# Systematic Mapping Study on Remote Sensing in Agriculture
## 5 Conclusions  
Precision agriculture is a very active area of research with a significant impact on the improvement of global sustainability and the optimization of natural resources. It is based on information and communication technologies to achieve its goals, being remote image capture systems one of the main branches. This includes the development of cameras and capture devices, the remote communications of the images, image processing and computer vision tasks, and machine learning methods to automate the farming decisions.  
The present systematic mapping study has presented a quantitative and qualitative analysis of the state-of-the-art in this rapidly evolving area. Only since the year 2000, more than 1400 journal and conference papers were found, and this trend is expected to continue in the future. A selection of the 106 most highly cited papers has been done to obtain an in-depth view of the state of the research. The archetype paper is a journal manuscript describing a classification problem on a dataset of satellite or UAV imagery, using existing computer vision and machine learning techniques, possibly with minor adaptations, applied in problems of parameter estimation, growth vigor, and water usage. Standard RGB and hyperspectral images are the most frequently found, although many works use different modalities.  
Current trends are towards the popularization of the use of UAVs and the increasing availability of satellite imagery. However, we believe that a solution should integrate in-field cameras and airborne images in order to achieve high spatial and temporal resolution to cover large areas and reduce operational costs. Deep neural networks are also a very marked tendency as they can obtain excellent results in tasks of classification, segmentation, feature extraction, recognition, and analysis of time series. Finally, the integration should also be referred to the development of more holistic approaches that consider all the aspects involved in the cultivation cycle, and not just the problems in isolation.  
Conceptualization, J.A.G.-B. and S.O.; methodology, J.A.G.-B. and S.O.; validation, B.B., G.-M., and J.M.M.-M.; formal analysis, J.A.G.-B., S.O., and B.B.; investigation, J.A.G.-B. and S.O.; data curation, J.A.G.-B., S.O., [PERSON]-M., and J.L.F.-A.; writing-original draft preparation, [PERSON]-B., S.O., B.B., and [PERSON]-M.; writing-review and editing, J.A.G.-B., S.O., B.B., [PERSON]-M., [PERSON]-A., and J.M.M.-M.; visualization, J.A.G.-B. and S.O.; supervision, [PERSON], [PERSON], and [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON], [PERSON], and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Spanish MICINN, as well as European Commission FEDER funds, under grant RTI2018-098156-B-C53. This research is part of the BIZDEVOPS-GLOBAL-UMU (RTI2018-098309-B-C33) project, and the Network of Excellence in Software Quality and Sustainability (TIN2017-90689-REDT). Both projects are supported by the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund (ERDF).  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.