# Systematic Mapping Study on Remote Sensing in Agriculture
## 4 Discussion
### Main Findings and Implications for Researchers and Practitioners
Among the most cited satellites in the selected papers, NASA's Landsat missions ([[https://landsat.gsfc.nasa.gov](https://landsat.gsfc.nasa.gov)]([https://landsat.gsfc.nasa.gov](https://landsat.gsfc.nasa.gov))), ESA's Sentinel missions ([[https://sentinel.esa.int/](https://sentinel.esa.int/)]([https://sentinel.esa.int/](https://sentinel.esa.int/))), and ESA's Envisat satellite ([[https://www.esa.int/Applications/Observing_the_Earth/Envisat/Mission_overview](https://www.esa.int/Applications/Observing_the_Earth/Envisat/Mission_overview)]([https://www.esa.int/Applications/Observing_the_Earth/Envisat/Mission_overview](https://www.esa.int/Applications/Observing_the_Earth/Envisat/Mission_overview))) can be mentioned. Other satellites that are referred in several papers include Proba-1/2, Spot, QuickBird, Ikonos, TerraSAR-X, and Radarsat-2. In the domain of UAVs, some research teams are specifically dedicated to the hardware development of capture systems that can be applied for different task,such as the system PhenoFly ([[https://kp.ethz.ch/infrastructure/uav-phenofly.html](https://kp.ethz.ch/infrastructure/uav-phenofly.html)]([https://kp.ethz.ch/infrastructure/uav-phenofly.html](https://kp.ethz.ch/infrastructure/uav-phenofly.html))), which has been used in many publications. * The main research topics addressed by the RSA community were growth vigor, cropland parameter extraction, and water usage. From a general perspective, these problems have the potential to impact on the sustainability of agriculture. As a matter of fact, the better usage of the water together with an adequate knowledge on the cropland, the better growth vigor, crop quality, and efficiency is achieved. Indeed, sustainability is one of the main goals of precision agriculture, in line with the United Nations' Sustainable Development Goals (SDG2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture. SDG14. Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss.). Sustainability was not a topic specifically addressed in the present mapping study, although all the problems are in some way related to it. The detection of pathogens, diseases, and insect pests attempts to reduce the amount of pesticides and insecticides; weed detection allows the use of site-specific spraying of herbicides, and nutrient analysis is related to the optimal use of fertilizers. On the other hand, yield prediction and automatic crop harvesting seek to optimize the productivity of farming. It cannot be discarded that new problems and applications appear in the future with the advance in technology, such as those involved in a completely automated cropping cycle. * Concerning the types of the images used, standard RGB images continue to be the most frequently used image type. This can be explained by the low cost and high availability of RGB cameras, and the fact that they are the main source for computer vision in general. In this sense, RSA research is commonly observed as a sub-domain of computer vision and image processing. New methodologies should be developed more specific to the agricultural domain, for example considering the spectral, temporal, and phenotypic dimensions. In many works, NIR channel is added to RGB tuples, forming a 4-valued tuple for each pixel. Visible spectrum allows to validate the results from simple inspection, but important information may be lost or not detected with this type of images. For example, the temperature of the objects cannot be measured with Vis-NIR images, but this could be useful to estimate the water status of the plants. In these applications, hyperspectral images and thermal infrared were found in a second level of usage, and on a third group near infrared, red edge spectrum and multispectral images. When the spectral bands of interest can be known a priori, multispectral images can be more interesting than hyperspectral, focusing on the wavelengths of interest.