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Training artificial intelligence to track greenhouses in Antarctica and Mars

Scientists from the Skoltech Center for Computational and Data-Intensive Science and Engineering (CDISE) and the Skoltech Digital Agriculture Laboratory and their collaborators from the German Aerospace Center (DLR) have developed an artificial intelligence (AI) system that enables processing images from autonomous greenhouses, monitoring plant growth and automating the cultivation process. Their research was published in the journal IEEE Sensors.

"One cannot maintain continuous communication with Neumayer III, and training computer vision models on board requires too many resources, so we had to find a way to send a stream of plant photographs to external servers for data processing and analysis," Skoltech Ph.D. student Sergey Nesteruk explains.

As a conclusion to their research, the Skoltech team processed a collection of images from remote automated systems using their new approach based on convolutional neural networks and outperforming popular codecs by over seven times in reducing the image size without apparent quality degradation. The researchers used the information from the reconstructed images to train a computer vision algorithm which, once trained, is capable of classifying 18 plant varieties according to species at different stages of development with an accuracy of 92%. This approach makes it possible to both visually monitor the system operation and continuously gather new ML model training data in order to enhance the models' functionality.

Read the complete article at www.techxplore.com.


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