Crazy Little Thing Called Technology
Data rich and information poor. This was a concept first mentioned in 1983, in one of the best-selling business book: In Search of Excellence . In summary, the book describes that several organisations were rich in data, but they lack the process to produce meaningful and useful information to create a competitive advantage in the market.
Over the past 4 decades, the artificial intelligence industry has grown like never before. Followed by robotics, Internet of Things (IoT), edge computing and blockchain. This digital transformation has enabled technologies that we have never seen before, like self-driving and electric cars, messenger RNA vaccines, green nitrogen and many more .
However, is agriculture following this digital revolution?
To answer this question, it is necessary to perform “quick” research in the available digital tools within this sector. Don’t worry, Food4sustainability team has performed this research and we will provide information so you can answer the previous question. We classified digital tools in agriculture into 3 groups:
Remote sensing technology: Using several types of technologies and sensors that acquire data in a remote way (e.g., Unmanned Aerial Vehicles, Satellites, Multi-spectral Cameras, Laser Scanning), these technologies are among the everyday life of a farmer and can provide reliable solutions not only for data acquisition, but also to enable the farmer to save a lot of resources with precision agriculture.
In this context, remote sensing technologies can bring smart farm solutions such as variable rate the application of fertilisers, chemicals and biological compounds, detect and predict the occurrence of pests and diseases, assess the nutrient status of the crops using a near real time approach, accurate forecasting systems for temperature, weather and the occurrence of extreme events (e.g., droughts, floods, frosts), precision irrigation and water stress detection .
IoT and digitalization: IoT sensors are used to collect several kinds of data from the crops and the environment. This kind of data can be explored by the farmers to develop smart solutions in agriculture and enable them to make better decisions about crop management. The European Commission states that combining IoT real time data with remote sensing data is the key to perform precision agriculture .
Some examples of sensors that provide real time data include optical sensors (like the UAV and their cameras mentioned before), electrochemical sensors that are available to analyse several soil parameters (e.g., soil moisture, temperature, pH), positioning sensors (GPS, wireless stations) and automated sensors for spraying regulation. Several more examples can be found here .
Software development and data integration: In order to avoid the data rich and information poor situation mentioned before, it is necessary to integrate all the data provided by sensors into a single space and generate meaningful information. This is usually called a decision support system, which will help the end user to take decisions based into several layers of information.
The application of all these technologies in agriculture can significantly improve farm productivity by analysing several layers of information: soil, crop, weeds and pests, weather and occurrence of extreme events. In such a competitive sector, farmers must be aware of new technologies that are proposed to reduce the losses in agriculture and increase productivity in crops .
This is only the tip of the iceberg! If you are interested in knowing more about digital solutions in agriculture, don’t miss our series of webinars in this topic!
 Van de Ven, A.H., 1983. In Search of Excellence: Lessons from America's Best-Run Companies.
 MIT Technology Review, 10 breakthrough technologies 2021, available at:
 Sishodia, R.P., Ray, R.L. and Singh, S.K., 2020. Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), p.3136.
 European Comission, Shaping Europe’s digital future, available at:
 Moysiadis, V., Sarigiannidis, P., Vitsas, V. and Khelifi, A., 2021. Smart farming in Europe. Computer science review, 39, p.100345.
 Wolfert, S., Ge, L., Verdouw, C. and Bogaardt, M.J., 2017. Big data in smart farming–a review. Agricultural systems, 153, pp.69-80.