Speaker 1: When a lot of us think of farming, it reminds
Speaker 1: us of simpler times, and perhaps it feels like one
Speaker 1: of the remaining industries exempt from the influences of the
Speaker 1: modern tech world. But imagine a world where the success
Speaker 1: of your family's farm crop yield is access to AI tools.
Speaker 1: There's so much labor and effort that goes into maintaining
Speaker 1: a farm, especially when farmers have to anticipate unpredictable weather
Speaker 1: patterns and unprecedented seasons brought on by climate change. Plants,
Speaker 1: like humans, are living things, with millions of tiny organisms
Speaker 1: both attacking and assisting their life cycle. Some threats to
Speaker 1: crop life are smaller than the human eye can see,
Speaker 1: and when not addressed, the results can be disastrous to
Speaker 1: local economies. But what if AI could solve the problem.
Speaker 1: Giving eyes and access to where farmers cannot reach. AI
Speaker 1: protects crops and the economy from the threat of microbial pests,
Speaker 1: resulting in a more prosperous tomorrow. Hey there, I'm gram
Speaker 1: Class and this is technically speaking an Intel podcast. The
Speaker 1: show is dedicated to highlighting the way technology is revolutionizing
Speaker 1: the way we live, work, and move. In every episode,
Speaker 1: we'll connect with innovators in areas like artificial intelligence to
Speaker 1: better understand the human centered technology they've developed. There has
Speaker 1: always been a disconnect between nature and technology. However, today
Speaker 1: there's a lot of science and technology at the core
Speaker 1: of modern farming, and we're not talking about GMOs. One
Speaker 1: of the biggest issues in agriculture is environmental threats. Farmers
Speaker 1: struggle with protecting crops from diseases and pests without using
Speaker 1: tools that could adversely affect consumers. AI has been instrumental
Speaker 1: in helping farmers detect pests before infestations occur and result
Speaker 1: in huge crop loss. But before we get into exactly
Speaker 1: how it all works, I want to introduce our guests.
Speaker 1: In twenty seventeen, Rishi kish amitt Nayak's family farm in
Speaker 1: India so ninety percent crop loss due to pest in infestation.
Speaker 1: After partnering with a fellow student, Niharika Haridas, the two Megatronics,
Speaker 1: Robotics and automation engineering students found a way to use
Speaker 1: AI to develop a method that could detect crop pests
Speaker 1: through thermal imaging. This system, called kishan No, has been
Speaker 1: proven effective and very affordable to local farmers. Rishikish America
Speaker 1: thanks for being here.
Speaker 2: Such a pleasure to be here, Graham, thank you for
Speaker 2: the invitation.
Speaker 1: We're also joined by Shwita Karuna, intel's Director of Government
Speaker 1: Partnerships and Initiatives for Japan and the Asia Pacific. Sharita
Speaker 1: has over twenty three years of experience creating trusted government
Speaker 1: relationships and fostering government programs that encouraged the implementation of
Speaker 1: modern science into the workforce PLUSH. She was instrumental in
Speaker 1: helping kishan No grow as a farming tactic across the region. Welcome, Shwrita.
Speaker 3: Thank you, Graham, such a pleasure being here.
Speaker 1: So let's start at the beginning a very interesting story
Speaker 1: around rishikish Can you tell a little bit about the
Speaker 1: problem that your family and other farmers experienced back in
Speaker 1: twenty seventeen.
Speaker 4: In India particularly, it's an agricultural country, so more than
Speaker 4: seventy percent of the people do agriculture as their own
Speaker 4: major occupation. In twenty seventeen, my father's grandfather was completely
Speaker 4: invested into agricultural farming, and during that time, in Orisa particularly,
Speaker 4: there was a plant best attack that couldn't be identified
Speaker 4: for a longer period of time, and that resulted in
Speaker 4: a lot of crop losses and hectares of land was
Speaker 4: just lost because of an unidentified pist. Personally, we saw
Speaker 4: a lot of farmer suicides in our own village, and
Speaker 4: that was the major reason when I thought, Okay, I
Speaker 4: do have a background of engineering, I do have a
Speaker 4: background of robotics, so why not to create something for
Speaker 4: our own farmers. And being part of that family where
Speaker 4: we do farming in our parental site, I was just
Speaker 4: touched with that fact that I need to do something
Speaker 4: for the farmers.
Speaker 1: In Rishikishi's village alone, there were four farmers who took
Speaker 1: their lives as a result of the devastated crop, and
Speaker 1: his family saw a ninety percent crop loss that year.
Speaker 1: The infestation was so devastating to their livelihood his family
Speaker 1: considered leaving farming all together. And to make matters worse,
Speaker 1: the problem was difficult to identify and trace. Before we
Speaker 1: get into the actual details of how you solved it
Speaker 1: in Arika, how did you get involved in the project.
