Math and AI to help speed up food production

Genebank Platform — CGIAR 2022

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).

Figure 1: Yield increase per acre in corn as of mid of last century in USA (Max Roser 2016 — ‘Yields’. Published online at OurWorldInData.org.)

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:

--

--

--

OperAI develops embedded ML/AI-based solutions to speed up and streamline operational processes at the edges of the cloud.

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Mercedes-Benz Research and Development India | Selection Experience & Placement Advice| AI/ML | IIT…

My new publication on Academia.edu

Actionable bot analytics with Dashbot

Crasolum backs Matterless

Can Civil Society Succeed in Its Quest to Ban ‘Killer Robots’?

How Machine Learning redefined website development

AI for Construction Estimation and Bidding

Case study: How a Real Estate Bot Sold 3 Apartments in 10 Days.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
OperAI - Operational AI

OperAI - Operational AI

OperAI develops embedded ML/AI-based solutions to speed up and streamline operational processes at the edges of the cloud.

More from Medium

Relativistic Doppler Effect From Lorentz Transformations

Comparing the Dynamics of Neural Networks using Edge-Ordered Multi-Directed Graphlets

Fun With Fractals: Using Fractals to Measure the Coastline Length of One of Canada’s Most Iconic…

Why Chess?