Deep-learning-based sensing and modeling of ever-changing agri-food characteristics

Modeling the future of plants

An ambitious new project is looking to predict and model the complex traits in fresh products that are constantly changing, using deep-learning and machine-learning approaches. The project brings together a multi-disciplinary team of Wageningen University & Research experts from Plant Physiology, Machine-Learning, and Agro-Food Robotics, and four international businesses: HortiKey International BV (NL), ioCrops (KR), Tungsram Operations Kft. (HU), and Visser Horti Systems BV (NL) with its aligned partner Corvus drones (NL).

Project lead Aneesh Chauhan, senior scientist and expertise leader of Computer Vision and Robotics at Wageningen University & Research: “We are addressing three different domains in which plants are grown: a greenhouse, an open field, and a vertical farm. If a similar type of technology could be applicable to these three different domains, we would demonstrate that the use of these technologies goes beyond just solving one problem. So the set of technologies that we develop in this project could become something to be used across domains, across the entire agricultural and horticultural sector.”

Three practical challenges
The sense of urgency coming from the market has been there for a long time. Modeling changing traits of fresh products is an extremely challenging issue. “What we are trying to do is translate those real industry challenges and try to solve them with the latest technological innovations at Wageningen University & Research,” says Aneesh.

To do so, the project is divided into three work packages, running 3-4 years:
 
Transferring knowledge between cultivars for yield prediction
HortiKey International are looking to advance the yield prediction capabilities of their autonomous robot in greenhouses across multiple tomato varieties. How to apply a previously built model to all kinds of different varieties, without having to go through the incredible effort of data collection and building new models for each? To this end, the project will look at techniques that can reuse what they have learned before, and transfer that knowledge to any new cultivar.  

Determining the growth stages of strawberry plants using drones
Using Corvus drones to collect large-scale data of some 10s of thousands of strawberry plantlets grown outdoors, Visser Horti Systems and the team are exploring machine-learning methods to make quick quality assessments and determine the growth stage of the strawberry plants. Aneesh: “Can we use technology to tell the growth stage and changing quality of plants? If we can, that will offer huge value later on in the supply chain, for example when plants start to produce fruit.”



Pioneering sensing technologies in vertical farming
ioCrops and Tungsram raised an open question: what do we need to measure to understand how well plants are growing on a vertical farm? Being a much newer growing system, the researchers need to reflect on what growth means in vertical farming conditions and if the existing sensors can actually measure it. The research will be more explorative in the first couple of years, surveying many different types of sensing technologies and traits of interest to vertical farming.

Data collection
All three work packages started running on the 1st of July 2021, and are gaining traction. In the coming six months, the researchers will focus on data collection and start the first modeling processes.

The team hopes to lay the groundwork for technologies that, like the products they model, will continue to change and mature, and lead to solutions that will help make better decisions in the face of various agri challenges. Aneesh: “These challenges are very broad and must be looked at from many different perspectives, but this is definitely one piece of the puzzle.”

For more information:
Wageningen University & Research
www.wur.nl 


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