Bayer's AI-driven plan to feed 10 billion people by 2050

Fortune

Bayer is using machine learning to predict the genetic makeup of crops, helping farmers speed up production.

Transcript

00:00 [MUSIC PLAYING]

00:02 We used to cross existing seed varieties

00:05 to create millions of new progeny

00:08 and select for the trades that we care about and advance them

00:13 in the pipeline.

00:14 We used to call this era of breeding as selecting the best.

00:18 Over the last few years, we have transitioned

00:21 into designing the best.

00:23 We are using machine learning to predict

00:28 what kind of a genome or what kind of a seed variety

00:32 will give us the characteristics that we care about

00:36 in the environment they're going to be grown.

00:39 My name is Fani Chawale, and I lead the data sciences

00:43 within the plant breeding organization at Bayer.

00:47 We focus on developing new seed varieties for our row crops

00:52 as well as vegetable crops.

00:56 What we are trying to do is continue

00:58 to advance the trades that we care about the most.

01:03 Historically, that can be a very cumbersome process

01:05 and depend on your luck to find the ones

01:08 that you want to advance.

01:10 But with AI, you can get very targeted

01:12 and you can get very specific from very early on

01:15 in the process.

01:17 So that will save you both the resources,

01:19 and then it will also increase the probability of finding

01:22 the trade that you care about.

01:24 We're just trying to make sure that we are collecting

01:26 as much data across all of those different variables.

01:30 We use the genomic information of the seed varieties,

01:33 and we apply a machine learning model

01:36 to predict what their performance in the field

01:39 would look like.

01:41 So instead of testing these varieties in the field,

01:43 you're testing them by predicting.

01:47 What this means from a grower perspective,

01:49 ultimately, is they can get the seed varieties

01:52 that they care about one year in advance.

01:55 What it means from a bear perspective

01:57 is we are able to run this operation efficiently

02:00 because we haven't used one year of field testing

02:03 and the resources corresponding to that.

02:06 And if you think about it from a planet perspective,

02:08 I think we have been using this approach for about 10 years

02:11 now.

02:12 And in this 10 years, we have replaced close to 20,000 acres

02:17 of field testing just in North America.

02:20 So not having to run a field trial

02:22 and being able to use predicted information to advance material

02:27 in a breeding pipeline can lead to resource savings

02:31 across these different aspects, whether it

02:33 is land, water, nitrogen, or even crop protection.

02:37 If you look at our population, the numbers are increasing.

02:40 By 2050, we are looking at close to 10 billion people globally.

02:43 The arable land is decreasing.

02:45 In order to meet this demand and do it

02:48 in a way that is sustainable, we need

02:50 to continue to discover new seed products that

02:53 help us mitigate this challenge.

02:55 And that's what plant breeding is about.

02:58 (upbeat music)

03:00 [BLANK_AUDIO]

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