Let me share with you why I love agriculture and why I think that as engineers, we have the possibility to change the world using technology. I come from a family that has devoted much time to cultivating the land and grazing the herd for years. My ancestors always repeated the same patterns year after year and only made variations depending on the behaviour of the weather, but what happened? Because my ancestors were late in time/acted late, and I always heard the same sentence: “We cannot do anything against the climate; we have already done everything we can.” Similarly, I have always been interested in technology and how it can help improve the world. When I was a kid I would take apart all the tech stuff I could to understand what was inside and find clever ways to use it for other purposes. That’s why as an adult, I asked myself the question:

What are the challenges of modern agriculture?

• Enable the healthy growth of plants and make them more productive
• Achieve better productivity levels while maintaining safe working conditions for farmers
• Improve the sustainability of the fields
• Keep agriculture economically viable, to prevent farmers from abandoning their land

How can we help?

Currently the costs of new technologies have been reduced to levels that make them accessible to most of the population. IoT, Cloud and Mobile technologies are key in the transformation that modern agriculture requires. However, one of the main challenges for the introduction of technology in rural areas is connectivity. Until a few years ago, the coverage was not very good. The adoption of 4G and 5G by telephone companies as well as the emergence of new technologies/protocols such as LoRaWAN, ZigBee, Z-wave, NB-IOT, etc. now offer a suitable level of connectivity to transmit data in real time at a very low cost. One way to use technology in agriculture is to create solutions that can measure the behaviour of the earth in real time. Here IoT plays a particularly key role, measuring the humidity, temperature and conductivity at the roots of plants, as well as the temperature, humidity, pressure and rainfall at ground level. Current IoT systems allow us to collect this data in real time, but this leads us to another challenge: Do we have to store all the data?

Make this simple calculation:
We take measurements from six different sensors (humidity, conductivity, temperature …) every 15 minutes for 365 days a year. Six sensors/ha x 4 measurements per hour x 24h x 365 days = 210 240 data records/ha.

According to the FAO, currently 11% of the Earth’s land (13.4 billion ha) is cultivable, so we have 1.474 billion ha times 210 240 measurements/ha per year, making a total of 309.894 trillion.

Almost 310 trillion recordings per year (20 billion Tb/year (2Kb per measure). Could you imagine what kind of databases we would have to use or how much energy we would spend?

Obviously, the answer is: No. We need to aggregate data and analyze when it has occurred. This is where technologies such as big data, elastic search, kibana, hadoop, distributed cloud, hybrid clouds, etc. play a key role.

With all this raw and processed data, we can extrapolate using meteorological services to generate data using predictive models with Machine Learning (AWS Machine learning, Google AI, Azure ML) environments to help farmers make reasonable decisions at key moments.

To support the mass collection and processing of data, the following technologies are used: Message Brokers such as Kafka, RabbitMQ, Azure Service Bus, Google PubSub Cloud, Amazon MQ, etc.Once the data and generated models have been processed, these data must be made accessible to the user. At this point, we face another challenge: Which technology to choose? SPA, Web, Hybrid Application, Native Application, Cross-Platform Application, Angular …
Because the data that the farmer must receive on the mobile must be very specific and does not require the high-performance capabilities of mobile phones, a good option is to choose Cross-Platform development for the mobile (like Xamarin, React Native, Ionic, Flutter, etc.) and a website for computer access with more detailed information and expanded options (like Angular, React, etc.)

According to the FAO, 70% of world water expenditure is from agriculture; 50% of this expenditure can be reduced by IoT and Machine Learning systems. Moreover, by providing accurate information on the state of the land and the need for farmers to plant in real time, we can reduce fertiliser and pesticide costs between 40 to 60% and increase production between 30 to 40% based on my current experience developing these types of solutions.

