Agrotech – A new revolution is coming to Europe´s farmlands and crop zones..

Where is the opportunity?

There are large areas of land dedicated to the same crop which means an increased risk to pests and greater efforts in the fight against them. There are many plantations of corn, vines, fruit trees or simply cereals where the soils support a tremendous demand in giving the appropriate results to the farmers and growers. In the case of Spain, southern areas such as Almería produce tons of vegetables, fruits, plants of all kinds with significant water pressure since they are places where there is little rain.

Adding to that. we are facing significant rural depopulation in countries such as Italy, Spain, Portugal, France and further east Europe like Romania, Bulgaria, etc. Older people are left alone in rural settings. Moreover, young people are less and less present in those small villages and medium-sized towns surrounded by a lot of farmland.

We need an urgent answer and it is “Control” and “Automation”. We need efficiency even though we have a small staff to take care of undesirable insects, floods, droughts and fertilizers.

Why public cloud with IoT native tools and Edge computing brings the solution..

On one hand, IoT brings efficiency to the growers and farmers so they know the best moment in the season for sowing, irrigating or harvesting.

On the other hand, provide a forecast to them and a series of historical data to be able to improve their answer in the future.

Finally, you don’t need too much people to take control on vast cereal extensions for example. Even more you can program some tasks to be done automatically following a pattern of conditions.

What Offers the public cloud providers …

This picture (based on Microsoft Azure approach) shows what could be a IoT solution for Agrotech.

  1. Sensors provide data and with the help of Edge nodes which are responsible of data processing, routing and computing operation, reduce latency and provides a first repository for the data to be transmitted to the cloud. Sensor works with lots of several data formats mostly not structured but also some based on tables and well structured.
  2. IoT Hub is in charge of ingest data from the Sensors. It can process data streaming in real-time with security and reliability. It is a managed cloud solution which support bidirectional communication between devices to the cloud or the cloud to the devices. That means that while you receive data from devices, you can also send commands and policies back to those devices, for example, to update properties or invoke device management actions. It can also authenticate access between the IoT device and the IoT hub. It can scale to millions of simultaneously connected devices and millions of events per second and be aligned with your policies in terms of security, monitoring or disaster recovery to another region
  3. CosmosDB it is a globally distributed, multi-model database that can replicate datasets between regions based on customer needs. You can tailor the reads&writes of the data in several partitions at a planet scale even if you want. . This multi-model architecture allows the database engineers to leverage the inherent capabilities of each model such as MongoDB for semi-structured data (JSON files or AVRO can be perfect here), Cassandra for wide columns (for example to store data for products with several properties) or Gremlin for graph databases (for example for data for social network or games).. Hence, it can be deployed using several API models for developers. In our scenario can be use as a way to analyze large operational datasets while minimizing the impact on the performance of mission-critical transactional workloads Besides this powereful database solution, we can use Azure Synapse which is key in the transformation of the data. It is a new Azure component where you are able to ingest, prepare, manage, and serve all the data for immediate BI and machine learning needs more easily. It use Azure Data Warehouse to store historical series of data. Uses Massive Parallel Processing (MPP) to run queries across petabytes of data quickly integrating Spark engine to work with predictive analytical workloads. Azure Synapse Analytics uses the Extract, Loads, and Transform (ELT) approach. Once we have used ML, streaming or batch processing of the data ingested before it´s time to report our information according to the growers or farmers needs.
  4. Presentation Layer. You can visualize the data for example with Power BI integrated with Azure Synapse.

To summarize, IoT market is increasing rapidly. It is expected about 25 billion connected objects worldwide in 2025 following information. There is a major opportunity to transform our society and enhance our agricultural sector.

See you then in the next post…