# Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture
## 4 Conclusions  
Computer vision is known to be a very powerful tool for water management in agricultural engineering. However, the use of image capture nodes in the field is limited by the environmental conditions, the requirement to be used in large areas, and the low availability of external communication systems. In this sense, ZigBee systems can be adjusted to provide an optimal solution, given their high level of scalability, their large coverage distances, and their low power consumption. The main weak point is its low data transmission speed, which in theory can be up to 250 Kbps, but must be reduced to only 19 Kbps for a reliable link without data loss.  
The experiments have successfully proved that it is possible to transfer images of VGA resolution through the XBee network, with average times of 15-22 s for images of 35-50 Kbytes. This has been achieved using the AT mode of XBee, which avoids cutting the images into small information packages required in the API mode. In that case, the image would be split in 255 bytes per packet, consuming a transmission time of 120-150 s per image.  
These results are similar to those observed by other authors, such as the performance comparison of wireless protocols presented in [36], where the LoRa and ZigBee protocols were found to be the most adequate technologies for agronomy applications. Moreover, compared to the use of drones and satellites, these systems based on inexpensive networks of fixed camera nodes can offer a greater availability of images at a lower cost of installation and operation. By contrast, they do not offer the possibility of capturing the entire crop. The captured images allow for the computation of the PGC parameter with an error around 1%, from which the crop coefficient, \(Kc\), and the water balance of the plants can be estimated.  
As the future work, there is still a long way to get a completely automatic system that estimates the irrigation needs of the crops with the obtained visual and meteorological information. For example, an important aspect is to define the pattern of automatic capture through the incorporation of weather forecasts. The time between captures could be increased or reduced, with the objective of maintaining the energy in the RICS nodes at optimum levels.  
Conceptualization, A.M.-A., J.M.M.-G.-P., A.R.-C., G.G.-M. and J.M.M.-M.; methodology, A.M.-A., J.M.M.-G.-P. and J.M.M.-M.; software, [PERSON] and [PERSON], validation, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]; investigation, [PERSON] and [PERSON]; resources, [PERSON]; writing of the original draft preparation, [PERSON] and [PERSON]; writing of review and editing, [PERSON]; visualization, [PERSON], [PERSON] and [PERSON]; supervision, [PERSON] and [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON], [PERSON] and [PERSON].  
This research was funded by the Spanish Ministry of Economy and Competitiveness (MINECO), as well as the European Regional Development Fund (ERDF) under the projects TIN2015-66972-C5-3-R and AGL2015-66938-C2-1-R. This article is the result of the activity carried out under the \"Research Program for Groups of Scientific Excellence in the Region of Murcia\" of the Seneca Foundation (Agency for Science and Technology of the Region of Murcia, Spain).  
The authors declare no conflict 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.