https://phys.org/news/2018-07-scientists-vision-machine-growth.html

Optimized use of resources is among the many challenges that pervasive agriculture is up against in attaining these goals. Accurate predictive models capable of predicting plant growth and optimizing production are an absolute necessity in this context. Unfortunately, the empirical models that describe an increase in biomass as a function of different factors have several weak points, including a limited scope of application and a great multitude of parameters that require long and expensive measurements. By contrast, the universal models that display fairly high accuracy are an indispensable tool for a wide range of applications ‒ from open-field agriculture and highly efficient greenhouse farms with crop growth monitoring capability to artificial life support systems for space stations.

Professors Rupert Gerzer, Tatiana Podladchikova and Andrey Somov, and Skoltech Ph.D. student Dmitry Shadrin, have developed a method for predicting plant growth in an artificial environment. As the first step, they collected statistics by measuring plant growth in an artificial soilless system using a 3-D camera and established a relationship between the increase in the total biomass and the expansion of the total surface area of the leaves. Then they captured the increase in the total leaf area using a 2-D camera and built a dynamic plant growth model based on these measurements.