Speaker 2: I decided to pursue mecatronics and automation at Viatchene out
Speaker 2: of a sheer passion for robotics as a twelfth grader.
Speaker 2: So I came across the work that many companies like
Speaker 2: Boston Dynamics were doing at that point and exactly right
Speaker 2: the spot pro vote of course, and I was just
Speaker 2: enthralled with potential that it helped, Like it was like,
Speaker 2: oh my, what this could change humanity?
Speaker 5: And I was like, I need.
Speaker 2: To do something in this space. I wanted to help
Speaker 2: people with this new technology. And that's how I went
Speaker 2: to Aitchen and that's where I'm Metri Shikish and we
Speaker 2: started talking and we were talking about this project and
Speaker 2: I was like, you know, that's that's amazing that we'll
Speaker 2: let me contribute to it as well, and that's how
Speaker 2: we started collaborating on the project and then we participated
Speaker 2: in the Inaugril Intelliet Global Impact Festival and the rest
Speaker 2: is history. We had a wonderful time and you know,
Speaker 2: the support that we have gotten from Intel for it
Speaker 2: as well has been phenomenal and that's the reason that
Speaker 2: Kishano is at the place where it is right now.
Speaker 1: Excellent. So now as the I guess the sixty four
Speaker 1: thousand dollars question is how does the kishan No work.
Speaker 4: Kishano basically taps into saturate based thermal imagery. These images
Speaker 4: can detect temperature variations and crops which often indicate Streuss
Speaker 4: disease or pestal activity. For instance, areas affected by certain
Speaker 4: pest or microbol infestations may exhibit different thermal patterns compared
Speaker 4: to healthy areas. We collect images from Sentinel two and
Speaker 4: lands At eight satellites. Those satellite images are then sys
Speaker 4: to get index mapping out likes, for example, vegetative indexes
Speaker 4: and moisture indexes through a software called QGIS, so it
Speaker 4: basically gives us the values for those vegetative indexes and
Speaker 4: moisture indexes, and these gathered thermal imageries processed using AA algorithms,
Speaker 4: where we've processed the images first into the open Veno
Speaker 4: platform and we get a d blood image for better
Speaker 4: accuracy of training of the models. Then these algorithms are
Speaker 4: trained to recognize patterns or animalies that correspond to microbilan
Speaker 4: pest outbreaks. Over time, has more data is collected and analyzed,
Speaker 4: the AA model becomes more accurate and efficient in its
Speaker 4: prediction and leveraging the power of machine learning. Once a
Speaker 4: potential threat is identified in the system, the systems can
Speaker 4: send alerts or recommendations to the farmers in the local
Speaker 4: administrative levels, where we also design the physical device apart
Speaker 4: from the AA algorithm to get a confirmatory test that
Speaker 4: there is a pest or plant disease outbreak. This actually
Speaker 4: includes information about the type of threat, it's severe, and
Speaker 4: recommendation algorithms.
Speaker 5: This proactive approach.
Speaker 4: Helps farmers to take actions before the problem becomes widespread
Speaker 4: and saving both time and resources.
Speaker 1: I'd like to talk about Intel open Veno a little
Speaker 1: bit so quickly, just to inform our audience. INTE open
Speaker 1: Vino is a cross platform toolkit developed by Intel that
Speaker 1: deploys deep learning models on visual data sets, helping computers
Speaker 1: better recognize and process images to inform decision making. But
Speaker 1: I'm curious as someone who's just as interested in what
Speaker 1: didn't work as opposed to what ultimately does. Why did
Speaker 1: you decide to use Intel open Veno. Were there are
Speaker 1: other methods you tried first?
Speaker 2: So we did try a lot of techniques, and we
Speaker 2: found that open Veno worked perfectly with our project, especially
Speaker 2: with the goal that we were trying to achieve. So
Speaker 2: we saw that the hardware requirements as well as the
Speaker 2: software requirements did completely match. Also, we had mentorship from
Speaker 2: Intel and we were able to properly and in a
Speaker 2: better way adapt to those systems to our project, and
Speaker 2: that's the reason which it was open.
Speaker 4: We know, we actually tried to degler images through some
Speaker 4: deep learning algorithms, but those algorithms was actually not satisfying
Speaker 4: the accuracy that we actually wanted, so open Veno just
Speaker 4: suited out the case perfectly.
Speaker 1: One thing I'm interested in is the pests that were
Speaker 1: being detected. Am I right in saying that it had
Speaker 1: a unique therm signature?
Speaker 5: Yeah?
Speaker 1: And how did you discover that?
