Technologies

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Climate Optimization for Indoor Farming

Technology Overview

Indoor farmers intuitively know that the environment can affect the growth of the crop. The indoor climate can either contribute to the yields or, in unfortunate circumstances, lead to loss of the crop.

However, it is not always easy to create an ideal environment for the crop. Currently, most building management systems (BMS) are closed off and don't talk with operational systems, such as the ones used for indoor farming. On the other hand, existing smart building solutions usually focus on energy efficiency alone and overlook the need to balance energy savings with crop growth, operational efficiency, low emissions, and other factors.

This technology overcomes these challenges by allowing the indoor farmer to input the optimal conditions for the crop and let the system optimize the rest. The users only need to specify which crop(s) they are growing or, if they prefer, what ‘recipe’ the system should follow. Accuracy can be improved by adding environmental sensors.

This technology provider is looking for either new or established indoor farms interested in piloting / test-bedding this solution in Singapore or APAC.

Technology Features, Specifications and Advantages

The technology offered is a computing method for management of buildings in which indoor farming facilities are located.

What makes it unique and beneficial for indoor farming:

  • Machine learning techniques which are used to create the optimal environment
  • Expect energy savings of 25% to 35%
  • Improved crop yields by 25% to 75%
  • 25% reduction in man-hours can be expected

Potential Applications

This technology is directly applicable to the indoor farming market in APAC and globally. The market size in APAC alone is expected to be USD 1.2B.

Primary application will be indoor farming. Indoor farmers can input the optimal conditions for the crop and let the system work towards optimization.

The user is able to simply specify the crop(s) they are growing and/or specify the ‘recipe’ for the system to follow.

If environmental sensors are used, accuracy of this climate optimization can be improved.

Customer Benefits

Customers in the indoor farming market can expect:

  • Expect energy savings of 25% to 35%
  • Improved crop yields by 25% to 75%
  • 25% reduction in man-hours can be expected
OVERVIEW
Contact Person

Ivan Damnjanovic

Organisation

NUS Graduate Research Innovation Programme (NUS GRIP)

Technology Category

  • Green Building
  • Building Automation / Management
  • Infocomm
  • Artificial Intelligence

Technology Readiness Level

Keywords

Machine Learning, Indoor Farming, Building Automation, Agriculture, Agritech, Crop, Crops, Leafy greens, climate control, controlled environment, energy, crop yield