When a lot of us think of farming, it reminds us of simpler times, and perhaps it feels like one of the remaining industries exempt from the influences of the modern tech world. But imagine a world where the success of your family's farm crop yield is access to AI tools. There's so much labor and effort that goes into maintaining a farm, especially when farmers have to anticipate unpredictable weather patterns and unprecedented seasons brought on by climate change. Plants, like humans, are living things, with millions of tiny organisms both attacking and assisting their life cycle. Some threats to crop life are smaller than the human eye can see, and when not addressed, the results can be disastrous to local economies. But what if AI could solve the problem. Giving eyes and access to where farmers cannot reach. AI protects crops and the economy from the threat of microbial pests, resulting in a more prosperous tomorrow. Hey there, I'm gram Class and this is technically speaking an Intel podcast. The show is dedicated to highlighting the way technology is revolutionizing the way we live, work, and move. In every episode, we'll connect with innovators in areas like artificial intelligence to better understand the human centered technology they've developed. There has always been a disconnect between nature and technology. However, today there's a lot of science and technology at the core of modern farming, and we're not talking about GMOs. One of the biggest issues in agriculture is environmental threats. Farmers struggle with protecting crops from diseases and pests without using tools that could adversely affect consumers. AI has been instrumental in helping farmers detect pests before infestations occur and result in huge crop loss. But before we get into exactly how it all works, I want to introduce our guests. In twenty seventeen, Rishi kish amitt Nayak's family farm in India so ninety percent crop loss due to pest in infestation. After partnering with a fellow student, Niharika Haridas, the two Megatronics, Robotics and automation engineering students found a way to use AI to develop a method that could detect crop pests through thermal imaging. This system, called kishan No, has been proven effective and very affordable to local farmers. Rishikish America thanks for being here.

When a lot of us think of farming, it reminds us of simpler times, and perhaps it feels like one of the remaining industries exempt from the influences of the modern tech world. But imagine a world where the success of your family's farm crop yield is access to AI tools. There's so much labor and effort that goes into maintaining a farm, especially when farmers have to anticipate unpredictable weather patterns and unprecedented seasons brought on by climate change. Plants, like humans, are living things, with millions of tiny organisms both attacking and assisting their life cycle. Some threats to crop life are smaller than the human eye can see, and when not addressed, the results can be disastrous to local economies. But what if AI could solve the problem. Giving eyes and access to where farmers cannot reach. AI protects crops and the economy from the threat of microbial pests, resulting in a more prosperous tomorrow. Hey there, I'm gram Class and this is technically speaking an Intel podcast. The show is dedicated to highlighting the way technology is revolutionizing the way we live, work, and move. In every episode, we'll connect with innovators in areas like artificial intelligence to better understand the human centered technology they've developed. There has always been a disconnect between nature and technology. However, today there's a lot of science and technology at the core of modern farming, and we're not talking about GMOs. One of the biggest issues in agriculture is environmental threats. Farmers struggle with protecting crops from diseases and pests without using tools that could adversely affect consumers. AI has been instrumental in helping farmers detect pests before infestations occur and result in huge crop loss. But before we get into exactly how it all works, I want to introduce our guests. In twenty seventeen, Rishi kish amitt Nayak's family farm in India so ninety percent crop loss due to pest in infestation. After partnering with a fellow student, Niharika Haridas, the two Megatronics, Robotics and automation engineering students found a way to use AI to develop a method that could detect crop pests through thermal imaging. This system, called kishan No, has been proven effective and very affordable to local farmers. Rishikish America thanks for being here.

Such a pleasure to be here, Graham, thank you for the invitation.

Such a pleasure to be here, Graham, thank you for the invitation.

We're also joined by Shwita Karuna, intel's Director of Government Partnerships and Initiatives for Japan and the Asia Pacific. Sharita has over twenty three years of experience creating trusted government relationships and fostering government programs that encouraged the implementation of modern science into the workforce PLUSH. She was instrumental in helping kishan No grow as a farming tactic across the region. Welcome, Shwrita.

We're also joined by Shwita Karuna, intel's Director of Government Partnerships and Initiatives for Japan and the Asia Pacific. Sharita has over twenty three years of experience creating trusted government relationships and fostering government programs that encouraged the implementation of modern science into the workforce PLUSH. She was instrumental in helping kishan No grow as a farming tactic across the region. Welcome, Shwrita.

Thank you, Graham, such a pleasure being here.

Thank you, Graham, such a pleasure being here.

So let's start at the beginning a very interesting story around rishikish Can you tell a little bit about the problem that your family and other farmers experienced back in twenty seventeen.

So let's start at the beginning a very interesting story around rishikish Can you tell a little bit about the problem that your family and other farmers experienced back in twenty seventeen.

In India particularly, it's an agricultural country, so more than seventy percent of the people do agriculture as their own major occupation. In twenty seventeen, my father's grandfather was completely invested into agricultural farming, and during that time, in Orisa particularly, there was a plant best attack that couldn't be identified for a longer period of time, and that resulted in a lot of crop losses and hectares of land was just lost because of an unidentified pist. Personally, we saw a lot of farmer suicides in our own village, and that was the major reason when I thought, Okay, I do have a background of engineering, I do have a background of robotics, so why not to create something for our own farmers. And being part of that family where we do farming in our parental site, I was just touched with that fact that I need to do something for the farmers.

In India particularly, it's an agricultural country, so more than seventy percent of the people do agriculture as their own major occupation. In twenty seventeen, my father's grandfather was completely invested into agricultural farming, and during that time, in Orisa particularly, there was a plant best attack that couldn't be identified for a longer period of time, and that resulted in a lot of crop losses and hectares of land was just lost because of an unidentified pist. Personally, we saw a lot of farmer suicides in our own village, and that was the major reason when I thought, Okay, I do have a background of engineering, I do have a background of robotics, so why not to create something for our own farmers. And being part of that family where we do farming in our parental site, I was just touched with that fact that I need to do something for the farmers.

