Wildfires are becoming more frequent in different parts of the globe, and the ability to predict when and where they will occur is a complex process. Identifying wildfire events with high probability of becoming a large wildfire is an important task for supporting initial attack planning. Different methods, including those that are physics-based, statistical, and based on machine learning (ML) are used in wildfire analysis. Among the whole, those based on machine learning are relatively novel. In addition, because the number of wildfires is much greater than the number of large wildfires, the dataset to be used in a ML model is imbalanced, resulting in overfitting or underfitting the results. In this manuscript, we propose to generate synthetic data from variables of interest together with ML models for the prediction of large wildfires. Specifically, five synthetic data generation methods have been evaluated, and their results are analyzed with four ML methods. The results yield an improvement in the prediction power when synthetic data are used, offering a new method to be taken into account in Decision Support Systems (DSS) when managing wildfires.

Remote sensing applied in the digital transformation of agriculture and, more particularly, in precision viticulture offers methods to map field spatial variability to support site-specific management strategies; these can be based on crop canopy characteristics such as the row height or vegetation cover fraction, requiring accurate three-dimensional (3D) information. To derive canopy information, a set of dense 3D point clouds was generated using photogrammetric techniques on images acquired by an RGB sensor onboard an unmanned aerial vehicle (UAV) in two testing vineyards on two different dates. In addition to the geometry, each point also stores information from the RGB color model, which was used to discriminate between vegetation and bare soil. To the best of our knowledge, the new methodology herein presented consisting of linking point clouds with their spectral information had not previously been applied to automatically estimate vine height. Therefore, the novelty of this work is based on the application of color vegetation indices in point clouds for the automatic detection and classification of points representing vegetation and the later ability to determine the height of vines using as a reference the heights of the points classified as soil. Results from on-ground measurements of the heights of individual grapevines were compared with the estimated heights from the UAV point cloud, showing high determination coefficients (R² > 0.87) and low root-mean-square error (0.070 m). This methodology offers new capabilities for the use of RGB sensors onboard UAV platforms as a tool for precision viticulture and digitizing applications.

Conservation Agriculture is an ecosystem approach to farming capable of providing solutions for numerous of the agri-environmental concerns in Europe. Certainly, most of the challenges addressed in the Common Agriculture Policy (CAP) could be tackled through Conservation Agriculture (CA). Not only the agri-environmental ones, but also those concerning farmer and rural communities’ prosperity. The optimisation of inputs and similar yields than conventional tillage, make Conservation Agriculture a profitable system compared to the tillage based agriculture. Whereas this sustainable agricultural system was conceived for protecting agrarian soils from its degradation, the numerous collateral benefits that emanate from soil conservation, i.e., climate change mitigation and adaptation, have raised Conservation Agriculture as one of the global emerging agrosciences, being adopted by an increasing number of farmers worldwide, including Europe.

The development of unmanned aerial vehicle (UAV) technology and the miniaturization of sensors have changed the way remote sensing (RS) is used, popularizing this geoscientific discipline in other fields, such as precision agriculture. This makes it necessary to implement the use of these technologies in teaching RS alongside the classical platforms (satellite and manned aircraft). This manuscript describes how The Higher Technical School of Agricultural Engineering at the University of Córdoba (Spain) has introduced UAV RS into the academic program by way of project-based learning (PBL). It also presents the basic characteristics of PBL, the design of the subject, the description of the teacher-guided and self-directed activities, as well as the degree of student satisfaction. The teaching and learning objectives of the subject are to learn how to determine the vigor, temperature, and water stress of a crop through the use of RGB, multispectral, and thermographic sensors onboard a UAV platform. From the onset, students are motivated, actively participate in the tasks related to the realization of UAV flights, and subsequent processing and analysis of the registered images. Students report that PBL is more engaging and allows them to develop a better understanding of RS.

The Initiative for Sustainable Productive Agriculture (INSPIA) project promotes best management practices for agriculture, to enhance the provision of ecosystem services through better stewardship of soil and water resources while ensuring high levels of productivity. This paper presents the INSPIA methodology for the assessment of sustainability and for guiding farmers on strategic decision-making at farm level, applicable to any kind of cropland. The methodology is based on the application of 15 best management practices, which are assessed through a set of 31 basic sustainability indicators that cover the economic, social and environmental dimensions both agreed by a panel of experts. Basic indicators are then grouped into 12 aggregated indicators, to build the final INSPIA composite index. The INSPIA methodology provides farmers and advisers with a tool to understand sustainability and which, to a certain extent, serves to improve performance toward sustainability. Results are presented in three different ways: a bar diagram with the whole set of basic indicator-values; a pie chart representing the sustainability split in the aggregated indicators; and a final sustainability index. In the medium and long term, the INSPIA methodology can help to monitor and assess agricultural and environmental policy implementation, as well as help improve its decision-making processes in the future.