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Speaker 1: When a lot of us think of farming, it reminds

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Speaker 1: us of simpler times, and perhaps it feels like one

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Speaker 1: of the remaining industries exempt from the influences of the

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Speaker 1: modern tech world. But imagine a world where the success

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Speaker 1: of your family's farm crop yield is access to AI tools.

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Speaker 1: There's so much labor and effort that goes into maintaining

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Speaker 1: a farm, especially when farmers have to anticipate unpredictable weather

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Speaker 1: patterns and unprecedented seasons brought on by climate change. Plants,

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Speaker 1: like humans, are living things, with millions of tiny organisms

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Speaker 1: both attacking and assisting their life cycle. Some threats to

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Speaker 1: crop life are smaller than the human eye can see,

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Speaker 1: and when not addressed, the results can be disastrous to

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Speaker 1: local economies. But what if AI could solve the problem.

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Speaker 1: Giving eyes and access to where farmers cannot reach. AI

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Speaker 1: protects crops and the economy from the threat of microbial pests,

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Speaker 1: resulting in a more prosperous tomorrow. Hey there, I'm gram

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Speaker 1: Class and this is technically speaking an Intel podcast. The

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Speaker 1: show is dedicated to highlighting the way technology is revolutionizing

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Speaker 1: the way we live, work, and move. In every episode,

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Speaker 1: we'll connect with innovators in areas like artificial intelligence to

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Speaker 1: better understand the human centered technology they've developed. There has

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Speaker 1: always been a disconnect between nature and technology. However, today

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Speaker 1: there's a lot of science and technology at the core

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Speaker 1: of modern farming, and we're not talking about GMOs. One

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Speaker 1: of the biggest issues in agriculture is environmental threats. Farmers

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Speaker 1: struggle with protecting crops from diseases and pests without using

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Speaker 1: tools that could adversely affect consumers. AI has been instrumental

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Speaker 1: in helping farmers detect pests before infestations occur and result

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Speaker 1: in huge crop loss. But before we get into exactly

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Speaker 1: how it all works, I want to introduce our guests.

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Speaker 1: In twenty seventeen, Rishi kish amitt Nayak's family farm in

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Speaker 1: India so ninety percent crop loss due to pest in infestation.

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Speaker 1: After partnering with a fellow student, Niharika Haridas, the two Megatronics,

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Speaker 1: Robotics and automation engineering students found a way to use

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Speaker 1: AI to develop a method that could detect crop pests

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Speaker 1: through thermal imaging. This system, called kishan No, has been

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Speaker 1: proven effective and very affordable to local farmers. Rishikish America

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Speaker 1: thanks for being here.

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Speaker 2: Such a pleasure to be here, Graham, thank you for

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Speaker 2: the invitation.

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Speaker 1: We're also joined by Shwita Karuna, intel's Director of Government

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Speaker 1: Partnerships and Initiatives for Japan and the Asia Pacific. Sharita

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Speaker 1: has over twenty three years of experience creating trusted government

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Speaker 1: relationships and fostering government programs that encouraged the implementation of

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Speaker 1: modern science into the workforce PLUSH. She was instrumental in

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Speaker 1: helping kishan No grow as a farming tactic across the region. Welcome, Shwrita.

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Speaker 3: Thank you, Graham, such a pleasure being here.

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Speaker 1: So let's start at the beginning a very interesting story

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Speaker 1: around rishikish Can you tell a little bit about the

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Speaker 1: problem that your family and other farmers experienced back in

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Speaker 1: twenty seventeen.

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Speaker 4: In India particularly, it's an agricultural country, so more than

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Speaker 4: seventy percent of the people do agriculture as their own

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Speaker 4: major occupation. In twenty seventeen, my father's grandfather was completely

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Speaker 4: invested into agricultural farming, and during that time, in Orisa particularly,

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Speaker 4: there was a plant best attack that couldn't be identified

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Speaker 4: for a longer period of time, and that resulted in

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Speaker 4: a lot of crop losses and hectares of land was

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Speaker 4: just lost because of an unidentified pist. Personally, we saw

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Speaker 4: a lot of farmer suicides in our own village, and

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Speaker 4: that was the major reason when I thought, Okay, I

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Speaker 4: do have a background of engineering, I do have a

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Speaker 4: background of robotics, so why not to create something for

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Speaker 4: our own farmers. And being part of that family where

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Speaker 4: we do farming in our parental site, I was just

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Speaker 4: touched with that fact that I need to do something

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Speaker 4: for the farmers.

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Speaker 1: In Rishikishi's village alone, there were four farmers who took

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Speaker 1: their lives as a result of the devastated crop, and

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Speaker 1: his family saw a ninety percent crop loss that year.

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Speaker 1: The infestation was so devastating to their livelihood his family

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Speaker 1: considered leaving farming all together. And to make matters worse,

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Speaker 1: the problem was difficult to identify and trace. Before we

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Speaker 1: get into the actual details of how you solved it

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Speaker 1: in Arika, how did you get involved in the project.

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Speaker 2: I decided to pursue mecatronics and automation at Viatchene out

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Speaker 2: of a sheer passion for robotics as a twelfth grader.

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Speaker 2: So I came across the work that many companies like

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Speaker 2: Boston Dynamics were doing at that point and exactly right

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Speaker 2: the spot pro vote of course, and I was just

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Speaker 2: enthralled with potential that it helped, Like it was like,

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Speaker 2: oh my, what this could change humanity?

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Speaker 5: And I was like, I need.

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Speaker 2: To do something in this space. I wanted to help

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Speaker 2: people with this new technology. And that's how I went

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Speaker 2: to Aitchen and that's where I'm Metri Shikish and we

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Speaker 2: started talking and we were talking about this project and

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Speaker 2: I was like, you know, that's that's amazing that we'll

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Speaker 2: let me contribute to it as well, and that's how

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Speaker 2: we started collaborating on the project and then we participated

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Speaker 2: in the Inaugril Intelliet Global Impact Festival and the rest

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Speaker 2: is history. We had a wonderful time and you know,

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Speaker 2: the support that we have gotten from Intel for it

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Speaker 2: as well has been phenomenal and that's the reason that

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Speaker 2: Kishano is at the place where it is right now.