Speaker 4: In twenty seventeen, Once we identified the problem, we actually
Speaker 4: tried to create a physical device through a thermal camera
Speaker 4: set up and microprocesses. We were rotating that device among
Speaker 4: the periphery of the crop fields to understand what exactly
Speaker 4: the thermal traces are in the leaf of the crop plants.
Speaker 4: And once we understood what are the thermal signatures for
Speaker 4: different crop plants, we understood there is a concept that
Speaker 4: whenever there is a pathogen or a plant disease, there
Speaker 4: is a certain increase in the leaf temperature. And if
Speaker 4: we identify that leaf temperature increases in the particular or
Speaker 4: in a particular duration of time, we can actually significantly
Speaker 4: say that there is a best attack or a plant
Speaker 4: disease in the crop area. Once we had the theory,
Speaker 4: we tried to incorporate that similar formula in the vegetative
Speaker 4: index of the satellite setup. So in twenty nineteen we
Speaker 4: had the physical setup, we tried the same literature to
Speaker 4: understand it to the satellites.
Speaker 1: Hearing Rishikish and Aharika elaborate on how they design their
Speaker 1: imaging tool reminded me of my own experience attempting to
Speaker 1: develop systems to work remotely in the jungles of Africa.
Speaker 1: It's not an easy feat though, as there's no real
Speaker 1: infrastructure for these sorts of products, especially when they are
Speaker 1: limited by internet access and availability in the area. Hearing
Speaker 1: how much progress these two had made with their program,
Speaker 1: maybe wonder about the challenges that went into making this
Speaker 1: tool available in the rural farmlands of India.
Speaker 2: There has always been a digital divide in India, as
Speaker 2: we can see, but now it's been narrowing and that's
Speaker 2: a very good news for all of us, and that
Speaker 2: infrastructure is also becoming better. There's also research that India
Speaker 2: has one of the cheapest internet out there in the world,
Speaker 2: so I mean, it's being adapted and we are glad
Speaker 2: that it is. But when we were working on it,
Speaker 2: we did face a lot of infrastructure issues regarding internet
Speaker 2: services as well and internet connectivity exactly, and sort of
Speaker 2: having that satellite imagery. Gaining access to the satellite imagery
Speaker 2: was very difficult for us because that area wasn't mapped.
Speaker 2: Remote areas aren't usually mapped with that much precision as
Speaker 2: that of let's say, an urban area, so we did
Speaker 2: have some issues with that, but then we did try
Speaker 2: our best to solve those and gain satellite images from
Speaker 2: the areas that we neated.
Speaker 4: Farmers in the villages particularly, they were quite a bit
Speaker 4: skeptical to try this out, and the farms because in
Speaker 4: India particularly didn't back that time, we didn't have that
Speaker 4: much of agritechnology tools or products, and going as a
Speaker 4: youngster something around in class ninth or tenth and trying
Speaker 4: out as some different new projects or new census in
Speaker 4: the field, they were quite a bit skeptical. So managing
Speaker 4: that side of that, Okay, we are doing something good,
Speaker 4: we are doing something better for your own crops, we
Speaker 4: are doing something for the best of the society. Convincing
Speaker 4: them was one of the very huge challenge over there
Speaker 4: in India.
Speaker 1: What kind of data or training processes were involved in
Speaker 1: training the model to recognize microbiopests in the crops.
Speaker 5: Initially it was only deep learning algorithms.
Speaker 4: Further on, when we had a lot of thermal praise
Speaker 4: data and we had did the d blood images, we
Speaker 4: were just focused on the CNN models to train the data.
Speaker 4: And it hadn't given a good accuracy of for around
Speaker 4: ninety points something percentage, so it was a pretty good
Speaker 4: accurate to start with for a particular set of crops.
Speaker 1: You said, CNN, could you just define what that is please?
Speaker 5: Conventional neural network.
Speaker 1: Okay, And that's just another AI technique to for learning.
Speaker 5: Yeah, yes, a machine learning okay, okay.
Speaker 1: And you just mentioned about the accuracy that you achieved.
Speaker 1: Would you say that's typical for the Intel Open Veno
Speaker 1: platform to get that sort of result.
Speaker 4: The accuracy is for the total accuracy of the model
Speaker 4: for a particular set of crops, for example, tomatoes and wheat.
Speaker 4: For those two crops we had an accuracy fround ninety
Speaker 4: point two eight percentage, and for other crops it's still
Speaker 4: in the process of getting more accurate and all. So
Speaker 4: for these two crops, overly, it was the accuracy that
Speaker 4: we measured out and.
Speaker 1: In terms of the Intel Open Veno technology, can you
Speaker 1: think of anything any other farming use cases beyond pest
Speaker 1: management and crop protection.
