Wageningen University teams up with Microsoft, Tencent, and Intel to test Artificial Intelligence for growing Cucumbers

Neural network example

There is a challenge under way where the worlds top tech companies are working on finding the best methods for using Artificial Intelligence and automation to grow plants.

 SoilGrids (the output of a system for automated global soil mapping) are the main products.

SoilGrids (the output of a system for automated global soil mapping) are the main products.

Wageningen University staff enter the greenhouse to do things like remove cucumbers or cut leaves, but an algorithm informed by sensors controls about 20 inputs, such as roof ventilation, artificial lighting, and heating, that affect plant growth.”

The Venture Beat article “Why Microsoft, Tencent, and Intel are growing cucumbers in autonomous greenhouses” goes onto say

“A jury primarily made up of Wageningen University research staff will choose winners based on their resource efficiency, the robustness of their AI model, and the sustainability of methods they use to grow cucumbers.

Cucumbers were chosen as the test crop because of the amount of existing modeling data and know-how available, Hemming said.”

The Challenge issued by the CXO of Tencent:
“The Challenge
The goal of the challenge is to produce a cucumber crop within 4 months inside a greenhouse remotely! Greenhouse space and controls will be provided by WUR and the teams are allowed to provide their own sensors and cameras.

Each team will be able to extract necessary data from the greenhouse compartment and add their own ICT/models/machine learning algorithms in order to decide on the control settings for the next day or period.”

The rules can be found here and their goals are listed on the autonomousgreenhouses.com website.

Ectomycorrhizal Specificity Patterns in a Mixed Pinus contorta and Picea engelmannii Forest in Yellowstone National Park

“We used molecular genetic methods to test two hypotheses, (i) that host plant specificity among ectomycorrhizal fungi would be common in a closed-canopy, mixed Pinus contorta-Picea engelmannii forest in Yellowstone National Park and (ii) that specificity would be more common in the early successional tree species, P. contorta, than in the invader, P. engelmannii. We identified 28 ectomycorrhizal fungal species collected from 27 soil cores.
The proportion of P. engelmannii to P. contorta ectomycorrhizae was nearly equal (52 and 48%, respectively).
Of the 28 fungal species, 18 composed greater than 95% of the fungal community. No species was associated exclusively with P. contorta, but four species, each found in only one core, and one species found in two cores were associated exclusively with P. engelmannii. These fungi composed less than 5% of the total ectomycorrhizae. Thus, neither hypothesis was supported, and hypothesized benefits of ectomycorrhizal specificity to both trees and fungi probably do not exist in this system.”

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