We will also help to reduce the contamination of soils and water reserves since we will be consuming only the amount of fertilizer and water that crops/trees require at the times that they require it. All of this will help us design better preventive treatments that are safer than the current corrective treatments, which have a huge impact and a greater residuality on fruits. Let’s do a quick calculation again:

Fruit field (1ha):

TreatmentQuantityCostTotal Euro/ha per monthIoT savingsTotal + IoT savings Euro/ha per month
Water160 m3/ha per month0.12 Euro/m319.250%9.6
Fertilisers100 kg/ha per month1.60 Euro/kg16050%80
Phytosanitary4 treatments/ha per month90 Euro/treatment36050%180
Total costs539.2269.62
Total productionPriceTotal
Without IoT20 000 kg/ha0.90 Euro/kg18 000 Euro
With IoT30 000 kg/ha0.90 Euro/kg27 000 Euro


“We aim to make IoT technology understandable for people who spend most of their time working on the farm and are normally outside getting their hands dirty. Once we engage them and help them understand that there is nothing to fear, then they’re in.”

News from ERNI

In our newsroom, you find all our articles, blogs and series entries in one place.

  • 06.12.2023.

    Streamlining software development: The journey from multiple to unified requirements management tools

    Productivity in software development is slowed down by managing specifications across various requirements management (RM) tools. Although moving to a single, updated RM tool involves an upfront investment, the long-term benefits are considerable. These include increased process efficiency, enhanced collaboration, superior traceability, improved software specification quality, cost reductions, scalability and better integration with other RM tools, among others.

  • 22.11.2023.

    Recognising trends: An insight into regression analysis

    Data plays a very important role in every area of a company. When it comes to data, a distinction is made primarily between operational data and dispositive data. Operational data play an important role, especially in day-to-day business. However, they are not nearly as relevant as dispositive data. This is because these data are collected over a longer period of time and provide an initial insight into the history or the past.

  • 08.11.2023.

    Why do we need digital transformation for medical devices?

    For hospitals, it is not up for discussion as to whether they want to digitalise. The increasing age of the population in western countries and the progressive shortage of medical professionals mean that without digitalisation, the healthcare system will not be able to provide the quality that patients want in the future.

  • 25.10.2023.

    Mastering the challenges of mobile app testing: Strategies for efficient quality assurance

    Discover the unique challenges faced in testing mobile applications and learn how to overcome them effectively. From selecting suitable devices and operating systems to leveraging cloud-based test platforms, test automation and emulators, this article provides seven essential strategies for optimising your mobile app testing process.

  • 11.10.2023.

    Incorporating classical requirements engineering methods in agile software development for a laboratory automation system

    Traditional agile methodologies can sometimes struggle to accommodate the complexity and regulatory requirements of laboratory automation systems, leading to misalignment with stakeholder needs, scope creep, and potential delays. The lack of comprehensive requirements documentation can result in ambiguous expectations and hinder effective communication among cross-functional teams.

  • 27.09.2023.

    Unveiling the power of data: Part III – Navigating challenges and harnessing insights in data-driven projects

    Transforming an idea into a successful machine learning (ML)-based product involves navigating various challenges. In this final part of our series, we delve into two crucial aspects: ensuring 24/7 operation of the product and prioritising user experience (UX).

  • 13.09.2023.

    Exploring Language Models: An overview of LLMs and their practical implementation

    Generative AI models have recently amazed with unprecedented outputs, such as hyper-realistic images, diverse music, coherent texts, and synthetic videos, sparking excitement. Despite this progress, addressing ethical and societal concerns is crucial for responsible and beneficial utilization, guarding against issues like misinformation and manipulation in this AI-powered creative era.

  • 01.09.2023.

    Peter Zuber becomes the new Managing Director of ERNI Switzerland

    ERNI is setting an agenda for growth and innovation with the appointment of Peter Zuber as Managing Director of the Swiss business unit. With his previous experience and expertise, he will further expand the positioning of ERNI Switzerland, as a leading consulting firm for software development and digital innovation.