Speaker 5: Currently, we were trying to work on crop genome analysis
Speaker 5: where we were actually trying to understand because of the
Speaker 5: climate change to the new variants of crops are needed
Speaker 5: to adapt to the new climatic conditions. So we were
Speaker 5: trying to understand how exactly we can use machine learning
Speaker 5: algorithms to create new genomes in the crops the microbiology
Speaker 5: side of it.
Speaker 4: So yeah, that's one area that I was completely focused
Speaker 4: on in this past recent days.
Speaker 2: I would like to add on to that, and as
Speaker 2: Education mentioned, convolutional neural network model that we used, it
Speaker 2: was at that point not something that was used by
Speaker 2: the AI community, but then we now see a lot
Speaker 2: of use cases for that and that's something that we
Speaker 2: are very glad about. And also some of the use
Speaker 2: cases that I have at least found as an AI
Speaker 2: enthusiast that models like these could have is in real
Speaker 2: time data, especially as the climatic change has become a
Speaker 2: huge issue. It is something that can help a lot
Speaker 2: of farmers with when there is excessive rains or when
Speaker 2: there is no rain at all, to predict these through
Speaker 2: AIML technologies. And I believe that the limit is boundless
Speaker 2: when it comes to AI technologies. Right we are seeing
Speaker 2: a start of a new era of AI, and I
Speaker 2: am very glad to see how I was being used
Speaker 2: by lots of companies, and we also hope to go
Speaker 2: contribute to that, and I hope for a very bright future.
Speaker 1: AI has been the focus of a lot of discourse
Speaker 1: over the last couple of decades. While many of us
Speaker 1: experience it as virtual assistance in our phones and computers,
Speaker 1: AI has been giving us listening, watching, and reading recommendations
Speaker 1: for years and we continue to see it evolve and
Speaker 1: even create content like images and written stories. But that's
Speaker 1: all just the beginning. AI has so much potential to
Speaker 1: positively impact the way we work and live. It can
Speaker 1: be used to detect new variants and threats in agriculture
Speaker 1: brought on by climate change conditions. The Intel Open Vino
Speaker 1: technology played an essential role in this, providing higher accuracy
Speaker 1: for detection. I'd just like to switch a little bit
Speaker 1: to the agribusiness side of things. And maybe I can
Speaker 1: get Shuita to comment on this in terms of the
Speaker 1: Intel Open Vino and its app cation here for pest detection.
Speaker 1: Do you see it complementing other pest control methods in
Speaker 1: agriculture and does it have the potential to replace pesticides
Speaker 1: and insecticides and farming replace.
Speaker 5: Is a little on the harsher terms.
Speaker 3: What I would actually look at it is AI and
Speaker 3: agricultures really helping farmers make data driven decisions, optimize crop
Speaker 3: yields conserved resources like water and energy. The challenge here
Speaker 3: is not just the solution part of it is also
Speaker 3: kind of encouraging next generation technologists student innovators to come together,
Speaker 3: look at the local problems like what Neharikan risikation have done,
Speaker 3: and then create a solution using all the skills they've
Speaker 3: learned as part of their formal education as well as
Speaker 3: as part of being a part of Intel programs the
Speaker 3: Interdigital Rediness Program portfolio, come together and democratize AI skills
Speaker 3: in a way which gets a common person a farmer,
Speaker 3: to understand trust and emergingology like artificial intelligence and hopefully
Speaker 3: become comfortable in applying it to solve the daily problems
Speaker 3: they would be facing as part of their community.
Speaker 1: I love that term democratization of technology, and I think
Speaker 1: that's ultimately what technology does is get it more accessible
Speaker 1: and cheaper to get it to the far regions of
Speaker 1: the world. I'd just like to expand a little bit more,
Speaker 1: maybe if you could explain some of the programs that
Speaker 1: are available through inter Open VENO to help farmers or
Speaker 1: businesses with limited resources to get access to this sort
Speaker 1: of technology and expertise.
Speaker 3: I'll just take a step back here, right because we
Speaker 3: keep talking about increasing digitization, which today a lot of
Speaker 3: governments and countries are going towards. But what it really
Speaker 3: means is when we focus on increased digitization, we also
Speaker 3: need to invest more in building the digital readiness of people,
Speaker 3: specifically in terms of emerging in critical technologies like AI
Speaker 3: or what you spoke about, like the usage of open Wino.
