# Systematic Mapping Study on Remote Sensing in Agriculture
## 4 Discussion
### Main Findings and Implications for Researchers and Practitioners
One example of these public resources is EuroSAT [40], a public dataset of 27,000 labeled and georeferenced images from Sentinel-2 satellite useful for the classification of land usages; the DeepSat Airborne Dataset [94], with 500,000 image patches in Vis-NIR range; or the Copernicus Programme, which offers satellite and in-situ images for land monitoring ([[https://land.copernicus.eu/](https://land.copernicus.eu/)]([https://land.copernicus.eu/](https://land.copernicus.eu/))). * The majority of the selected empirically evaluated publications were conducted through experiments. Only two case studies have been identified in the selected papers, which means that it is difficult to perform this type of studies in RSA, as normally the research is done under uncontrolled settings. The small number of meta-analysis papers that were found indicates that there is an interesting opportunity to apply this type of statistical analysis whose purpose is to combine the results of multiple previous scientific works to assess these results and derive conclusions. * The computer vision task most frequently found in the selected papers is classification: given a image patch or region, classify it into a predefined set of classes of interest. This is an expected observation, as it is one of the most studied machine learning problems, it has a simple a clear definition, and its results can be used in different applications. Decision trees, support vector machines, classical neural networks, k-nearest neighbors. and Bayes classifiers are among the most frequent techniques. However, deep learning methods are gaining popularity [20; 74; 76; 77; 78; 79], proving to overcome other techniques in many domains. It is recommended that it should be applied when it is really of interest and not as a fad. An interesting alternative could be the use of ensemble classification systems that have not been widely used in RSA research. Other problems that have been identified in the selected papers include feature extraction, maximum likelihood, image preprocessing and segmentation, and recognition systems. As a general recommendation, we advice to make use of free tools and libraries for machine learning and computer vision, taking advantage of the great effort done by the free software community, for example using Python with tools such as the scientific programming environment Scikit-Learn ([[https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)]([https://scikit-learn.org/stable/](https://scikit-learn.org/stable/))) and deep learning ecosystem PyTorch ([[https://pytorch.org/](https://pytorch.org/)]([https://pytorch.org/](https://pytorch.org/))). * The main types of platforms employed in RSA to capture the images are satellites, UAVs, and manned aircraft. However, in-field cameras and ground vehicles have not been widely used in RSA. The reason these images are used instead of the ground-based ones could be explained by the fact that they provide a broader view of the land. In addition, the resolution of the cameras permits to go from a global perspective to a more detailed view of a specific area. The fusion of satellite and UAV imagery [207] is an emerging field that would be very useful to harness the power of both capture systems. It is interesting to observe that many satellite imagery is freely available for research purposes, so this would be a convenient source for beginners.