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Speaker 1: Excellent. So now as the I guess the sixty four

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Speaker 1: thousand dollars question is how does the kishan No work.

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Speaker 4: Kishano basically taps into saturate based thermal imagery. These images

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Speaker 4: can detect temperature variations and crops which often indicate Streuss

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Speaker 4: disease or pestal activity. For instance, areas affected by certain

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Speaker 4: pest or microbol infestations may exhibit different thermal patterns compared

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Speaker 4: to healthy areas. We collect images from Sentinel two and

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Speaker 4: lands At eight satellites. Those satellite images are then sys

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Speaker 4: to get index mapping out likes, for example, vegetative indexes

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Speaker 4: and moisture indexes through a software called QGIS, so it

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Speaker 4: basically gives us the values for those vegetative indexes and

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Speaker 4: moisture indexes, and these gathered thermal imageries processed using AA algorithms,

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Speaker 4: where we've processed the images first into the open Veno

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Speaker 4: platform and we get a d blood image for better

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Speaker 4: accuracy of training of the models. Then these algorithms are

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Speaker 4: trained to recognize patterns or animalies that correspond to microbilan

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Speaker 4: pest outbreaks. Over time, has more data is collected and analyzed,

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Speaker 4: the AA model becomes more accurate and efficient in its

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Speaker 4: prediction and leveraging the power of machine learning. Once a

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Speaker 4: potential threat is identified in the system, the systems can

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Speaker 4: send alerts or recommendations to the farmers in the local

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Speaker 4: administrative levels, where we also design the physical device apart

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Speaker 4: from the AA algorithm to get a confirmatory test that

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Speaker 4: there is a pest or plant disease outbreak. This actually

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Speaker 4: includes information about the type of threat, it's severe, and

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Speaker 4: recommendation algorithms.

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Speaker 5: This proactive approach.

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Speaker 4: Helps farmers to take actions before the problem becomes widespread

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Speaker 4: and saving both time and resources.

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Speaker 1: I'd like to talk about Intel open Veno a little

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Speaker 1: bit so quickly, just to inform our audience. INTE open

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Speaker 1: Vino is a cross platform toolkit developed by Intel that

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Speaker 1: deploys deep learning models on visual data sets, helping computers

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Speaker 1: better recognize and process images to inform decision making. But

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Speaker 1: I'm curious as someone who's just as interested in what

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Speaker 1: didn't work as opposed to what ultimately does. Why did

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Speaker 1: you decide to use Intel open Veno. Were there are

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Speaker 1: other methods you tried first?

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Speaker 2: So we did try a lot of techniques, and we

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Speaker 2: found that open Veno worked perfectly with our project, especially

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Speaker 2: with the goal that we were trying to achieve. So

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Speaker 2: we saw that the hardware requirements as well as the

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Speaker 2: software requirements did completely match. Also, we had mentorship from

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Speaker 2: Intel and we were able to properly and in a

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Speaker 2: better way adapt to those systems to our project, and

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Speaker 2: that's the reason which it was open.

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Speaker 4: We know, we actually tried to degler images through some

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Speaker 4: deep learning algorithms, but those algorithms was actually not satisfying

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Speaker 4: the accuracy that we actually wanted, so open Veno just

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Speaker 4: suited out the case perfectly.

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Speaker 1: One thing I'm interested in is the pests that were

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Speaker 1: being detected. Am I right in saying that it had

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Speaker 1: a unique therm signature?

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Speaker 5: Yeah?

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Speaker 1: And how did you discover that?

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Speaker 4: In twenty seventeen, Once we identified the problem, we actually

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Speaker 4: tried to create a physical device through a thermal camera

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Speaker 4: set up and microprocesses. We were rotating that device among

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Speaker 4: the periphery of the crop fields to understand what exactly

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Speaker 4: the thermal traces are in the leaf of the crop plants.

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Speaker 4: And once we understood what are the thermal signatures for

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Speaker 4: different crop plants, we understood there is a concept that

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Speaker 4: whenever there is a pathogen or a plant disease, there

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Speaker 4: is a certain increase in the leaf temperature. And if

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Speaker 4: we identify that leaf temperature increases in the particular or

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Speaker 4: in a particular duration of time, we can actually significantly

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Speaker 4: say that there is a best attack or a plant

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Speaker 4: disease in the crop area. Once we had the theory,

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Speaker 4: we tried to incorporate that similar formula in the vegetative

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Speaker 4: index of the satellite setup. So in twenty nineteen we

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Speaker 4: had the physical setup, we tried the same literature to

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Speaker 4: understand it to the satellites.

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Speaker 1: Hearing Rishikish and Aharika elaborate on how they design their

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Speaker 1: imaging tool reminded me of my own experience attempting to

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Speaker 1: develop systems to work remotely in the jungles of Africa.

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Speaker 1: It's not an easy feat though, as there's no real

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Speaker 1: infrastructure for these sorts of products, especially when they are

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Speaker 1: limited by internet access and availability in the area. Hearing

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Speaker 1: how much progress these two had made with their program,

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Speaker 1: maybe wonder about the challenges that went into making this

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Speaker 1: tool available in the rural farmlands of India.

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Speaker 2: There has always been a digital divide in India, as

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Speaker 2: we can see, but now it's been narrowing and that's

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Speaker 2: a very good news for all of us, and that

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Speaker 2: infrastructure is also becoming better. There's also research that India

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Speaker 2: has one of the cheapest internet out there in the world,

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Speaker 2: so I mean, it's being adapted and we are glad

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Speaker 2: that it is. But when we were working on it,

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Speaker 2: we did face a lot of infrastructure issues regarding internet

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Speaker 2: services as well and internet connectivity exactly, and sort of

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Speaker 2: having that satellite imagery. Gaining access to the satellite imagery

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Speaker 2: was very difficult for us because that area wasn't mapped.

