The use of artificial intelligence techniques with advanced computer vision techniques offers great potential for non-invasive health assessments in the poultry industry. Evaluating the condition of poultry by monitoring their droppings can be highly valuable as significant changes in consistency and color can be indicators of serious and infectious diseases. While most studies have prioritized the classification of droppings into two categories (normal and abnormal), with some relevant studies dealing with up to five categories, this investigation goes a step further by employing image processing algorithms to categorize droppings into six classes, based on visual information indicating some level of abnormality. To ensure a diverse dataset, data were collected in three different poultry farms in Lithuania by capturing droppings on different types of litter. With the implementation of deep learning, the object detection rate reached 92.41% accuracy. A range of machine learning algorithms, including different deep learning architectures, has been explored and, based on the obtained results, we have proposed a comprehensive solution by combining different models for segmentation and classification purposes. The results revealed that the segmentation task achieved the highest accuracy of 0.88 in terms of the Dice coefficient employing the K-means algorithm. Meanwhile, YOLOv5 demonstrated the highest classification accuracy, achieving an ACC of 91.78%.

The trend for e-commerce, estimated population size to 11 billion by 2050, and an increase in urbanization level to 70 % is requiring to re-think the current supply chain. These trends changed the distribution process: delivery distances are decreasing, the product variety is increasing, and more products are being sold in smaller quantities. Therefore, the concept of supply chain resilience has gained more recognition in recent years. The scientific literature analysis conducted by the authors indicate several capabilities that influence supply chain resilience. Collaboration, flexibility, redundancy and integration are the most influential capabilities to supply chain resilience. However, the authors identify that the combination of these capabilities to supply chain resilience is under researched. The authors indicate that by combining these capabilities with the upcoming technologies of industry 4.0, supply chain resilience can be achieved. In the future, the authors are planning to conduct further research to identify the influence of these capabilities to supply chain resilience, to quantify supply chain resilience, and to provide further practices of industry 4.0 concept usage for supply chain resilience.

The strategies of competitive advantage are changing dramatically because of high technology development. The data size in the world is multiplying rapidly - the amount of information in the world doubles every 12 months. Therefore, the authors analyzed Big data in the food supply chain. The methodology used in the paper consists of a review of global competitiveness reports and secondary data analysis together with document-based literature synthesis; a competitiveness maximization methodology was modelled referring to a case of small markets. The supply of food industry is complicated, because of various regulations and a demand for high quality products just on time. Various companies are transporting partial freight; therefore, the visibility, lead-time and cost minimization is essential for them. However, they are unable to use all the gathered information and are not utilizing the potential that is possible. The problem of data analysis is a bigger concern to the smaller markets. Many of the small markets are less developed countries that still are not using Big data in their enterprises. In addition, new technologies are developing in the Big data industry. Therefore, the gap of technology will increase even more between large and small markets. The analysed innovation level and technology usage indicated a need for the food industry to change competitiveness strategies. Therefore, the authors developed a competitiveness strategy that is orientated to the food industry of small markets.

Consumer demand for organic products, rapidly growing urbanizations levels requires the food supply chain to reduce lead-time and maintain higher product quality. For the food supply chain to cope with the raising issues an e-commerce type of supply chain must be implemented. This approach creates challenges for supply chain, because the food industry must shift towards high variety and low quantity freight forwarding with multiple delivery points. The methodology of the paper consists of scientific literature analysis and macro indicator clustering. The author of the paper proposes a supply chain management framework, which is grounded through complexity theory. The framework mainly consists of 3 characteristics, which organizations should operationalize to maintain system resilience and which in the long-run would evolve to sustainable development–capabilities, collaboration, complexity management. The proposed framework defines how operational and tactical levels should be automated through cyber-physical systems, while the automation should be controlled through strategic level variables. The macro level analysis of existing EU markets of the food industry has been conducted to identify the food industry’s contingencies, in which an agent-based model will be used to validate the proposed framework. Main 3 clusters were identified, which number was chosen based on the elbow method and validated with the silhouette score of 0.749. The food industry can be categorized in to developing, underdeveloped, and developed food industries. Moreover, singularities of different contingencies have been identified which considers population size, population density, market size of the food industry and disruption intensity. The application of the framework depends on the identified contingencies. From strategic level the SCMF is similar in all contingencies, however, depending on the type of market, more emphasize on vehicle routing or demand forecasting should be made.