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Photo: Muhammad Salman S

The project focuses on optimizing Controlled Environment Agriculture (CEA) by integrating advanced technologies like Artificial Intelligence (AI) and Internet of Things (IoT). It aims to develop intelligent systems that precisely control environmental factors such as light, temperature, and nutrients to maximize crop yield. By addressing challenges in data management, sensor integration, and resource optimization, the project seeks to enhance the efficiency and sustainability of indoor farming systems.

27 Sep 2024 - 27 Sep 2027
Computational Science and Engineering (CSE)

This project has received funding from the Industrial PhD Scheme of Research Council Norway

About Project

  • Background

    Global crises and climate change have underscored the need for sustainable, local food production, particularly in countries like Norway that rely heavily on imports. Controlled Environment Agriculture (CEA) offers a solution by growing crops indoors with precise control over factors like light, temperature, and nutrients. While CEA incorporates advanced technologies such as AI and sensors to optimize plant growth, challenges in data management, sensor integration, and resource control must be addressed to fully realize its potential.

  • Objective

    The project aims to develop an AI-driven system to optimize environmental conditions for plant growth, specifically focusing on maximizing LED lighting efficiency. This involves creating an image-based deep learning model to automatically measure plant phenotypes, such as height, canopy area, and leaf count, ensuring precise monitoring and control of growth parameters.

Participants

    Rakibul Islam

    Dr. Md. Rakibul Islam

    Chief Scientist, Rift Labs

    Morten Hjerde

    Rift Labs

    Rusith Chamara   Hathurusinghe Dewage

    PhD Student

Timeline

Publications