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Speaker 2: Remote areas aren't usually mapped with that much precision as

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Speaker 2: that of let's say, an urban area, so we did

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Speaker 2: have some issues with that, but then we did try

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Speaker 2: our best to solve those and gain satellite images from

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Speaker 2: the areas that we neated.

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Speaker 4: Farmers in the villages particularly, they were quite a bit

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Speaker 4: skeptical to try this out, and the farms because in

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Speaker 4: India particularly didn't back that time, we didn't have that

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Speaker 4: much of agritechnology tools or products, and going as a

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Speaker 4: youngster something around in class ninth or tenth and trying

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Speaker 4: out as some different new projects or new census in

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Speaker 4: the field, they were quite a bit skeptical. So managing

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Speaker 4: that side of that, Okay, we are doing something good,

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Speaker 4: we are doing something better for your own crops, we

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Speaker 4: are doing something for the best of the society. Convincing

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Speaker 4: them was one of the very huge challenge over there

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Speaker 4: in India.

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Speaker 1: What kind of data or training processes were involved in

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Speaker 1: training the model to recognize microbiopests in the crops.

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Speaker 5: Initially it was only deep learning algorithms.

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Speaker 4: Further on, when we had a lot of thermal praise

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Speaker 4: data and we had did the d blood images, we

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Speaker 4: were just focused on the CNN models to train the data.

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Speaker 4: And it hadn't given a good accuracy of for around

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Speaker 4: ninety points something percentage, so it was a pretty good

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Speaker 4: accurate to start with for a particular set of crops.

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Speaker 1: You said, CNN, could you just define what that is please?

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Speaker 5: Conventional neural network.

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Speaker 1: Okay, And that's just another AI technique to for learning.

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Speaker 5: Yeah, yes, a machine learning okay, okay.

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Speaker 1: And you just mentioned about the accuracy that you achieved.

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Speaker 1: Would you say that's typical for the Intel Open Veno

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Speaker 1: platform to get that sort of result.

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Speaker 4: The accuracy is for the total accuracy of the model

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Speaker 4: for a particular set of crops, for example, tomatoes and wheat.

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Speaker 4: For those two crops we had an accuracy fround ninety

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Speaker 4: point two eight percentage, and for other crops it's still

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Speaker 4: in the process of getting more accurate and all. So

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Speaker 4: for these two crops, overly, it was the accuracy that

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Speaker 4: we measured out and.

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Speaker 1: In terms of the Intel Open Veno technology, can you

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Speaker 1: think of anything any other farming use cases beyond pest

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Speaker 1: management and crop protection.

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Speaker 5: Currently, we were trying to work on crop genome analysis

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Speaker 5: where we were actually trying to understand because of the

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Speaker 5: climate change to the new variants of crops are needed

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Speaker 5: to adapt to the new climatic conditions. So we were

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Speaker 5: trying to understand how exactly we can use machine learning

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Speaker 5: algorithms to create new genomes in the crops the microbiology

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Speaker 5: side of it.

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Speaker 4: So yeah, that's one area that I was completely focused

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Speaker 4: on in this past recent days.

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Speaker 2: I would like to add on to that, and as

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Speaker 2: Education mentioned, convolutional neural network model that we used, it

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Speaker 2: was at that point not something that was used by

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Speaker 2: the AI community, but then we now see a lot

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Speaker 2: of use cases for that and that's something that we

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Speaker 2: are very glad about. And also some of the use

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Speaker 2: cases that I have at least found as an AI

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Speaker 2: enthusiast that models like these could have is in real

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Speaker 2: time data, especially as the climatic change has become a

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Speaker 2: huge issue. It is something that can help a lot

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Speaker 2: of farmers with when there is excessive rains or when

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Speaker 2: there is no rain at all, to predict these through

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Speaker 2: AIML technologies. And I believe that the limit is boundless

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Speaker 2: when it comes to AI technologies. Right we are seeing

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Speaker 2: a start of a new era of AI, and I

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Speaker 2: am very glad to see how I was being used

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Speaker 2: by lots of companies, and we also hope to go

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Speaker 2: contribute to that, and I hope for a very bright future.

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Speaker 1: AI has been the focus of a lot of discourse

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Speaker 1: over the last couple of decades. While many of us

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Speaker 1: experience it as virtual assistance in our phones and computers,

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Speaker 1: AI has been giving us listening, watching, and reading recommendations

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Speaker 1: for years and we continue to see it evolve and

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Speaker 1: even create content like images and written stories. But that's

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Speaker 1: all just the beginning. AI has so much potential to

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Speaker 1: positively impact the way we work and live. It can

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Speaker 1: be used to detect new variants and threats in agriculture

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Speaker 1: brought on by climate change conditions. The Intel Open Vino

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Speaker 1: technology played an essential role in this, providing higher accuracy

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Speaker 1: for detection. I'd just like to switch a little bit

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Speaker 1: to the agribusiness side of things. And maybe I can

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Speaker 1: get Shuita to comment on this in terms of the

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Speaker 1: Intel Open Vino and its app cation here for pest detection.

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Speaker 1: Do you see it complementing other pest control methods in

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Speaker 1: agriculture and does it have the potential to replace pesticides

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Speaker 1: and insecticides and farming replace.

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Speaker 5: Is a little on the harsher terms.

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Speaker 3: What I would actually look at it is AI and

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Speaker 3: agricultures really helping farmers make data driven decisions, optimize crop

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Speaker 3: yields conserved resources like water and energy. The challenge here

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Speaker 3: is not just the solution part of it is also

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Speaker 3: kind of encouraging next generation technologists student innovators to come together,

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Speaker 3: look at the local problems like what Neharikan risikation have done,

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Speaker 3: and then create a solution using all the skills they've

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Speaker 3: learned as part of their formal education as well as

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Speaker 3: as part of being a part of Intel programs the

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Speaker 3: Interdigital Rediness Program portfolio, come together and democratize AI skills

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Speaker 3: in a way which gets a common person a farmer,

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Speaker 3: to understand trust and emergingology like artificial intelligence and hopefully

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Speaker 3: become comfortable in applying it to solve the daily problems

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Speaker 3: they would be facing as part of their community.