Speaker 3: How do we get person to understand how they can
Speaker 3: utilize the technology like open we know to be able
Speaker 3: to solve their local problem and create indigender solutions. So
Speaker 3: all this kind of comes together through a whole program
Speaker 3: portfolio which we have which is called the Intel Digital
Speaker 3: Readiness Programs, which is driven in collaboration with government, academia,
Speaker 3: civil society, and the industry and focuses around building shared value,
Speaker 3: shared responsibility so that we can really demystify democratize these
Speaker 3: superpowers which we keep talking about, like artificial intelligence for
Speaker 3: a very broader and a diverse audience for young budding
Speaker 3: technologists like Neiharika Ushikish but also for those who are
Speaker 3: going to be consuming the technology at the other end
Speaker 3: of the spectrum. The programs are a lot, they're many.
Speaker 3: They range from you know, programs like AI for Citizens,
Speaker 3: which talks about getting a citizen to understand how to
Speaker 3: navigate his or her life in an AI driven world.
Speaker 3: AI for Youth, which really allows us to empower youth
Speaker 3: with not just the technical skills associated with AI, but
Speaker 3: also the social skills in a very inclusive manner. And
Speaker 3: then we have AI for Future Workforce, which is for
Speaker 3: vocational community college students, engineering students, which really helps them
Speaker 3: to understand how to be prepare themselves for becoming a
Speaker 3: part of the future workforce. So a huge spectrum, lots
Speaker 3: of programs, but the one which is very special to
Speaker 3: all three of us in this case, and I'm sure
Speaker 3: Education Aherka would agree with that is our EI Global
Speaker 3: Impact Festival, because this is where we work with all
Speaker 3: these student innovators. We get them together and we get
Speaker 3: them to celebrate their AI innovations with a huge passage
Speaker 3: which does not just allow them to showcase what they've built,
Speaker 3: but also helps them hone their skills by getting mentored
Speaker 3: by Intel technologists because at the end of the day,
Speaker 3: these young students are the next generation technologists, so we
Speaker 3: want to make sure we work for them to support
Speaker 3: and build an AI ready generation.
Speaker 2: Platforms like these have been really instrumental and I have
Speaker 2: seen the impact on ground that they make in supporting technologists,
Speaker 2: young technologists like us, and we have always been very
Speaker 2: grateful for the opportunities and mentorship as well that Intel
Speaker 2: has provided. And that's something that we wish that every
Speaker 2: budding technologist in India and all over the globe can
Speaker 2: at least experience, because mentorship and guidance is an important
Speaker 2: pillar of one's journey and having someone who can teach
Speaker 2: you more about AI, how to use AI, and how
Speaker 2: to benefit from AI, especially with the immense potential it
Speaker 2: has that is life changing.
Speaker 1: You're listening to technically speaking, an Intel podcast will be
Speaker 1: right back. Welcome back to technically speaking an Intel podcast
Speaker 1: shweeta last episode of this podcast, we talked with Reachhuvu,
Speaker 1: one of your colleagues, about the ethics and responsibility of AIM,
Speaker 1: wondering if we could get your thoughts on how you're
Speaker 1: working with governments and industry leaders around AI and trying
Speaker 1: to help them navigate some of the ethics and responsibilities
Speaker 1: around AI development.
Speaker 3: That's a very interesting question for us, right because when
Speaker 3: we speak about digital reddiness or how do we build
Speaker 3: digital readiness, we look at three pillars. Largely, one is,
Speaker 3: of course learning the skills of emerging technologies like AI,
Speaker 3: but more importantly, getting to understand and trust those skills,
Speaker 3: So getting to understand not just what the advantages are,
Speaker 3: but also what the pitfalls are. Getting to understand which
Speaker 3: situation should we apply the emerging technology in and which
Speaker 3: ones we should abstain from. So our programs, in fact,
Speaker 3: inculcate a lot of discussions around these there is, which
Speaker 3: range from the ethics piece of it, which range from
Speaker 3: how how do we make it more inclusive, how do
Speaker 3: we make it more diverse? And so much so that
Speaker 3: if you kind of package it all together, it comes
Speaker 3: under the larger umbrella of responsible AI. So how do
Speaker 3: we really encourage not just youth, but every citizen, which
Speaker 3: includes the governments who we collaborate with and partner with
Speaker 3: to understand what is the responsible use of these superpowers
Speaker 3: like AI to gain broader socioeconomic benefits for everybody.
Speaker 2: As a youth igffellow. That is exactly what I focus
Speaker 2: on Internet governance right and how AI governance works and
Speaker 2: how AI can be regulated. But then what about AI innovation?
Speaker 2: It shouldn't be regulated or stifled due to laws coming
Speaker 2: into place that can have that effect where people continuate
Speaker 2: and they can't contribute to new technologies, so that there's
Speaker 2: a delicate balance between them, and that is what I
Speaker 2: also do look into. And the whole area of how
Speaker 2: becoming emerging technology is like even robotics which has a
Speaker 2: huge artificient intelligence ethics background out there, So how do
Speaker 2: we harness this without harming humanity? And that is something
Speaker 2: that I believe all stakeholders, including the youth of our
Speaker 2: country or the globe, should be focusing on because there
Speaker 2: also tends to be the whole bias of youth not
Speaker 2: being given a voice when it comes to these emerging technologies.
