## 7 Final Remarks and Future Challenges
In the comparison between the subject matter most frequently addressed at the beginning of the twentieth century, exclusively using satellite images at low and medium resolutions, and more recent topics such as phenotyping and disease detection, which benefit from very high satellite and UAV spatial and temporal resolutions, the gradual technical advancement of RS in the field of agri-environmental analyses is revealed. The strengths and weaknesses of satellites and UAVs are evident when the goal is to detect disease symptoms early. UAVs are not the ideal platform for monitoring large areas, but they are generally able to provide images with a higher spatial and temporal resolution, especially in the case of thermal images, so they are useful in the monitoring of olive diseases such as Xlyella and Verticillium at early stages [183]. UAVs can be used in two stages of disease control: in the early stages by detecting a possible infection before visible symptoms appear, or mapping the infection's size in different parts of a crop. In the latter case, the farmer will have a clearer map of the affected areas and support in making early decisions to reduce losses. At intermediate-advanced stages, UAVs can be used during the treatment of the infection for targeted spraying and monitoring [239]. On the other hand, as shown in [218], images from the Sentinel-2 (available with a frequency of 2-3 days) can be useful for large-scale monitoring of the spread of Xf at intermediate to severe stages. As stated by [PERSON] et al. and [PERSON] [9; 240], results from using different types of sensors over an area, including proximal, UAV-mounted, aircraft-mounted, and satellite-mounted indicate benefits from complementary rather than competitive use. Several authors have proposed combining images acquired with different platforms as UAVs and satellites and at different spatial scales aiming at a data fusion approach to improve stress detection capability [9; 241]. In addition, combining RS data with different types of data, including weather information, historical data on disease incidence, and information on agronomic practices in the field, will allow for more efficient disease monitoring [199]. These considerations are valid not only in disease detection. Generally, the data fusion from remote and proximal sensors should be coupled with well-defined protocols by which spatial congruence between the sensors and the data provided by the laboratory's analysis is achieved. Regarding the data field collection, an important contribution can come from the Internet of Things (IoT), which allows \"on-site monitoring\" remotely farms [242]. IoT has increasingly developed thanks to scientists, researchers, and engineers implementing new technologies, methods to monitor crops, and facilitating access to agricultural data (such as crop-related and meteorological data) critical in the analysis and prediction of phenology and implementation of the yield forecasting models [123; 128]. This momentum offers many possibilities for optimizing procedures and adapting these technologies in PA [243]. However, the current trend sees PA products primarily directed towards large area farms and agricultural companies equipped with advanced machinery and tools, with technology providers and data companies seemingly uninterested in collecting and processing small farm data [244]. Given the complexity of image processing methods and the expertise required for RS data management, it is necessary to develop a reliable yet simple workflow for real-time data analysis and extraction. For example, artificial intelligence techniques, such as machine learning, could generate spatially and temporally continuous data from satellites at a scale necessary for PA applications [245]. Facilitatingthe acquisition and processing of RS data is also an important challenge in increasing the pool of users able to access and use RS technologies in agriculture [122].