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Case study card:

Waste and energy reduction in herb and lettuce production

Waste and energy reduction in herb and lettuce production


Primary Industries and Infrastructure

Business Challenge:

Svegro is Sweden's largest producer of ecological herbs and lettuces, located at the Färingsö island just outside Stockholm city. The company cultivates some 20 different crops on 55,000 square meters of green house area with millions of pots harvested each year. These are sold to supermarkets, grocery stores and restaurants all over the country. Although Svegro are justifiably proud of their low wastage, they still want to drive those down even further, while also minimizing energy costs.

Inspired by pioneering research into data-driven agriculture, Svegro turned to our supplier aiming to introduce data- and AI-driven methods into the process for growing their best selling crop: basil. The project was co-financed by Svegro and Vinnova through the latter's "Start your AI journey" program.

The aims were threefold:

1) To train an AI system to estimate crop height from surveillance image data.
2) To build a prognostic model that can estimate final crop height based on measurements and climate one week before time of harvest, or earlier.
3) To introduce a system for continuous crop health monitoring and anomaly detection to flag abnormal growth and plant discolouration.

Supplier Solution:
The first challenge was to integrate the data from Svegro's different sources: the climate control system, manually measured quality data, soil electric conductivity values and more. After establishing a process for continuous data updates, we moved on to evaluating possible AI solutions to estimate a plant's height based image data from standard surveillance cameras. In order to train a machine learning model to accomplish this task, we needed to label thousands of images as training data for the model.

The final solution was a deep learning model which was able to estimate the height of a plant from an image with a mean error of less than half a centimeter, even in varying lighting conditions and in different growth stages. In addition, we trained a separate object detection model that is able to detect the plants and register their age from time of seeding.

With the help of these AI models, we built a dashboard where Svegro can continuously monitor the state of their cultivation and get answers to questions such as:

How tall are the plants under each camera?
How old are they?
What size plants can we expect at harvest?
Are there any quality associated anomalies?
Suggestions on changes to greenhouse temperature in order to optimize harvest crop size.
The dashboard integrates real-time picture data with harvest forecasts and warnings about crop quality and has found immediate use crop in crop monitoring and sales planning.

Apart from the real-time dashboard, we also built a historical data analysis dashboard for retrospective analysis of the relationship between basil growth and indoor and outdoor climate. Svegro's employees frequently discuss specific batches or "seeding dates" that were especially successful or unsuccessful and try to figure out how they differed, and now they can continue doing that with the added backing of data. The dashboard allows export of climate data from arbitrary historical periods for comparison.





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