INSUBCONTINENT EXCLUSIVE:
Presenting onstage today in the 2018 TC Disrupt Berlin Battlefield is Indian agtech startup Imago AI, which is applying AI to help feed the
manual and time-consuming process to quantify plant traits, often involving tools like calipers and weighing scales, toward the goal of
developing higher-yielding, more disease-resistant crop varieties.Currently they say it can take seed companies between six and eight years
to develop a new seed variety
So anything that increases efficiency stands to be a major boon.And they claim their technology can reduce the time it takes to measure crop
their crops using traditional methods like scales
processing technology, they can also crucially capture more data points than the human eye can (or easily can), because their algorithms can
measure and asses finer-grained phenotypic differences than a person might pick up on or be easily able to quantify just judging by eye
this more coverage we are having more scope to select the next cycle of this seed
research [on the kind of rice you are eating now] has been done in the previous seven to eight years
The overarching vision is not only that AI will help seed companies make key decisions to select for higher-quality seed that can deliver
higher-yielding crops, while also speeding up that (slow) process
Ultimately their hope is that the data generated by applying AI to automate phenotypic measurements of crops will also be able to yield
location/environment can involve using genetic engineering
Though the technology has attracted major controversy when applied to foodstuffs.Imago AI hopes to arrive at a similar outcome via an
entirely different technology route, based on data and seed selection
will be highly, highly valuable to them because this will help them in reducing their time resources in terms of this breeding and
understanding of the underlying traits.In the case of disease-resistant plant strains it could potentially even help reduce the amount of
pesticides farmers use, say, if the the selected crops are naturally more resilient to disease.While, on the seed generation front, Gupta
to identify crop diseases and measure with greater precision how extensively a particular plant has been affected.This is another key data
point if your goal is to help select for phenotypic traits associated with better natural resistance to disease, with the founders noting
So by automating disease capture using AI-based image analysis the recorded data becomes more uniformly consistent, thereby allowing for
better quality benchmarking to feed into seed selection decisions, boosting the entire hybrid production cycle.Sample image processed by
Imago AI showing the proportion of a crop affected by diseaseIn terms of where they are now, the bootstrapping, nearly year-old startup is
depend on any proprietary camera hardware
Data can be captured with tablets or smartphones, or even from a camera on a drone or using satellite imagery, depending on the sought for
achieved by either fixing the distance of object from the camera or by placing a reference object in the image
other players is that their approach is entirely non-destructive
This means crop samples do not need to be plucked and taken away to be photographed in a lab, for example
percent) but using our software they can accurately pin point the exact percentage (e.g
they add, noting that they used WhatsApp groups to gather intel from local farmers.While seed companies are the initial target customers,
