AdaViv is developing an adaptive and efficient indoor growing system that uses sensors, actuators, and machine learning to monitor plant growth, predict yields, detect diseases, and understand precisely how nutrients, environment and light are affecting plant growth. This system will help indoor producers attain higher yields, precise quality control, and hyper-efficient production.
To combat costly, complex hardware implementations, AdaViv is developing a custom sensory array in a small, lightweight package that can easily move around the greenhouse to capture visible and invisible plant features. Using standard sensors all packaged into mobile housing, AdaViv expects their sensor to be able to meet the price point required while still capturing the level of data needed to feed their predictive analytics models. Using the data gathered from the greenhouse sensory array along with environmental data—such as humidity levels and temperature readings—AdaViv’s predictive analytics models will be able to monitor plant growth, predict yields, and detect diseases. Through these insights, AdaViv plans to provide growers with precisely tailored recommendations, fundamentally changing how indoor crops are grown, inspected and maintained. Using their AI for Earth grant, AdaViv plans to enhance the capabilities of their predictive models and enable the scale needed for general availability rollout.
AdaViv is using AI to unleash the potential of urban agriculture. This system is useful for fostering more localized and sustainable food systems for urban areas.