AI and photosynthesis

In the race to improve food security, the progressive development of AI plays a very important role. The world’s population is expected to reach 9 billion within 2–3 decades, so growing staple foods is not likely to meet growing demand. Scientists around the world are carrying out serious research work aimed at increasing the yields of staple cereal crops.
The aim of this research is photosynthesis. To increase yields, scientists are seeking to improve the efficiency of plants in capturing sunlight and converting it into plant growth (both biomass and grain). Presumably, this will increase the yield of the same grain by almost 50%.

Australian professor Graham Hammer and his team have developed AI-enabled crop simulation software that could radically accelerate scientific and technological progress on this issue. It is an agricultural production systems simulator to support researchers and breeders and to help farmers.

This simulator allows you to set up a virtual crop where plant genetics can interact realistically with soil, climate, and farming practices to investigate the effects of changing these variables. And machine learning allows a multitude of variations to be calculated, taking into account a huge number of factors.

In doing so, Professor Hammer’s team has extended the simulator’s core capabilities to take account of differences in photosynthetic activity in plants. The new subroutines determine the efficiency of light conversion on the basis of very detailed models of photosynthetic biochemistry. Scientists can therefore account for differences in how efficiently plants capture carbon dioxide as well as how well it is converted into sugar.
The value of this modeling capability was highlighted recently when an initial study showed that increasing the efficiency of photosynthetic enzymes by 25% caused only a five percent increase in crop growth. Thanks to artificial intelligence, scientists can retain a view of the wider reality in which the plant functions, so that they can better focus on the most appropriate options for different growing conditions.