Speaker 2: But I believe if they do understand what it is
Speaker 2: about and what potential risks it has and what potential
Speaker 2: benefits it has, that gives them the knowledge to use
Speaker 2: it responsibly and ethically.
Speaker 1: Using AI can be as complicated as Niharika has pointed out,
Speaker 1: but the tool she and Wishikish have been able to
Speaker 1: create from this place of innovation and AI have changed
Speaker 1: the world for the better and they have the results
Speaker 1: to prove it. In terms of the Kisheno technology that
Speaker 1: you have developed, do you have any stats on the
Speaker 1: crop that has been saved or the reduction in crop loss?
Speaker 4: In twenty to twenty, we actually piloted this around in
Speaker 4: eight districts in Orissa and more than around seventy two villages.
Speaker 4: We actually serve it upon and piloted upon and for
Speaker 4: one season we tried it particularly on wheats and tomatoes.
Speaker 4: Once we had data that we could actually predict that
Speaker 4: there is a pest attack or plant this is coming up,
Speaker 4: we use that data to try to save those fifty villages.
Speaker 4: We used pesticides and fertilizers just before whenever the pest
Speaker 4: and pest attack could have happened, So it actually saved
Speaker 4: around those fifty villages.
Speaker 1: I'm really interested in how the technology actually is deployed
Speaker 1: and distributed to as many villages as possible. To me,
Speaker 1: the innovation is part of that as well. How do
Speaker 1: you deploy it, how do you scale it? And you
Speaker 1: said you went to seventy two villages, how did you
Speaker 1: get to all of them and provide this service and
Speaker 1: this knowledge.
Speaker 5: In the local districts.
Speaker 4: We contacted the administrations and with the recognitions we had
Speaker 4: with until it was really easy to contact the administrations.
Speaker 4: So once we had contacted the administration the local villagers, they
Speaker 4: were actually understood, Okay, there is someone who is coming
Speaker 4: to do something in their villages and it won't harm them,
Speaker 4: So they were at least a relaxed that nothing is
Speaker 4: going to be happening.
Speaker 5: And also they actually co operated out.
Speaker 4: So we had to draw the plots, We had to
Speaker 4: map it on the satellite software that we had and
Speaker 4: it would actually give us a satellite based crop image.
Speaker 4: And for each crop images, we just needed to market
Speaker 4: around the perimeters of that particular individual farmer and the
Speaker 4: work is done. We just needed to understand how what
Speaker 4: area that particular farmer has, what is the crop type?
Speaker 4: When did so what is the raining patterns and what
Speaker 4: is the soil type. With these certain parameters understood, the
Speaker 4: farmer had to do nothing. We were sitting on a
Speaker 4: room played server and we were training these images and
Speaker 4: it was again the process kept on going. We had
Speaker 4: the results each week, we just to share them. Okay,
Speaker 4: this is the condition, this is what your crop health is,
Speaker 4: and your crop is safe and if not, we will
Speaker 4: at least give them some predictions.
Speaker 2: One of the other plus points or advantages of our
Speaker 2: innovation was how cost effective it was. So now this
Speaker 2: is a huge issue when it comes to India that
Speaker 2: technologies are out there, but they can be very expensive
Speaker 2: and that's not reachable to a conventional Indian farmer. They
Speaker 2: need solutions that are cost effective because of budget constraints
Speaker 2: and that's what we provided. So that also helped in
Speaker 2: the reach for them to know that there is a
Speaker 2: device out there which is very cost effective, which won't
Speaker 2: cost thousands and lacks of rupees for them, just a
Speaker 2: dollar which is a meal a day, right, So that
Speaker 2: amount of money to protect their crops that was huge
Speaker 2: for them. So that also helped us make them acquainted
Speaker 2: with the technology and the benefits of it.
Speaker 1: At the cost of one dollar to use kishan No.
Speaker 1: The America and Rishikish have made these resources accessible to
Speaker 1: those who need it most, but being cost effective is
Speaker 1: only half the battle. They had to work hand in
Speaker 1: hand with the farmers to teach them how the technology worked.
Speaker 1: But this technology had a more profound impact in identifying
Speaker 1: the source of the crop loss. It also led to
Speaker 1: revelations about the dangerous fertilizers and pesticides they were using.
Speaker 1: How have you found the process of having the farmers
Speaker 1: actually take some action based on the results that you
Speaker 1: give them.
