Familiar Face Wins Latest Autonomous Greenhouse Challenge
The American team Koala was named the winner of the third edition of the Autonomous Greenhouse Challenge at Wageningen University & Research (WUR). The team consists of the start-up company Koidra and researchers from Cornell University. Team captain and Koidra founder Kenneth Tran also led the winning team of the first edition.
Due to the ever-growing world population, the demand for fresh and healthy vegetables is increasing. Autonomous greenhouses can ensure that more people are fed with vitamin- and mineral-rich products. In addition, these techniques contribute to increasing food safety and a higher production volume of healthy vegetables, using fewer resources such as energy. Its potential has been successfully demonstrated in previous editions of the Autonomous Greenhouse Challenge.
Growing Autonomously
In this third edition of the competition, five international teams from around the world produced a lettuce crop using a fully autonomous algorithm. With the lowest feasible input of resources such as energy and CO2 and the production of a maximum of good quality heads of lettuce, they aimed to optimize net profits.
Each team had a high-tech greenhouse compartment of Wageningen University & Research in Bleiswijk at its disposal to grow lettuce. The teams created their own AI algorithms that autonomously determined the set points for temperature, amount of daylight and artificial light, heating, CO2 concentration, and cultivation-related parameters such as crop density, spacing moments, and day of harvest. Next to standard greenhouse sensors, the teams had access to images from a Realsense 3D camera and specific sensors provided by the sponsors Sigrow and Ridder. Some teams added their own sensors to deliver input for their algorithm.
Lettuce ‘Lugano’ (RijkZwaan) was planted on May 2. Teams had to grow lettuce with a target plant weight of 250 grams, and the quality was judged. If plants were too small or had leaf tip burn or other deformations, they were classified as class B with a lower price or even unsellable class C. If plants were too large, teams wasted resources. The resource use (e.g. heating energy, electricity, CO2) was measured during the growing period and operational costs were calculated. Fixed costs depended on occupation of the greenhouse space and use of different installations (e.g. artificial light capacities). From these figures net profit was determined. The team with the highest net profit won the competition.
The teams’ AI algorithms were mounted on a virtual machine on a protected WUR server. Within this protected environment, the algorithms acquired data via a digital interface from LetsGrow and Azure Cloud. At the same time, the algorithms autonomously returned setpoints to the process computer (again through LetsGrow), which ultimately took the action on climate control in the experimental greenhouse. In a first cultivation cycle during February/March this year, each team could test their algorithm and procedure. The real challenge involved a second crop cycle during May/June. Teams could not access their algorithm any longer after start of the experiment but had to ask for permission in case they had to make urgent changes (bug fixes) in their algorithms. Access was charged, and costs were subtracted from the net profit. The winning team only accessed the virtual machine once to fix a little mistake.
Learn more about the challenge here.
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