predict plant growth
Channel: Jagad Meru
Date: 2023-01-10

predict the dynamics of plant growth using computer vision and machine learning

A group of scientists from the Space Center and the Center for Scientific and Engineering Computing Technologies for tasks with large data arrays of Skoltech has developed a method for predicting the growth of plant biomass based on 2D and 3D images.

The results obtained will increase the efficiency of precision farming, both on Earth and in space. 

The results of the study were presented at the prestigious international scientific and technical conference IEEE Instrumentation and Measurement Technology Conference.

The population of our planet is constantly growing, so research aimed at the development of precision farming is extremely relevant. 

The high technologies used in this area offer great opportunities to combat hunger in developing countries, ensure food security, reduce the human impact on the environment and increase the economic efficiency of agriculture. 

However, precision farming faces a number of difficulties and unsolved tasks, the key problem is the optimization of the use of resources.

For these purposes, there is a need to create accurate predictive models that allow you to predict growth and optimize the production of products. 

Unfortunately, empirical models that describe biomass growth depending on various factors have a number of disadvantages limited application to different types of crops and too many parameters, the measurement of which is expensive or slow. 

Universal models with sufficient accuracy are indispensable not only in the fields, but also in the creation of highly efficient greenhouses with controlled growth conditions, as well as in artificial life support systems, on space stations.

A team of scientists, which included three Skoltech professors Rupert Herzer, Tatyana Podladchikova, Andrey Somov and Skoltech graduate student Dmitry Shadrin, developed a method for predicting plant growth in artificial conditions. 

The first stage of the work was the collection of statistical data. The growth of plants in an artificial groundless system was recorded using a 3D camera. 

The data obtained made it possible to find a connection in the increase in the total surface area of all leaves with the increase in total plant biomass. 

After that, the increase in leaf area was recorded using a 2D camera, and on the basis of these measurements, a dynamic model of plant growth was built. 

The main feature of the developed method is the combination of 3D and 2D cameras when collecting data.

With this approach, there is no need for complex calculations with many parameters. 

Indicators of the area of all leaves and biomass in combination with the developed mathematical models give accurate results. 

To conduct the experiment, an automatic system with artificial growth conditions was created, equipped with 2D and 3D cameras, as well as sensors that collect environmental data. 

This system uses machine learning to model plant growth and predict its dynamics.

During the experiment, more than 10,000 images were processed. 

The study formed the basis of a pilot project to optimize plant growth in a high-tech experimental greenhouse farm of Michurinsk State Agrarian University.

The main advantage of our method is that 3D images are enough to obtain for each plant species only once. 

After that, to predict the growth of biomass in greenhouses, it is enough to use the simplest chambers. 

This greatly simplifies and reduces the cost of forecasting, monitoring and optimization systems for greenhouses and artificial life support systems, says the first author of the study, Dmitry Shadrin.

The desire to be in harmony with the surrounding world causes a person's interest in understanding the essence of the observed phenomena, their patterns and anticipating the further development of events. 

Knowledge about the world around us is based on observation and experiment. 

Dmitry Shadrin, a graduate student of Skoltech, implemented a unique experiment on the groundless cultivation of tomatoes and lettuce. 

The latest data collected during the experiment, as well as the development of effective methods for their analysis to predict the growth of biomass is a great contribution to the creation of autonomous life support systems in space and on Earth, says Tatyana Podladchikova, professor at the Skoltech Space Center.

# Hi-Tech
# Biology
# Machine Learning
# plants
# Skoltech