With digital technology in greenhouses toward a future-proof business model

For a successful transformation, it is essential to determine for each element of the (digital) operating model what the appropriate speed of implementation should be.

The demand for efficient, sustainable, healthy and locally produced food is increasing rapidly driven by several pull and push factors:

• The growing need for efficient and sustainable production is driven by macroeconomic developments such as the growing world population, and climate goals. There are also sector-specific developments such as the structurally low margins of food producers, the dominant position of supermarkets, rise of digital natives and labor shortages in horticulture. The growing demand for locally produced and healthy food is driven by the trade war, COVID-19 and changing consumer behavior
• Various technological developments that enable more efficient processes such as sensors, Internet of Things and AI create competitive pressures to continue to innovate

In order for flowers or plants to grow properly, an optimal environment is required, consisting of various parameters such as temperature, humidity, light intensity, certain levels of chemicals, etc. A greenhouse is a complex system with different components, such as crop, climate and irrigation strategies. Within this system, sensors measure various plant characteristics with optical and imaging techniques. Therefore, greenhouse farming is a data-rich environment where people try to achieve an "optimal production cycle" with many repetitive tasks. It is therefore extremely fertile ground to start working with new digital technology to exploit the data generated and meet changing demand

Combining deep knowledge of cultivation recipes in the "heads" of horticulturists with the capabilities of data, robotics and AI offers the opportunity to substantially increase the efficiency and quality of food production. With the deployment of digital technology in horticulture, the following sequential steps can be taken:

1. Describe and diagnose with dashboards - creating key insights through dashboards that continuously display a comprehensive overview of business performance. With this descriptive and diagnostic analysis, decision makers can better see what is happening and why and adjust actions accordingly
2. Yield forecasting - the combination of accurate prediction of food production with prediction of customer demand through the use of data and AI results in optimizing yields, planning, asset allocation and business strategy
3. Automating production through Robotics - industrial Robotics is deployed on internal processes where a lot of data needs to be processed and/or repetitive tasks are involved to make production more efficient and precise, to ensure product quality or reduce dependence on labor
4. Autonomous operating greenhouse through a combination of Robotics and AI - Bringing together an accurate yield prediction model with Robotics in a digital platform driven by AI and machine learning will result in (semi) autonomous greenhouses where the requested products are efficiently produced and sold at the optimal price

For a successful transformation, it is essential to make a make-or-buy choice for each element of the (digital) operating model and determine what the right speed of implementation should be. Using these two axes, four choices are distinguished:

1. collaborative innovation
2. business transformation,
3. M&A/strategic partnerships
4. Greenfield

Because an important part of the knowledge about digital technology is missing within the agribusiness sector, technology companies are trying to gain a position. In addition, private equity firms are opportunities to realize an international growth strategy with Dutch companies. These developments are resulting in an increase in M&A transactions. For example, JBR recently guided the purchase of a minority stake in Certhon by Japanese robotics specialist Denso with the aim of jointly realizing step 3.