Microsoft AI to Win Greenhouse Competition

Five AI systems from different teams took part in cucumber growth competition
17 December 2018   747

From August to December, five teams from technology companies and universities from different countries participated in the competition for growing cucumbers in autonomous greenhouses. Irrigation, fertilizing, temperature control and other factors were controlled by artificial intelligence. The best result was shown by the Sonoma team from Microsoft Research. In addition, it reached a yield of 50 kilograms of cucumbers per square meter.

The results of the participants were evaluated by three parameters:

  1. net profit (market value of the harvested crop minus water, energy, and labor) was 50% of the final estimate;
  2. the use of artificial intelligence (reliability of the algorithm, the effectiveness of its strategy) - 30%;
  3. system stability (water, carbon dioxide and energy per kilogram of cucumbers) - 20%.

The Microsoft Research team achieved the highest yield and net profit. The second place was taken by the team from Tencent - their algorithm showed the best strategy for the use of resources. Third place went to The Croperators team from Delphy and AgroEnergy.

The jury noted that the control group of agronomists, which grew cucumbers on their own, used less electricity than any of the teams with AI. At the same time, it lost only to the team from Microsoft in terms of net profit.

According to the founders, the purpose of the competition was not to start building autonomous greenhouses around the world. The organizers wanted to know at what stage of development artificial intelligence is and what is its advantage over people in the agrarian sphere.

Nvidia to Open SPADE Source Code

SPADE machine learning system creates realistic landscapes based on rough human sketches
15 April 2019   676

NVIDIA has released the source code for the SPADE machine learning system (GauGAN), which allows for the synthesis of realistic landscapes based on rough sketches, as well as training models associated with the project. The system was demonstrated in March at the GTC 2019 conference, but the code was published only yesterday. The developments are open under the non-free license CC BY-NC-SA 4.0 (Creative Commons Attribution-NonCommercial-ShareAlike 4.0), allowing use only for non-commercial purposes. The code is written in Python using the PyTorch framework.

Sketches are drawn up in the form of a segmented map that determines the placement of exemplary objects on the scene. The nature of the generated objects is set using color labels. For example, a blue fill turns into sky, blue into water, dark green into trees, light green into grass, light brown into stones, dark brown into mountains, gray into snow, a brown line into a road, and a blue line into the river. Additionally, based on the choice of reference images, the overall style of the composition and the time of day are determined. The proposed tool for creating virtual worlds can be useful to a wide range of specialists, from architects and urban planners to game developers and landscape designers.

Objects are synthesized by a generative-adversarial neural network (GAN), which, based on a schematic segmented map, creates realistic images by borrowing parts from a model previously trained on several million photographs. In contrast to the previously developed systems of image synthesis, the proposed method is based on the use of adaptive spatial transformation followed by transformation based on machine learning. Processing a segmented map instead of semantic markup allows you to achieve an exact match of the result and control the style.

To achieve realism, two competing neural networks are used: the generator and the discriminator (Discriminator). The generator generates images based on mixing elements of real photos, and the discriminator identifies possible deviations from real images. As a result, a feedback is formed, on the basis of which the generator begins to assemble more and more qualitative samples, until the discriminator ceases to distinguish them from the real ones.