IDEAS Team wins 2024 Autonomous Greenhouse Challenge

February 3, 2025

Competing against the world’s top universities and industry leaders, China-based Zhejiang University’s (ZJU) IDEAS team was crowned the victor of the 4th Autonomous Greenhouse Challenge held in the Netherlands.

The IDEAS team is the first Chinese team to win this honour.

Hosted by Wageningen University & Research (WUR), the 2024 Autonomous Greenhouse Challenge brought together 23 teams from 24 countries, aiming to advance smart greenhouse management through artificial intelligence, while minimizing resource consumption and labour inputs. Participants tackled four major tasks including 1) machine vision task to exact critical growth parameters; 2) environmental control strategies to improve tomato quality and yield while effectively managing costs; 3) deep learning model task to identify pest on a small trap, and 4) AI strategy for system optimization.

In early June 2024, the first online phase of the Challenge was held, with five teams advancing to the next phase of the competition. The five teams included the IDEAS team (Zhejiang University, China), MuGrow (TU Delft, Gardin, Rijk Zwaan, Wageningen University), AgriFusion (Croft, IMEC, GreenBites, Harvard University, Korea University of Technology and Education, Seoul National University), Trigger (Grit, Ridder, Daeyoung, Bigwave, Seoul National University, Keimyung University), and Tomatonuts (Wageningen University, China Agricultural University, Jingwa Agricultural Science and Technology Innovation Centre, Golden Scorpion).

From 2 September to 15 December, these five teams put their machine learning and computer vision skills to the test by remotely and autonomously growing a real dwarf tomato crop in their own greenhouse compartment. Their goal was to achieve the highest yields and best quality with the most sustainable input of resources, such as water and energy, and thus maximize net profit. They also had to make decisions on biological pest control actions.

All members of the IDEAS team hail from the Intelligence Driven and Enabled Agricultural Systems (IDEAS) laboratory at the College of Biosystems Engineering and Food Science of ZJU. Under the mentorship of Professor LIN Tao, who specializes in agricultural artificial intelligence, team members XIA Fulin, LIU Wei, FU Rongmei, MA Xunyi, and WU Yanxu applied their combined expertise in agricultural engineering, computer vision, AI, and horticulture.

Team leader XIA Fulin reflected on their achievement. “Winning this championship is both an honour and a powerful motivator for us. It strengthens our resolve to continue exploring the agricultural field. We believe that with ongoing technological innovation and its application, we can truly realize fully automated greenhouse management.”

Several factors were taken into account when determining the winner, explains Stef Maree, a data scientist involved in the Challenge. “The most important criterion was the net profit achieved by the teams. It was pre-determined that teams would earn money based on the number of ripe tomatoes per square metre of the greenhouse. This amount was then divided by the number of days it took to complete the harvest. The earlier the harvest, the better – provided the tomatoes were ripe, of course. On the other hand, the teams incurred costs for heating, electricity, CO₂, depreciation, and materials.”

In addition to the score for their harvest, teams could earn bonus points or incur penalties. Maree explains: “Bonus points were awarded for decisions related to integrated pest management (IPM). Each week, teams had to decide how much and which types of biological pest control to use against potential threats, such as whiteflies. Correct IPM strategies earned them points. Teams incurred few penalties for manual interventions. This happened once when one team’s irrigation stopped, and in two cases, the algorithm miscalculated the harvest date, resulting in delays.”

According to Maree, the winning factor was IDEAS’ decision to cultivate with as many pots per square metre as possible. “Normally, during cultivation, a grower spaces the plants further apart to ensure all leaves receive enough light and the plants maintain a good shape. But it turns out that this is not necessary for a good yield,” he said. “The plant density of IDEAS was almost twice as high as that of most other teams, resulting in a higher profit per square metre per day. IDEAS’ overall cultivation strategy was also effective – they were resource-efficient, making extensive use of energy screens.”

Challenge project leader Silke Hemming noted the teams operated at an incredibly high level. “Apart from a few minor adjustments, each team managed to achieve a successful autonomous harvest. And they did so well before the 15 December deadline,” she said. “This demonstrates that they developed excellent algorithms in a relatively short amount of time. As in previous years, the teams initially underestimated the amount of work and complexity involved. Especially during the testing phase before the challenge began, things often didn’t go as planned. But in the end, every team succeeded.”

According to Hemming, this fourth edition of the challenge has brought the horticultural sector closer to fully autonomous greenhouse cultivation. “Letting an algorithm take control of a greenhouse and achieving a full harvest after a few months doesn’t yet exist in practice,” she noted. “No grower has fully automated this process. However, specific aspects, such as autonomous temperature control, are already in use. We’ve demonstrated that cultivation – except for aspects like IPM – can be fully autonomous. Of course, there are still many challenges and areas for improvement, but we now have proof that it’s possible to complete a growing cycle with an algorithm.”

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