# Systematic Mapping Study on Remote Sensing in Agriculture
## 2 Research Methodology
### Data Extraction Strategy
Image segmentation consists in separating image regions in different categories [78], e.g., separating plants and background, or detecting the regions of a crop of interest. Segmentation can be considered a result by itself, or it can be the input for further processing. Feature extraction. Most frequently, after segmenting the regions of interest in the images, a set of features are extracted from them, although it can also be applied to the entire image. Feature extractors are a set of techniques to obtain relevant and high-level data from the images. The most usual types of features in RSA are color, texture, shape, and spectral features [91]. In many cases, the features are not explicitly predefined by the human experts, but they are given by a machine learning algorithm [75]. The extracted features can be used later for computing parameters of interest from the images, such as the water stress of the plants, or the crop yield. Similarity measures and maximum likelihood. Most empirical research has been dedicated to find effective similarity measures on the extracted features. Then, the similarity values can be used in a maximum likelihood approach [92]. This can be used, for example, to predict the evolution of a certain crop from other previously observed cases with similar characteristics. * Classification systems. Given an image, or an image region, classification consists of determining the most likely class among a predefined set of classes [32; 39; 40]. For example, it can be used to classify a segmented region of plants in crop or weed, it can be used to classify a plot in dry land or irrigated, or to classify a fruit in unripe/ripe/overripe. Common classifiers used in RSA include support vector machines (SVM) [69; 70], decision trees (DT), and artificial neural networks (ANN) [52], although they can also be used in the other problems. * Recognition systems. The purpose of a recognition system is to find the specific identity of the object of the given class. For example, a segmentation step can be used to separate an image in plant/background; then, a classifier is applied to find if a plant region is a tree, a grass or a weed; finally, the recognition step would determine the specific type of tree, grass, or weed [77]. Obviously, a recognizer should not be prepared to deal with all the instances from all the classes, but only for those species of interest that have been trained. * Other machine learning algorithms. In this category we include additional applications of machine learning algorithms [14]. These can include regression algorithms (e.g., for estimating the crop evapotranspiration), decision support systems (e.g., for deciding the ferritin schedules), or methods to automatize different processes (e.g., harvesting or fumigation machines). A complete computer vision system in agriculture should include many (if not all) of these techniques. Therefore, the papers have been classified according to the area where the most important contributions are done, although they could be classified into different categories. * **MQ6**. The platforms typically used to capture the images in agriculture are highly diversified [93]. They can be classified according to different criteria, such as the type of information captured (spectral or depth maps), the spatial, spectral and photometric resolution, or the type of cameras. However, they are most commonly classified considering the type of vehicles or devices in which they are mounted [80]. The main categories are listed as follows. * Satellite imagery. They are characterized for offering images of very large areas, with lower temporal resolution compared to the other platforms [94; 95]. The high cost of this kind of device places them beyond the reach of farmers, being controlled by governmental or international institutions. However, in many cases, these organizations provide free access to the obtained satellite images for research purposes. Another characteristic of satellites is that most of them are equipped with multispectral or hyperspectral cameras [96].