indirect ELISA results; (3) assessment of anthelmintic treatment usage and related costs; (4) assessment of the effects on production of helminth infections; (5) estimation of the monetary value of production outputs and (6) aggregation of the results. The tool was applied on data from Belgian dairy herds. Unlike previous studies that provide only a global estimate, this approach enabled us to assess the potential costs for a farmer and propose a minimum attainable disease cost. This could be used as a target value for farmers. The users evaluated ParaCalc ® to be a useful tool to raise the farmer's awareness on the costs of worm infections, offering added value for their services. Future improvements and developments are discussed.

Helminth infections of cattle affect productivity in all classes of stock, and are amongst the most important production-limiting diseases of grazing ruminants. Over the last decade, there has been a shift in focus in the diagnosis of these infections from merely detecting presence/absence of infection towards detecting its impact on production. This has been facilitated by studies observing consistent negative correlations between helminth diagnostic test results and measures of productivity. Veterinarians are increasingly challenged to consider the economic aspects of their work, and the use of these tests should now be integrated in economic evaluation frameworks for improved decision making. In this paper, we review recent insights in the farm-specific economic impact of helminth infections on dairy cattle farms as well as in farmer attitudes and behaviour regarding helminth control. Combining better economic impact assessments of helminth infections together with a deeper understanding of the non-economic factors that drive a farmer’s animal health decisions should result in more effective control strategies and increased farmer satisfaction.

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A large part of the world's resources are used to produce animal products. Efficient use of these resources is important to improve social well-being. Endemic animal diseases decrease production efficiency, because they require a higher level of input to produce the same amount of output or result in a lower output with the same amount of input. The optimal level of production with and without disease differs from farm to farm and depends on varying economic circumstances. Given these difficulties, making an accurate theoretical estimation of the economic impact of endemic diseases is challenging. Current approaches towards the economic assessment of endemic diseases are, therefore, quite pragmatic. For on-farm decision-making, the total costs consist of failure costs and preventive costs. Failure costs are associated with production losses (i.e. decreases in milk production, mortality and culling), treatment costs (i.e. veterinary treatment, drugs, and discarded milk) and the use of other resources associated with the occurrence of disease (i.e. increased labour costs). Preventive costs are associated with preventive measures in terms of equipment, consumables (e.g. diagnostics and chemicals) and the use of other resources to prevent diseases (i.e. increased labour). There is a substitution relationship between failure costs and preventive costs. That means that, in order to maximise profit at the farm level, the amount of resources invested in prevention should be chosen in such a way that total costs are minimised. The most studied endemic disease in animal production is mastitis. Most publications on mastitis only assess failure costs, and studies on assessing the total costs and best methods to determine an optimal level of prevention are scarce. Future challenges lie in researching frameworks that can assist decision-makers to establish optimal prevention levels for endemic diseases.

Accurate forecasting of dairy cow milk yield is useful to dairy farmers, both in relation to financial planning and for detection of deviating yield patterns, which can be an indicator of mastitis and other diseases. In this study we developed a dynamic linear model (DLM) designed to forecast milk yields of individual cows per milking, as they are milked in milking robots. The DLM implements a Wood's function to account for the expected total daily milk yield. It further implements a second-degree polynomial function to account for the effect of the time intervals between milkings on the proportion of the expected total daily milk yield. By combining these 2 functions in a dynamic framework, the DLM was able to continuously forecast the amount of milk to be produced in a given milking. Data from 169,774 milkings on 5 different farms in 2 different countries were used in this study. A separate farm-specific implementation of the DLM was made for each of the 5 farms. To determine which factors would influence the forecast accuracy, the standardized forecast errors of the DLM were described with a linear mixed effects model (lme). This lme included lactation stage (early, middle, or late), somatic cell count (SCC) level (nonelevated or elevated), and whether or not the proper farm-specific version of the DLM was used. The standardized forecast errors of the DLM were only affected by SCC level and interactions between SCC level and lactation stage. Therefore, we concluded that the implementation of Wood's function combined with a second-degree polynomial is useful for dynamic modeling of milk yield in milking robots, and that this model has potential to be used as part of a mastitis detection system.