Speaker 5: Initially, like they didn't understand what exactly we were trying
Speaker 5: to do.
Speaker 4: They just were, Okay, there's nothing harm in it, but
Speaker 4: there's nothing good in it. So that's how it was.
Speaker 4: So we actually startle if some visual based learning. Each
Speaker 4: weekends we try to un make them understand what exactly
Speaker 4: we were doing in just some graphics, cartoon type animations,
Speaker 4: just to understand what exactly we are trying to do,
Speaker 4: so that they're also getting literate about Okay, this is
Speaker 4: a technology that they are paying for the cost of
Speaker 4: for one acre of land in crop area was just
Speaker 4: costing them around seventy troopees. That's around one dollar near
Speaker 4: to one dollar, and it was a monthly based service,
Speaker 4: so they were giving for each acre seventy troopees.
Speaker 5: Each farmer would have been paying us.
Speaker 4: The cost was just to handle out the server that
Speaker 4: we were trying to maintain, and these informations that we
Speaker 4: are trying to literate them with.
Speaker 5: They understood at least some parts of the technology.
Speaker 4: They understood how exactly the pest and plant disease affect
Speaker 4: the crop, and what kind of pesticides, what kind of
Speaker 4: fertilizers are actually affecting both the crops and both.
Speaker 5: As humans when we consume that product.
Speaker 4: So they also started to understand and they started to
Speaker 4: stop using those pest sets and fertilizers for a particular
Speaker 4: duration of time because in India, in particular crops, they
Speaker 4: farmers just used to spray pesticides and fertilizers even if
Speaker 4: they have not been attacked by any pests. This is
Speaker 4: used to spray it before any pest infestation, just to
Speaker 4: understand that it should be protected. But actually it's was
Speaker 4: hampings as human beings because even if there is no
Speaker 4: pest attack, we were actually consuming that pesticides and fertilizers.
Speaker 2: It matters on how we present the data to farmers,
Speaker 2: and this also ties into the whole digital literacy programs
Speaker 2: that we wanted to run. And as the Religash mentioned,
Speaker 2: we were trying to present the data to them in
Speaker 2: a way that they could understand as an individual. Anne
Speaker 2: I impact enthusiast. I believe that having that AI accessible
Speaker 2: in regional languages is very important and that is something
Speaker 2: that we try to incorporate as well. And even as
Speaker 2: Retigish mentioned, like pesticides, when used unnecessarily, they do drive
Speaker 2: the costs also, so the farmers, if you don't talk money,
Speaker 2: they do understand that, right, So you can see, you know,
Speaker 2: like all the pesticides that you have been using, you
Speaker 2: don't have to use those much. You can just use
Speaker 2: on the base of the data that we're giving you,
Speaker 2: and that too in a very accessible form.
Speaker 1: And Sweeta we talked a little bit about previously around
Speaker 1: regulations and how Intel can assist the adoption of these
Speaker 1: sorts of technologies. I mean, we heard from Risha, Kisha
Speaker 1: and Erica that they had to sort of contact the
Speaker 1: local administration bureaus to get permission. Maybe you could talk
Speaker 1: a little bit about the way Intel can actually help
Speaker 1: that process to get the technology down locally.
Speaker 3: So all countries governments, both at the central government level
Speaker 3: and at the local government level today are developing strategies
Speaker 3: on how do you really take emerging technology to the
Speaker 3: last mile or to the grassroot level. Nindia specifically has
Speaker 3: a very rapus Daia strategy on how do you really
Speaker 3: develop a sustainable, inclusive, positive impact on citizens by improving
Speaker 3: public awareness, by helping them access public services which would
Speaker 3: allow technology to become a part of their regular routine.
Speaker 5: The way they work, the way they.
Speaker 3: Function, such as what Niharika and Nishikisha developed can be
Speaker 3: driven in a larger way, can be scaled with the
Speaker 3: help of the local state government and we're already working
Speaker 3: with multiple state governments to ensure that they create platforms
Speaker 3: where these can be taken further. The idea or the
Speaker 3: objective of our collaboration with the government is how do
Speaker 3: we really bring AI everywhere in an extremely inclusive and
Speaker 3: responsible manner. But a large obstacle which I see is
Speaker 3: the availability of infrastructure right because for the adoption of technology,
Speaker 3: we have to make sure that precision farming requires investments
Speaker 3: in digital infrastructure at scale and now there are multiple
Speaker 3: schemes and initiators which coment to in India is doing.
Speaker 3: They're trying their best to improve the living standards of
Speaker 3: Indian farmers, trying to support them in smart farming practices.
Speaker 3: But apart from this, there are tax benefits, there are
Speaker 3: financial grants, etc. Which can help accelerate the cost of
Speaker 3: technology adoption.
