Plantae has been using satellite imaging technology provided by Copernicus' European satellites for over a year. With it, agronomic consultancies have been carried out, sensor installations defined and planned, and studies of the hydric and vegetative behavior on farms have been carried out through the indices NDVI and NDWI, among other parameters.
Copernicus Accelerator is a platform that offers support and advice to innovative companies to help them improve their solutions and technologies .
For this, it has created an ecosystem of experts who evaluate the requests of companies interested in having its launch and advisory services, checking their quality and relevance.
Plantae has the honor of being selected by Copernicus Accelerator, participating in the inauguration of the program on December 2 and 4 in Helsinki (Finland).
Plantae Goals with the Copernicus Accelerator Program
Plantae will be tutored and advised for 12 months by mentors specialized in image satellite , machine learning and IoT.
Our company has undergone several selection processes to become part of this program, also positioning itself as one of the next candidates to enter the program Copernicus Incubator 2020 (winners only 6 companies out of 150 at European level and where Plantae is already in 8th place in the ranking ).
Plantae already uses its own communication protocols based on IoT for the interaction of its devices and satellite image to optimize and plan its facilities, but our growth and international projection make it necessary to automate certain processes in order to meet current demand.
Copernicus Accelerator Goals
Introducing all these technologies through experts and mentors dedicated to supporting Europe's new generation of intrepid innovators and with them, Plantae.
The way to do this is through two bootcamps (intensive courses), at the beginning and end of the project and monthly online connections to:
- Automatically obtain data from satellite images on the Plantae platform.
- Improve the frequency of reading and its level of detail.
- Aspire to other European projects related to precision agriculture.
The event started in the European space week, an event that brings together companies, politicians and experts in space applications. There, information was discussed on different programs such as Copernicus and Galileo, problems of European startups, new technologies and approaches related to a sustainable Europe.


Meritxell Gimeno, CEO of Draco Systems (Plantae's mentor at the Copernicus Accelerator) and Samuel López, CEO of Plantae, in Helsinki.
What are these technologies?
- Machine Learning or machine learning is a scientific discipline within the field of Artificial Intelligence (AI) that consists of training our system so that it is capable of learning to solve complex problems based on certain input data, It will help us to customize cultivation techniques, production optimization, detection of pests or anomalies, etc. thanks to the data received in real time.
In short, Machine Learning will help us go from being reactive to being proactive in some aspects of counseling. For this, help such as Copernicus Accelerator is necessary to project their experience in our project.

- Big Data is the set of data, whose variability and speed of growth make it difficult to store and further process it. Understanding how this data can help us make more accurate predictions (relying on Machine Learning algorithms) will be Plantae's great challenge in the coming months.
Big Data analysis, such as CMR, helps to:
- Cost reduction.
- It favors decision making.
- Measure the needs of customers and promote their satisfaction.

- IoT or internet of things ( Internet of Things) is the basis of communication between the different Plantae devices. Investing in R&D has allowed us to significantly reduce data exchange between devices. Now, n our intention with the Copernicus program is to further increase the security in the exchange of the same without adding latency in our system, since for us real time is the most important thing in decision making.

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