“In a vertical farm, the main operating costs are attributed to energy consumption, of which artificial lighting is one of the main components. Reducing this cost is essential for controlled environment farming to become a more cost-effective way of production, and that’s the main problem we’re trying to solve with our technology,” says Dr. Rakibul Islam, biomedical science researcher at Photosynthetic, a subsidiary of Rift Labs.
He explains that the Photosynthetics R&D Lab provides a platform to perform scientific testing by comparing environmental variability side-by-side while monitoring plant growth. It provides valuable information to reduce resource waste by adjusting, for example, photoperiod, wavelength, temperature, etc., for optimal plant growth. It provides vertical farms with a recipe for energy-efficient plant growth, which they can implement in the production environment.
As a researcher, Rakibul has always had a huge interest in AI. He worked in a leading AI research group, where they developed and evaluated the application of artificial intelligence in cancer diagnosis and disease prognosis. However, at Photosynthetic, Rakibul’s role is to innovate AI solutions for growers.
“It was so rewarding to see the great potential of using my AI knowledge to help CEA become more efficient by creating smart processes and workflows. Working on this project satisfies both my hobby of growing plants and my intellectual curiosity.”
Grow with the producer
Photosynthetic’s expertise lies in generating data and precisely controlling plant growth inputs using its proprietary software and patented technology to mix light of different wavelengths. For example, through the self-contained R&D lab, growers can better understand the environmental needs of a candidate crop, allowing experimentation to achieve efficacy without compromising plant physiology.
By controlling the environmental conditions in this R&D lab, one can recreate the “problem situation” for growers to generate highly relevant data needed to train machine learning models. Data stored for optimal growth can be used to simulate growth and predict crop harvest time by calculation.
“We don’t just make a product for growers, we make it with them. Through Photosynthetic, our goal is to establish ourselves as a research partner and technology provider for growers around the world. We have a data-centric approach where we want to use our expertise in the field of light science and deliver smart solutions that help our customers continuously improve their production process,” concludes Rakibul.
Since AI is a vast field; generally it is used interchangeably for Machine Learning (ML), Deep Learning (DL), etc. It consists of a set of tools that allow computers to learn without being explicitly programmed, explains Rakibul.
“Traditional software is rule-based, where exact instructions are given to a computer to solve a problem, which means we need inputs and rules to provide an output. In machine learning, we have need previous inputs and outputs to train a model so that it can predict the output when it receives new input Machine learning applications are all around us today; more importantly, they are useful says Rakibul.
For example, the machine learning model works behind the screen when you unlock your phone using a facial recognition system. Spam filters in your emails, search engine results and Netflix movie recommendation system; are also machine learning models. There are applications of AI in flying drones and talking robots, so it’s not entirely wrong to imagine them when thinking about AI.
“We must be mindful when designing technology to aid productivity so that it not only sounds good, but rather adds value to users by reducing unnecessary work and complexity. The best products and AI services are often invisible and seamlessly integrated into workflows.”
Current roles of AI at CEA
According to Rakibul, CEA is one of the ideal candidates to harness the power of AI. Indeed, AECs are designed to control and monitor growing conditions to improve the sustainability of agricultural production. Producers make decisions based on monitored variability for better resource utilization and increased productivity, quality, profitability and sustainability.
AI can play a role in these areas; for example, it can potentially optimize electricity consumption, which is one of the main cost drivers at CEA; automate processes to increase productivity; optimize plant growth and predict yield to reduce uncertainty; detect substandard products to ensure quality.
“The technological capability is there. However, the fact is that there are no ready-made solutions. These solutions are data dependent and therefore must be built in collaboration with producers, depending on the type of optimization they need. “, he notes.
Improve production with AI
In fact, AI solutions in indoor plant production require a remarkably diverse set of expertise. Coding a machine learning model is, of course, a crucial part, but not the only part and not even the biggest challenge. AI development involves many different aspects of complex infrastructure surrounding model development, such as data collection, feature extraction, service infrastructure, monitoring, etc.
“Still, I see AI as one of the many interesting tools that we can use to increase yield and reduce production costs in indoor plant production. In my opinion, it has enormous potential, which has not yet been fully explored.We work with AI through our multidisciplinary team, comprised of crop scientists, AI product managers, and mechatronics, electronics, and software engineers, to help our customers helping to frame and define their challenges as machine learning problems to be solved. AI can help reduce routine manual tasks, aid in decision-making, and ultimately optimize the production process.”
For more information:
Oyvind Hasund Dahl, Trade Revenue Officer
Photosynthetic (by Rift Labs)
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