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Speaker 1: I love that term democratization of technology, and I think

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Speaker 1: that's ultimately what technology does is get it more accessible

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Speaker 1: and cheaper to get it to the far regions of

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Speaker 1: the world. I'd just like to expand a little bit more,

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Speaker 1: maybe if you could explain some of the programs that

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Speaker 1: are available through inter Open VENO to help farmers or

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Speaker 1: businesses with limited resources to get access to this sort

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Speaker 1: of technology and expertise.

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Speaker 3: I'll just take a step back here, right because we

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Speaker 3: keep talking about increasing digitization, which today a lot of

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Speaker 3: governments and countries are going towards. But what it really

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Speaker 3: means is when we focus on increased digitization, we also

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Speaker 3: need to invest more in building the digital readiness of people,

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Speaker 3: specifically in terms of emerging in critical technologies like AI

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Speaker 3: or what you spoke about, like the usage of open Wino.

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Speaker 3: How do we get person to understand how they can

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Speaker 3: utilize the technology like open we know to be able

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Speaker 3: to solve their local problem and create indigender solutions. So

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Speaker 3: all this kind of comes together through a whole program

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Speaker 3: portfolio which we have which is called the Intel Digital

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Speaker 3: Readiness Programs, which is driven in collaboration with government, academia,

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Speaker 3: civil society, and the industry and focuses around building shared value,

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Speaker 3: shared responsibility so that we can really demystify democratize these

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Speaker 3: superpowers which we keep talking about, like artificial intelligence for

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Speaker 3: a very broader and a diverse audience for young budding

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Speaker 3: technologists like Neiharika Ushikish but also for those who are

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Speaker 3: going to be consuming the technology at the other end

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Speaker 3: of the spectrum. The programs are a lot, they're many.

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Speaker 3: They range from you know, programs like AI for Citizens,

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Speaker 3: which talks about getting a citizen to understand how to

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Speaker 3: navigate his or her life in an AI driven world.

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Speaker 3: AI for Youth, which really allows us to empower youth

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Speaker 3: with not just the technical skills associated with AI, but

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Speaker 3: also the social skills in a very inclusive manner. And

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Speaker 3: then we have AI for Future Workforce, which is for

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Speaker 3: vocational community college students, engineering students, which really helps them

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Speaker 3: to understand how to be prepare themselves for becoming a

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Speaker 3: part of the future workforce. So a huge spectrum, lots

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Speaker 3: of programs, but the one which is very special to

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Speaker 3: all three of us in this case, and I'm sure

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Speaker 3: Education Aherka would agree with that is our EI Global

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Speaker 3: Impact Festival, because this is where we work with all

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Speaker 3: these student innovators. We get them together and we get

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Speaker 3: them to celebrate their AI innovations with a huge passage

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Speaker 3: which does not just allow them to showcase what they've built,

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Speaker 3: but also helps them hone their skills by getting mentored

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Speaker 3: by Intel technologists because at the end of the day,

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Speaker 3: these young students are the next generation technologists, so we

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Speaker 3: want to make sure we work for them to support

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Speaker 3: and build an AI ready generation.

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Speaker 2: Platforms like these have been really instrumental and I have

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Speaker 2: seen the impact on ground that they make in supporting technologists,

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Speaker 2: young technologists like us, and we have always been very

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Speaker 2: grateful for the opportunities and mentorship as well that Intel

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Speaker 2: has provided. And that's something that we wish that every

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Speaker 2: budding technologist in India and all over the globe can

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Speaker 2: at least experience, because mentorship and guidance is an important

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Speaker 2: pillar of one's journey and having someone who can teach

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Speaker 2: you more about AI, how to use AI, and how

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Speaker 2: to benefit from AI, especially with the immense potential it

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Speaker 2: has that is life changing.

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Speaker 1: You're listening to technically speaking, an Intel podcast will be

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Speaker 1: right back. Welcome back to technically speaking an Intel podcast

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Speaker 1: shweeta last episode of this podcast, we talked with Reachhuvu,

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Speaker 1: one of your colleagues, about the ethics and responsibility of AIM,

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Speaker 1: wondering if we could get your thoughts on how you're

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Speaker 1: working with governments and industry leaders around AI and trying

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Speaker 1: to help them navigate some of the ethics and responsibilities

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Speaker 1: around AI development.

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Speaker 3: That's a very interesting question for us, right because when

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Speaker 3: we speak about digital reddiness or how do we build

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Speaker 3: digital readiness, we look at three pillars. Largely, one is,

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Speaker 3: of course learning the skills of emerging technologies like AI,

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Speaker 3: but more importantly, getting to understand and trust those skills,

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Speaker 3: So getting to understand not just what the advantages are,

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Speaker 3: but also what the pitfalls are. Getting to understand which

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Speaker 3: situation should we apply the emerging technology in and which

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Speaker 3: ones we should abstain from. So our programs, in fact,

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Speaker 3: inculcate a lot of discussions around these there is, which

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Speaker 3: range from the ethics piece of it, which range from

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Speaker 3: how how do we make it more inclusive, how do

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Speaker 3: we make it more diverse? And so much so that

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Speaker 3: if you kind of package it all together, it comes

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Speaker 3: under the larger umbrella of responsible AI. So how do

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Speaker 3: we really encourage not just youth, but every citizen, which

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Speaker 3: includes the governments who we collaborate with and partner with

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Speaker 3: to understand what is the responsible use of these superpowers

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Speaker 3: like AI to gain broader socioeconomic benefits for everybody.

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Speaker 2: As a youth igffellow. That is exactly what I focus

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Speaker 2: on Internet governance right and how AI governance works and

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Speaker 2: how AI can be regulated. But then what about AI innovation?

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Speaker 2: It shouldn't be regulated or stifled due to laws coming

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Speaker 2: into place that can have that effect where people continuate

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Speaker 2: and they can't contribute to new technologies, so that there's

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Speaker 2: a delicate balance between them, and that is what I

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Speaker 2: also do look into. And the whole area of how

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Speaker 2: becoming emerging technology is like even robotics which has a

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Speaker 2: huge artificient intelligence ethics background out there, So how do

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Speaker 2: we harness this without harming humanity? And that is something

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Speaker 2: that I believe all stakeholders, including the youth of our

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Speaker 2: country or the globe, should be focusing on because there

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Speaker 2: also tends to be the whole bias of youth not

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Speaker 2: being given a voice when it comes to these emerging technologies.

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Speaker 2: But I believe if they do understand what it is

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Speaker 2: about and what potential risks it has and what potential

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Speaker 2: benefits it has, that gives them the knowledge to use

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Speaker 2: it responsibly and ethically.

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Speaker 1: Using AI can be as complicated as Niharika has pointed out,

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Speaker 1: but the tool she and Wishikish have been able to

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Speaker 1: create from this place of innovation and AI have changed

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Speaker 1: the world for the better and they have the results

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Speaker 1: to prove it. In terms of the Kisheno technology that

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Speaker 1: you have developed, do you have any stats on the

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Speaker 1: crop that has been saved or the reduction in crop loss?

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Speaker 4: In twenty to twenty, we actually piloted this around in

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Speaker 4: eight districts in Orissa and more than around seventy two villages.

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Speaker 4: We actually serve it upon and piloted upon and for

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Speaker 4: one season we tried it particularly on wheats and tomatoes.

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Speaker 4: Once we had data that we could actually predict that

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Speaker 4: there is a pest attack or plant this is coming up,

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Speaker 4: we use that data to try to save those fifty villages.

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Speaker 4: We used pesticides and fertilizers just before whenever the pest

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Speaker 4: and pest attack could have happened, So it actually saved

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Speaker 4: around those fifty villages.

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Speaker 1: I'm really interested in how the technology actually is deployed

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Speaker 1: and distributed to as many villages as possible. To me,

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Speaker 1: the innovation is part of that as well. How do

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Speaker 1: you deploy it, how do you scale it? And you

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Speaker 1: said you went to seventy two villages, how did you

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Speaker 1: get to all of them and provide this service and

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Speaker 1: this knowledge.

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Speaker 5: In the local districts.

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Speaker 4: We contacted the administrations and with the recognitions we had

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Speaker 4: with until it was really easy to contact the administrations.

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Speaker 4: So once we had contacted the administration the local villagers, they

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Speaker 4: were actually understood, Okay, there is someone who is coming

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Speaker 4: to do something in their villages and it won't harm them,

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Speaker 4: So they were at least a relaxed that nothing is

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Speaker 4: going to be happening.

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Speaker 5: And also they actually co operated out.

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Speaker 4: So we had to draw the plots, We had to

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Speaker 4: map it on the satellite software that we had and

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Speaker 4: it would actually give us a satellite based crop image.

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Speaker 4: And for each crop images, we just needed to market

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Speaker 4: around the perimeters of that particular individual farmer and the

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Speaker 4: work is done. We just needed to understand how what

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Speaker 4: area that particular farmer has, what is the crop type?

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Speaker 4: When did so what is the raining patterns and what

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Speaker 4: is the soil type. With these certain parameters understood, the

440
00:24:50,640 --> 00:24:52,679
Speaker 4: farmer had to do nothing. We were sitting on a

441
00:24:52,760 --> 00:24:55,879
Speaker 4: room played server and we were training these images and

442
00:24:56,000 --> 00:24:58,240
Speaker 4: it was again the process kept on going. We had

443
00:24:58,240 --> 00:25:01,000
Speaker 4: the results each week, we just to share them. Okay,

444
00:25:01,000 --> 00:25:03,920
Speaker 4: this is the condition, this is what your crop health is,

445
00:25:04,480 --> 00:25:07,200
Speaker 4: and your crop is safe and if not, we will

446
00:25:07,200 --> 00:25:08,760
Speaker 4: at least give them some predictions.

447
00:25:09,320 --> 00:25:12,159
Speaker 2: One of the other plus points or advantages of our

448
00:25:12,200 --> 00:25:15,760
Speaker 2: innovation was how cost effective it was. So now this

449
00:25:15,800 --> 00:25:17,919
Speaker 2: is a huge issue when it comes to India that

450
00:25:18,000 --> 00:25:20,720
Speaker 2: technologies are out there, but they can be very expensive

451
00:25:20,760 --> 00:25:24,840
Speaker 2: and that's not reachable to a conventional Indian farmer. They

452
00:25:24,880 --> 00:25:28,679
Speaker 2: need solutions that are cost effective because of budget constraints

453
00:25:28,720 --> 00:25:31,159
Speaker 2: and that's what we provided. So that also helped in

454
00:25:31,200 --> 00:25:33,680
Speaker 2: the reach for them to know that there is a

455
00:25:33,720 --> 00:25:36,800
Speaker 2: device out there which is very cost effective, which won't

456
00:25:37,119 --> 00:25:40,800
Speaker 2: cost thousands and lacks of rupees for them, just a

457
00:25:40,880 --> 00:25:43,960
Speaker 2: dollar which is a meal a day, right, So that

458
00:25:44,320 --> 00:25:47,359
Speaker 2: amount of money to protect their crops that was huge

459
00:25:47,359 --> 00:25:50,119
Speaker 2: for them. So that also helped us make them acquainted

460
00:25:50,160 --> 00:25:52,520
Speaker 2: with the technology and the benefits of it.

461
00:25:54,480 --> 00:25:56,800
Speaker 1: At the cost of one dollar to use kishan No.

462
00:25:57,800 --> 00:26:01,879
Speaker 1: The America and Rishikish have made these resources accessible to

463
00:26:01,920 --> 00:26:05,439
Speaker 1: those who need it most, but being cost effective is

464
00:26:05,480 --> 00:26:08,520
Speaker 1: only half the battle. They had to work hand in

465
00:26:08,560 --> 00:26:11,760
Speaker 1: hand with the farmers to teach them how the technology worked.

466
00:26:12,600 --> 00:26:16,520
Speaker 1: But this technology had a more profound impact in identifying

467
00:26:16,520 --> 00:26:19,399
Speaker 1: the source of the crop loss. It also led to

468
00:26:19,440 --> 00:26:23,040
Speaker 1: revelations about the dangerous fertilizers and pesticides they were using.

469
00:26:25,760 --> 00:26:28,720
Speaker 1: How have you found the process of having the farmers

470
00:26:28,760 --> 00:26:31,720
Speaker 1: actually take some action based on the results that you

471
00:26:31,760 --> 00:26:32,640
Speaker 1: give them.

472
00:26:32,920 --> 00:26:36,320
Speaker 5: Initially, like they didn't understand what exactly we were trying

473
00:26:36,320 --> 00:26:36,560
Speaker 5: to do.

474
00:26:36,680 --> 00:26:39,520
Speaker 4: They just were, Okay, there's nothing harm in it, but

475
00:26:39,560 --> 00:26:42,639
Speaker 4: there's nothing good in it. So that's how it was.

476
00:26:43,200 --> 00:26:46,560
Speaker 4: So we actually startle if some visual based learning. Each

477
00:26:46,640 --> 00:26:48,760
Speaker 4: weekends we try to un make them understand what exactly

478
00:26:48,840 --> 00:26:52,399
Speaker 4: we were doing in just some graphics, cartoon type animations,

479
00:26:52,440 --> 00:26:54,280
Speaker 4: just to understand what exactly we are trying to do,

480
00:26:54,520 --> 00:26:56,960
Speaker 4: so that they're also getting literate about Okay, this is

481
00:26:57,040 --> 00:26:59,600
Speaker 4: a technology that they are paying for the cost of

482
00:26:59,680 --> 00:27:02,879
Speaker 4: for one acre of land in crop area was just

483
00:27:02,960 --> 00:27:06,240
Speaker 4: costing them around seventy troopees. That's around one dollar near

484
00:27:06,280 --> 00:27:08,359
Speaker 4: to one dollar, and it was a monthly based service,

485
00:27:08,840 --> 00:27:11,399
Speaker 4: so they were giving for each acre seventy troopees.

486
00:27:11,440 --> 00:27:13,119
Speaker 5: Each farmer would have been paying us.

487
00:27:13,320 --> 00:27:16,239
Speaker 4: The cost was just to handle out the server that

488
00:27:16,280 --> 00:27:19,919
Speaker 4: we were trying to maintain, and these informations that we

489
00:27:19,960 --> 00:27:21,560
Speaker 4: are trying to literate them with.

490
00:27:21,960 --> 00:27:24,520
Speaker 5: They understood at least some parts of the technology.

491
00:27:24,560 --> 00:27:28,159
Speaker 4: They understood how exactly the pest and plant disease affect

492
00:27:28,200 --> 00:27:31,240
Speaker 4: the crop, and what kind of pesticides, what kind of

493
00:27:31,280 --> 00:27:36,000
Speaker 4: fertilizers are actually affecting both the crops and both.

494
00:27:35,840 --> 00:27:38,000
Speaker 5: As humans when we consume that product.

495
00:27:38,040 --> 00:27:41,040
Speaker 4: So they also started to understand and they started to

496
00:27:41,080 --> 00:27:44,440
Speaker 4: stop using those pest sets and fertilizers for a particular

497
00:27:44,520 --> 00:27:47,800
Speaker 4: duration of time because in India, in particular crops, they

498
00:27:48,240 --> 00:27:51,040
Speaker 4: farmers just used to spray pesticides and fertilizers even if

499
00:27:51,080 --> 00:27:53,280
Speaker 4: they have not been attacked by any pests. This is

500
00:27:53,400 --> 00:27:56,439
Speaker 4: used to spray it before any pest infestation, just to

501
00:27:57,200 --> 00:28:00,280
Speaker 4: understand that it should be protected. But actually it's was

502
00:28:00,320 --> 00:28:04,719
Speaker 4: hampings as human beings because even if there is no

503
00:28:04,720 --> 00:28:08,199
Speaker 4: pest attack, we were actually consuming that pesticides and fertilizers.

504
00:28:08,720 --> 00:28:11,880
Speaker 2: It matters on how we present the data to farmers,

505
00:28:11,920 --> 00:28:15,480
Speaker 2: and this also ties into the whole digital literacy programs

506
00:28:15,520 --> 00:28:18,320
Speaker 2: that we wanted to run. And as the Religash mentioned,

507
00:28:18,400 --> 00:28:20,800
Speaker 2: we were trying to present the data to them in

508
00:28:20,840 --> 00:28:23,960
Speaker 2: a way that they could understand as an individual. Anne

509
00:28:24,040 --> 00:28:27,720
Speaker 2: I impact enthusiast. I believe that having that AI accessible

510
00:28:27,800 --> 00:28:30,960
Speaker 2: in regional languages is very important and that is something

511
00:28:31,000 --> 00:28:34,160
Speaker 2: that we try to incorporate as well. And even as

512
00:28:34,200 --> 00:28:38,640
Speaker 2: Retigish mentioned, like pesticides, when used unnecessarily, they do drive

513
00:28:38,680 --> 00:28:42,120
Speaker 2: the costs also, so the farmers, if you don't talk money,

514
00:28:42,160 --> 00:28:45,920
Speaker 2: they do understand that, right, So you can see, you know,

515
00:28:46,000 --> 00:28:50,320
Speaker 2: like all the pesticides that you have been using, you

516
00:28:50,360 --> 00:28:52,320
Speaker 2: don't have to use those much. You can just use

517
00:28:52,480 --> 00:28:54,240
Speaker 2: on the base of the data that we're giving you,

518
00:28:54,360 --> 00:28:56,160
Speaker 2: and that too in a very accessible form.

