This paper presents and explores the different Earth Observation approaches and their contribution to the achievement of United Nations Sustainable Development Goals. A review on the Sustainable Development concept and its goals is presented followed by Earth Observation approaches relevant to this field, giving special attention to the contribution of Machine Learning methods and algorithms as well as their potential and capabilities to support the achievement of Sustainable Development Goals. Overall, it is observed that Earth Observation plays a key role in monitoring the Sustainable Development Goals given its cost-effectiveness pertaining to data acquisition on all scales and information richness. Despite the success of Machine Learning upon Earth Observation data analysis, it is observed that performance is heavily dependent on the ability to extract and synthesise characteristics from data. Hence, a deeper and effective analysis of the available data is required to identify the strongest features and, hence, the key factors pertaining to Sustainable Development. Overall, this research provides a deeper understanding on the relation between Sustainable Development, Earth Observation and Machine Learning, and how these can support the Sustainable Development of countries and the means to find their correlations. In pursuing the Sustainable Development Goals, given the relevance and growing amount of data generated through Earth Observation, it is concluded that there is an increased need for new methods and techniques strongly suggesting the use of new Machine Learning techniques.

Maize is an important worldwide commodity susceptible to fungal contamination in the field, at harvest, and during storage. This work aimed to determine the occurrence of Fusarium spp. in maize grains produced in the Tagus Valley region of Portugal and the levels of related mycotoxins in the 2018 harvest and during their storage for six months in barrels, mimicking silos conditions. Continuous monitoring of temperature, CO₂, and relative humidity levels were done, as well as the concentration of mycotoxins were evaluated and correlated with the presence of Fusarium spp. F. verticillioides was identified as the predominant Fusarium species. Zearalenone, deoxynivalenol and toxin T2 were not found at harvest and after storage. Maize grains showed some variability in the levels of fumonisins (Fum B1 and Fum B2). At the harvest, fumonisin B1 ranged from 1297 to 2037 µg/kg, and fumonisin B2 ranged from 411 to 618 µg/kg. Fumonisins showed a tendency to increase (20 to 40%) during six months of storage. Although a correlation between the levels of fumonisins and the monitoring parameters was not established, CO₂ levels may be used to predict fungal activity during storage. The composition of the fungal population during storage may predict the incidence of mycotoxins.

This paper presented a review on the capabilities of machine learning algorithms toward Earth observation data modelling and information extraction. The main purpose was to identify new trends in the application of or research on machine learning and Earth observation—as well as to help researchers positioning new development in these domains, considering the latest peer-reviewed articles. A review of Earth observation concepts was presented, as well as current approaches and available data, followed by different machine learning applications and algorithms. Special attention was given to the contribution, potential and capabilities of Earth observation-machine learning approaches. The findings suggested that the combination of Earth observation and machine learning was successfully applied in several different fields across the world. Additionally, it was observed that all machine learning categories could be used to analyse Earth observation data or to improve acquisition processes and that RF, SVM, K-Means, NN (CNN and GAN) and A2C were among the most-used techniques. In conclusion, the combination of these technologies could prove to be crucial in a wide range of fields (e.g., agriculture, climate and biology) and should be further explored for each specific domain.

Process monitoring is a key aspect of industrial automation nowadays and paves the way towards unmanned machine operation. Machine learning has been favoured in condition monitoring since it provides the ability to model even the most complex processes. This paper presents an approach concerning feature selection and process monitoring using decision trees with continuous variables. Through the use of time as well as other commonly used features condition monitoring of the turning process is evaluated. Results demonstrate the applicability of decision trees to multi-sensor process monitoring with underlying continuous variables. This approach revealed that decision trees can be applied for feature selection purposes in a simple and transparent fashion even in the presence of noisy data.

Supply chain is an integrated process in which a group of several organizations, such as suppliers, producers, distributors, and retailers, work together, and where activities such as procurement, production, distribution and demand planning must be addressed. The selection of suppliers is one of the most crucial activities in supply chain management and is conditioned to factors such as lead time, responsiveness, and capacity. This chapter presents an overview of the state of art techniques regarding optimization of supply chain management focussed on the selection of suppliers and order allocation as well as optimization objectives and includes some practical applications. Apart from presenting some of the most common problem categories and optimization techniques, a comparison is provided suggesting the growing importance of heuristic and metaheuristic-based artificial intelligence techniques, given the increased complexity of supply chains and its non-deterministic nature.