Speaker 1: In terms of AI, and it's becoming obviously more popular
Speaker 1: across multiple industries. What's the number one thing you'd like
Speaker 1: to try and solve using AI technology in the in farming.
Speaker 1: I'll start with the Ahurica.
Speaker 5: Thank you for the question.
Speaker 2: So it's a wonderful question and I could think of
Speaker 2: a million things that I could solve, and I'm pretty
Speaker 2: sure the farmers would also agree with me. But one
Speaker 2: of the things that I believe would be a very
Speaker 2: huge issue that AI could potentially solve is protecting farmers
Speaker 2: and their farms from climate change. Now, this is a
Speaker 2: huge issue that's cropping. Our global climatic changes are worsening
Speaker 2: every year. There's droughts everywhere, there's floods in some places,
Speaker 2: So things like that farmers should be protected from natural
Speaker 2: calamities disasters like that that could potentially just endanger their
Speaker 2: livelihoods and destroy their economic and social levels, and that
Speaker 2: is something that we should look into as AI enthusiast
Speaker 2: on how to protect far from that, and that I
Speaker 2: believe would be one way that AI could totally revolutionize
Speaker 2: the agricultural industry.
Speaker 1: Excellent, Rishi, Kishi, You've had extra time to think, so yeah.
Speaker 4: So basically the area that I'm also currently working on,
Speaker 4: that's the genomics selection of particular varieties in crop farms,
Speaker 4: and that's one area that AI can be used to
Speaker 4: analyze vast genomic data to identify genes associated with desirable
Speaker 4: crop traits that can adapt to the climate change. Because
Speaker 4: as you're proceeding, like we all know like where exactly
Speaker 4: we are proceeding on, so the only way is to
Speaker 4: adapt to the upcoming situations and to prevent it. So
Speaker 4: I'm working on the adaption side of the climate change
Speaker 4: in particularly farming. So we are trying to understand how
Speaker 4: these AI tools and AI can be used. Machine learning
Speaker 4: algorithms can be used to understand this various genomic data
Speaker 4: and create new genomes that can actually accelerate breeding programs,
Speaker 4: resulting in crops that are more disease resistant, nutritious.
Speaker 5: And adaptable to changing emitic conditions.
Speaker 4: So that's one area that can be a very huge
Speaker 4: factor to revolutionize the farming sector.
Speaker 1: And Shwita, what's the number one area of AI technology
Speaker 1: you'd like to see.
Speaker 3: Actually focus on most sustainable and economical farming which as
Speaker 3: a result provides or becomes climate smart farming. So that
Speaker 3: is where adoption of smart farming practices right, which would
Speaker 3: really help grow India and the farmer and the community
Speaker 3: to which they belong.
Speaker 1: Excellent, excellent. I would like to thank my guests Rishi
Speaker 1: kish Ahmit Nayak, Swita Karuna and Niharika Haridas for joining
Speaker 1: me on this episode of Technically Speaking and Intel podcast.
Speaker 1: This was a very enjoyable discussion for me as I
Speaker 1: love talking with actual innovators, engineers and developers with fruits
Speaker 1: on the ground deploying technology and making a difference. What
Speaker 1: amakes me about the efforts was the use of the
Speaker 1: Intel Open Vino platform and it seemingly casual use of it.
Speaker 1: It was only a few years ago that running machine
Speaker 1: learning in AR models was a massive undertaking. The kishan
Speaker 1: No initiative that Ushikish and Erica have developed is a
Speaker 1: testament to the ability for new AI tools like Intel
Speaker 1: open Vino to speed up the development and deployment of
Speaker 1: industry changing technology. It was also important to understand the
Speaker 1: larger governmental impact on AI development. We heard from our
Speaker 1: guests the importance of ensuring that we strive to reduce
Speaker 1: any barriers to innovators from exploring, experimenting, and succeeding in
Speaker 1: their AI efforts democratization of technology. By continually striving to
Speaker 1: reduce the cost of AI deployments, two tools like Intel
Speaker 1: open Vino will be a boomed not only to the
Speaker 1: remote villages of India, but also in the tallest skyscrapers
Speaker 1: of New York. Please join us on Tuesday, October thirty
Speaker 1: first for the next episode of technically Speaking, an Intel podcast.
Speaker 1: Technically Speaking was produced by Ruby Studios from iHeartRadio in
Speaker 1: partnership with Intel, and hosted by me Graham Class. Our
Speaker 1: executive producer is Molly Sosher, our EP of Post Production
Speaker 1: is James Foster, and our supervising producer is Nikair Swinton.
Speaker 1: This episode was edited by Cierra Spreen and written and
Speaker 1: produced by Tyree Rush