519
00:28:56,960 --> 00:29:01,360
Speaker 1: And Sweeta we talked a little bit about previously around

520
00:29:02,000 --> 00:29:06,920
Speaker 1: regulations and how Intel can assist the adoption of these

521
00:29:07,080 --> 00:29:09,880
Speaker 1: sorts of technologies. I mean, we heard from Risha, Kisha

522
00:29:09,920 --> 00:29:12,360
Speaker 1: and Erica that they had to sort of contact the

523
00:29:12,440 --> 00:29:16,400
Speaker 1: local administration bureaus to get permission. Maybe you could talk

524
00:29:16,400 --> 00:29:18,800
Speaker 1: a little bit about the way Intel can actually help

525
00:29:18,960 --> 00:29:23,000
Speaker 1: that process to get the technology down locally.

526
00:29:23,920 --> 00:29:28,760
Speaker 3: So all countries governments, both at the central government level

527
00:29:28,800 --> 00:29:31,840
Speaker 3: and at the local government level today are developing strategies

528
00:29:31,920 --> 00:29:34,320
Speaker 3: on how do you really take emerging technology to the

529
00:29:34,440 --> 00:29:38,360
Speaker 3: last mile or to the grassroot level. Nindia specifically has

530
00:29:38,360 --> 00:29:40,720
Speaker 3: a very rapus Daia strategy on how do you really

531
00:29:40,760 --> 00:29:47,160
Speaker 3: develop a sustainable, inclusive, positive impact on citizens by improving

532
00:29:47,240 --> 00:29:53,160
Speaker 3: public awareness, by helping them access public services which would

533
00:29:53,240 --> 00:29:56,880
Speaker 3: allow technology to become a part of their regular routine.

534
00:29:56,920 --> 00:29:58,440
Speaker 5: The way they work, the way they.

535
00:29:58,320 --> 00:30:04,600
Speaker 3: Function, such as what Niharika and Nishikisha developed can be

536
00:30:04,680 --> 00:30:07,200
Speaker 3: driven in a larger way, can be scaled with the

537
00:30:07,240 --> 00:30:09,560
Speaker 3: help of the local state government and we're already working

538
00:30:09,600 --> 00:30:13,400
Speaker 3: with multiple state governments to ensure that they create platforms

539
00:30:13,440 --> 00:30:16,640
Speaker 3: where these can be taken further. The idea or the

540
00:30:16,680 --> 00:30:19,640
Speaker 3: objective of our collaboration with the government is how do

541
00:30:19,720 --> 00:30:23,440
Speaker 3: we really bring AI everywhere in an extremely inclusive and

542
00:30:23,480 --> 00:30:26,840
Speaker 3: responsible manner. But a large obstacle which I see is

543
00:30:26,960 --> 00:30:31,520
Speaker 3: the availability of infrastructure right because for the adoption of technology,

544
00:30:31,880 --> 00:30:36,480
Speaker 3: we have to make sure that precision farming requires investments

545
00:30:36,480 --> 00:30:40,080
Speaker 3: in digital infrastructure at scale and now there are multiple

546
00:30:40,080 --> 00:30:42,400
Speaker 3: schemes and initiators which coment to in India is doing.

547
00:30:42,440 --> 00:30:44,560
Speaker 3: They're trying their best to improve the living standards of

548
00:30:44,600 --> 00:30:48,520
Speaker 3: Indian farmers, trying to support them in smart farming practices.

549
00:30:49,040 --> 00:30:51,719
Speaker 3: But apart from this, there are tax benefits, there are

550
00:30:51,760 --> 00:30:56,480
Speaker 3: financial grants, etc. Which can help accelerate the cost of

551
00:30:56,520 --> 00:30:57,960
Speaker 3: technology adoption.

552
00:30:58,920 --> 00:31:01,880
Speaker 1: In terms of AI, and it's becoming obviously more popular

553
00:31:01,920 --> 00:31:05,400
Speaker 1: across multiple industries. What's the number one thing you'd like

554
00:31:05,480 --> 00:31:09,600
Speaker 1: to try and solve using AI technology in the in farming.

555
00:31:10,160 --> 00:31:11,720
Speaker 1: I'll start with the Ahurica.

556
00:31:12,480 --> 00:31:13,479
Speaker 5: Thank you for the question.

557
00:31:14,280 --> 00:31:18,000
Speaker 2: So it's a wonderful question and I could think of

558
00:31:18,280 --> 00:31:20,880
Speaker 2: a million things that I could solve, and I'm pretty

559
00:31:20,880 --> 00:31:23,880
Speaker 2: sure the farmers would also agree with me. But one

560
00:31:23,920 --> 00:31:26,440
Speaker 2: of the things that I believe would be a very

561
00:31:27,040 --> 00:31:31,480
Speaker 2: huge issue that AI could potentially solve is protecting farmers

562
00:31:31,720 --> 00:31:34,920
Speaker 2: and their farms from climate change. Now, this is a

563
00:31:35,000 --> 00:31:38,560
Speaker 2: huge issue that's cropping. Our global climatic changes are worsening

564
00:31:38,640 --> 00:31:42,920
Speaker 2: every year. There's droughts everywhere, there's floods in some places,

565
00:31:43,240 --> 00:31:47,320
Speaker 2: So things like that farmers should be protected from natural

566
00:31:47,320 --> 00:31:51,400
Speaker 2: calamities disasters like that that could potentially just endanger their

567
00:31:51,400 --> 00:31:55,760
Speaker 2: livelihoods and destroy their economic and social levels, and that

568
00:31:56,120 --> 00:31:59,000
Speaker 2: is something that we should look into as AI enthusiast

569
00:31:59,080 --> 00:32:01,760
Speaker 2: on how to protect far from that, and that I

570
00:32:01,840 --> 00:32:05,560
Speaker 2: believe would be one way that AI could totally revolutionize

571
00:32:05,600 --> 00:32:06,840
Speaker 2: the agricultural industry.

572
00:32:07,600 --> 00:32:10,880
Speaker 1: Excellent, Rishi, Kishi, You've had extra time to think, so yeah.

