Math and AI to help speed up food production
Math and AI-based solutions are helping to speed up plant production and adaptation to climate change. Mathematics played a major role in the past producing more food, prior to the emergence of molecular approaches. Fisher (1930) elaborated the mathematical theoretical framework that was considered as the basis of quantitative genetic theory, on which crop improvement was established, to produce more food, leading to remarkable yield increase such as that of corn (Figure 1).
There is a regain of use of math and AI to help produce more food and speed up adaptation to climate change. The agriculture sector is in the midst of climate change, in a time of a need to keep pace and produce more food to close the gap of 56% between the amount of food available today and that required by 2050 (WRI, 2021).
Mathematics and Artificial Intelligence are considered as two branches of the same tree. There are both interlinked and either of them alone may lead to ad-hoc programs.
While math is required for AI, AI in turn helps mathematicians in discovering new theorems.
In terms of food production, in an article published by PNAS Tanksley, a professor emeritus at Cornell University in Ithaca, NY said: “We have to double the productivity per acre of our major crops if we’re going to stay on par with the world’s needs.” Tanksley and other researchers are using artificial intelligence (AI) to speed up crop improvement and ultimately help to increase food production. https://www.pnas.org/doi/10.1073/pnas.2018732117
Developing new crop varieties with the desired traits can be a costly procedure both in terms of time and money. Geneticists usually carry out crossings to develop new varieties followed by the selection of these crossings, a process that take time and can be costly. Math, such as Bayesian networks (BN) is being used to help find the desired traits within a short period of time.
In this article titled “Feeding The World–With Math” by Joseph Byrum “One of the greatest needs–and thus one of the greatest business opportunities–will be harnessing advanced mathematical techniques and technologies to ensure global food security.”
We have used Math and ML that helped us in speeding discovery of genes that are being used in crop improvement. Different ML and features extraction techniques using R and Spark as well as GIS (raster data) to dig into large environmental data sets.
Some of the codes used using R and Spark can be found at GitHub platform such as:
