# Systematic Mapping Study on Remote Sensing in Agriculture
## 2 Research Methodology
### Data Extraction Strategy
It is particularly relevant the use of nitrogen (N), which has proved to affect the leaf and plant reflectance signatures [17]. * Growth vigor. Monitoring plant vigor during the different stages of growth is another of the principal applications of RSA [23]. It can be based on different parameters such as the growth of the plant height, the total biomass, and the PGC. We distinguish this category from the parameter estimation in that these works perform a temporal analysis of the images. * Detection of pathogens, diseases, and insect pests. Early detection of these problems can help reduce losses. Precision agriculture systems are able to reduce pesticide use by performing site-specific spraying [102]. Thus, the effectiveness of these systems is related with the obtained quality, yield and sustainability of the crops. * Weed detection. The appearance of weeds is another problem that can appear during the cultivation process, leading to a reduction in the water and nutrients available for the crops of interest [103]. As weeds are also plants, the distinction between crops and weeds must be done using color, texture, shape, or spectral features. * Yield prediction. Regarding the last stages of the cultivation process, remote sensing images have been used to predict the yield before the actual harvesting [58]. These systems are usually based on regression models using parameters extracted from the images, although the most precise methods use accumulated temporal information and crop growth models. * Automatic crop harvesting. Intelligent harvesting machinery and picking robots have emerged in the last years as a feasible alternative to traditional harvesting methods [80], although the first experimental systems for automatic harvesting using machine vision date back to the 1980s. * **MQ8**. Computer vision systems in agriculture are not exclusively based on the use of visible light; a wide range of the electromagnetic spectrum has shown to be effective in different RSA applications, normally is frequencies lower than the visible wavelengths. Several reviews have analyzed the suitability of spectral information in different RSA problems [17; 80; 104]. The main types can be classified as follows. * RGB (visible spectrum). The visible spectrum corresponds to the wavelengths between 380 and 740 nm, which are visible by the human eye [105]. RGB cameras do not capture a complete spectrum of these wavelengths, but only three bands corresponding to red, green, and blue color. The main advantage of this category is the high availability, high spatial resolution, and low cost of the cameras, with respect to the other types of sensors. For these reasons, it is the predominant class in computer vision in general. * Red edge spectrum. This class corresponds to a small part of the visible spectrum, located at the end of the lowest frequencies, approximately from 670 to 740 nm. It is particularly important in agriculture [104], as the chlorophyll contained in vegetation reflects most of these wavelengths, while it absorbs a great part of the rest of the visible spectrum. Therefore, several vegetation indices have been defined based on the relationship between the reflection of red edge and red. * Near-infrared (NIR) and Vis-NIR. NIR includes the part of the infrared spectrum nearest to the visible region, approximately from 740 to 1500 nm. This class is also characterized by a high reflectance by the plants. The normalized difference vegetation index (NDVI) [23] is based on NIR and red bands, and is a very common parameter to study the amount
and healthiness of vegetation. Consequently, most works include NIR and visible bands, being a typical range from 400 nm to 1500 nm; this is usually called visible-NIR or Vis-NIR. * Short-wave infrared. The term infrared refers to a broad slice of the electromagnetic spectrum ranging from 740 nm to 1 mm [93]. It is subdivided in near, short, mid, high, and far infrared, from lowest to highest wavelength.