573
00:32:11,400 --> 00:32:14,520
Speaker 4: So basically the area that I'm also currently working on,

574
00:32:14,840 --> 00:32:20,200
Speaker 4: that's the genomics selection of particular varieties in crop farms,

575
00:32:20,520 --> 00:32:22,960
Speaker 4: and that's one area that AI can be used to

576
00:32:23,040 --> 00:32:27,520
Speaker 4: analyze vast genomic data to identify genes associated with desirable

577
00:32:27,520 --> 00:32:30,960
Speaker 4: crop traits that can adapt to the climate change. Because

578
00:32:31,160 --> 00:32:33,840
Speaker 4: as you're proceeding, like we all know like where exactly

579
00:32:33,840 --> 00:32:36,360
Speaker 4: we are proceeding on, so the only way is to

580
00:32:36,400 --> 00:32:39,280
Speaker 4: adapt to the upcoming situations and to prevent it. So

581
00:32:39,400 --> 00:32:41,880
Speaker 4: I'm working on the adaption side of the climate change

582
00:32:42,240 --> 00:32:45,680
Speaker 4: in particularly farming. So we are trying to understand how

583
00:32:45,720 --> 00:32:48,080
Speaker 4: these AI tools and AI can be used. Machine learning

584
00:32:48,080 --> 00:32:50,760
Speaker 4: algorithms can be used to understand this various genomic data

585
00:32:50,800 --> 00:32:54,760
Speaker 4: and create new genomes that can actually accelerate breeding programs,

586
00:32:54,800 --> 00:32:59,240
Speaker 4: resulting in crops that are more disease resistant, nutritious.

587
00:32:58,640 --> 00:33:00,960
Speaker 5: And adaptable to changing emitic conditions.

588
00:33:01,200 --> 00:33:05,320
Speaker 4: So that's one area that can be a very huge

589
00:33:05,320 --> 00:33:07,320
Speaker 4: factor to revolutionize the farming sector.

590
00:33:08,040 --> 00:33:11,920
Speaker 1: And Shwita, what's the number one area of AI technology

591
00:33:11,920 --> 00:33:13,600
Speaker 1: you'd like to see.

592
00:33:13,800 --> 00:33:18,800
Speaker 3: Actually focus on most sustainable and economical farming which as

593
00:33:18,800 --> 00:33:22,360
Speaker 3: a result provides or becomes climate smart farming. So that

594
00:33:22,520 --> 00:33:26,080
Speaker 3: is where adoption of smart farming practices right, which would

595
00:33:26,120 --> 00:33:30,120
Speaker 3: really help grow India and the farmer and the community

596
00:33:30,400 --> 00:33:31,280
Speaker 3: to which they belong.

597
00:33:32,280 --> 00:33:36,920
Speaker 1: Excellent, excellent. I would like to thank my guests Rishi

598
00:33:37,000 --> 00:33:41,720
Speaker 1: kish Ahmit Nayak, Swita Karuna and Niharika Haridas for joining

599
00:33:41,760 --> 00:33:44,760
Speaker 1: me on this episode of Technically Speaking and Intel podcast.

600
00:33:46,320 --> 00:33:48,800
Speaker 1: This was a very enjoyable discussion for me as I

601
00:33:48,840 --> 00:33:52,080
Speaker 1: love talking with actual innovators, engineers and developers with fruits

602
00:33:52,080 --> 00:33:55,640
Speaker 1: on the ground deploying technology and making a difference. What

603
00:33:55,720 --> 00:33:57,720
Speaker 1: amakes me about the efforts was the use of the

604
00:33:57,760 --> 00:34:01,720
Speaker 1: Intel Open Vino platform and it seemingly casual use of it.

605
00:34:01,720 --> 00:34:03,600
Speaker 1: It was only a few years ago that running machine

606
00:34:03,680 --> 00:34:06,640
Speaker 1: learning in AR models was a massive undertaking. The kishan

607
00:34:06,760 --> 00:34:10,319
Speaker 1: No initiative that Ushikish and Erica have developed is a

608
00:34:10,320 --> 00:34:13,280
Speaker 1: testament to the ability for new AI tools like Intel

609
00:34:13,320 --> 00:34:16,160
Speaker 1: open Vino to speed up the development and deployment of

610
00:34:16,200 --> 00:34:20,360
Speaker 1: industry changing technology. It was also important to understand the

611
00:34:20,440 --> 00:34:23,719
Speaker 1: larger governmental impact on AI development. We heard from our

612
00:34:23,760 --> 00:34:26,040
Speaker 1: guests the importance of ensuring that we strive to reduce

613
00:34:26,080 --> 00:34:30,279
Speaker 1: any barriers to innovators from exploring, experimenting, and succeeding in

614
00:34:30,320 --> 00:34:34,560
Speaker 1: their AI efforts democratization of technology. By continually striving to

615
00:34:34,600 --> 00:34:37,800
Speaker 1: reduce the cost of AI deployments, two tools like Intel

616
00:34:37,880 --> 00:34:40,000
Speaker 1: open Vino will be a boomed not only to the

617
00:34:40,040 --> 00:34:42,960
Speaker 1: remote villages of India, but also in the tallest skyscrapers

618
00:34:43,000 --> 00:34:48,040
Speaker 1: of New York. Please join us on Tuesday, October thirty

619
00:34:48,080 --> 00:34:51,840
Speaker 1: first for the next episode of technically Speaking, an Intel podcast.

620
00:35:01,400 --> 00:35:04,880
Speaker 1: Technically Speaking was produced by Ruby Studios from iHeartRadio in

621
00:35:04,920 --> 00:35:08,799
Speaker 1: partnership with Intel, and hosted by me Graham Class. Our

622
00:35:08,840 --> 00:35:12,320
Speaker 1: executive producer is Molly Sosher, our EP of Post Production

623
00:35:12,400 --> 00:35:15,960
Speaker 1: is James Foster, and our supervising producer is Nikair Swinton.

624
00:35:16,800 --> 00:35:20,000
Speaker 1: This episode was edited by Cierra Spreen and written and

625
00:35:20,040 --> 00:35:21,440
Speaker 1: produced by Tyree Rush