In Rishikishi's village alone, there were four farmers who took their lives as a result of the devastated crop, and his family saw a ninety percent crop loss that year. The infestation was so devastating to their livelihood his family considered leaving farming all together. And to make matters worse, the problem was difficult to identify and trace. Before we get into the actual details of how you solved it in Arika, how did you get involved in the project.

In Rishikishi's village alone, there were four farmers who took their lives as a result of the devastated crop, and his family saw a ninety percent crop loss that year. The infestation was so devastating to their livelihood his family considered leaving farming all together. And to make matters worse, the problem was difficult to identify and trace. Before we get into the actual details of how you solved it in Arika, how did you get involved in the project.

I decided to pursue mecatronics and automation at Viatchene out of a sheer passion for robotics as a twelfth grader. So I came across the work that many companies like Boston Dynamics were doing at that point and exactly right the spot pro vote of course, and I was just enthralled with potential that it helped, Like it was like, oh my, what this could change humanity?

I decided to pursue mecatronics and automation at Viatchene out of a sheer passion for robotics as a twelfth grader. So I came across the work that many companies like Boston Dynamics were doing at that point and exactly right the spot pro vote of course, and I was just enthralled with potential that it helped, Like it was like, oh my, what this could change humanity?

And I was like, I need.

And I was like, I need.

To do something in this space. I wanted to help people with this new technology. And that's how I went to Aitchen and that's where I'm Metri Shikish and we started talking and we were talking about this project and I was like, you know, that's that's amazing that we'll let me contribute to it as well, and that's how we started collaborating on the project and then we participated in the Inaugril Intelliet Global Impact Festival and the rest is history. We had a wonderful time and you know, the support that we have gotten from Intel for it as well has been phenomenal and that's the reason that Kishano is at the place where it is right now.

To do something in this space. I wanted to help people with this new technology. And that's how I went to Aitchen and that's where I'm Metri Shikish and we started talking and we were talking about this project and I was like, you know, that's that's amazing that we'll let me contribute to it as well, and that's how we started collaborating on the project and then we participated in the Inaugril Intelliet Global Impact Festival and the rest is history. We had a wonderful time and you know, the support that we have gotten from Intel for it as well has been phenomenal and that's the reason that Kishano is at the place where it is right now.

Excellent. So now as the I guess the sixty four thousand dollars question is how does the kishan No work.

Excellent. So now as the I guess the sixty four thousand dollars question is how does the kishan No work.

Kishano basically taps into saturate based thermal imagery. These images can detect temperature variations and crops which often indicate Streuss disease or pestal activity. For instance, areas affected by certain pest or microbol infestations may exhibit different thermal patterns compared to healthy areas. We collect images from Sentinel two and lands At eight satellites. Those satellite images are then sys to get index mapping out likes, for example, vegetative indexes and moisture indexes through a software called QGIS, so it basically gives us the values for those vegetative indexes and moisture indexes, and these gathered thermal imageries processed using AA algorithms, where we've processed the images first into the open Veno platform and we get a d blood image for better accuracy of training of the models. Then these algorithms are trained to recognize patterns or animalies that correspond to microbilan pest outbreaks. Over time, has more data is collected and analyzed, the AA model becomes more accurate and efficient in its prediction and leveraging the power of machine learning. Once a potential threat is identified in the system, the systems can send alerts or recommendations to the farmers in the local administrative levels, where we also design the physical device apart from the AA algorithm to get a confirmatory test that there is a pest or plant disease outbreak. This actually includes information about the type of threat, it's severe, and recommendation algorithms.

Kishano basically taps into saturate based thermal imagery. These images can detect temperature variations and crops which often indicate Streuss disease or pestal activity. For instance, areas affected by certain pest or microbol infestations may exhibit different thermal patterns compared to healthy areas. We collect images from Sentinel two and lands At eight satellites. Those satellite images are then sys to get index mapping out likes, for example, vegetative indexes and moisture indexes through a software called QGIS, so it basically gives us the values for those vegetative indexes and moisture indexes, and these gathered thermal imageries processed using AA algorithms, where we've processed the images first into the open Veno platform and we get a d blood image for better accuracy of training of the models. Then these algorithms are trained to recognize patterns or animalies that correspond to microbilan pest outbreaks. Over time, has more data is collected and analyzed, the AA model becomes more accurate and efficient in its prediction and leveraging the power of machine learning. Once a potential threat is identified in the system, the systems can send alerts or recommendations to the farmers in the local administrative levels, where we also design the physical device apart from the AA algorithm to get a confirmatory test that there is a pest or plant disease outbreak. This actually includes information about the type of threat, it's severe, and recommendation algorithms.

This proactive approach.

This proactive approach.

Helps farmers to take actions before the problem becomes widespread and saving both time and resources.

Helps farmers to take actions before the problem becomes widespread and saving both time and resources.

I'd like to talk about Intel open Veno a little bit so quickly, just to inform our audience. INTE open Vino is a cross platform toolkit developed by Intel that deploys deep learning models on visual data sets, helping computers better recognize and process images to inform decision making. But I'm curious as someone who's just as interested in what didn't work as opposed to what ultimately does. Why did you decide to use Intel open Veno. Were there are other methods you tried first?

I'd like to talk about Intel open Veno a little bit so quickly, just to inform our audience. INTE open Vino is a cross platform toolkit developed by Intel that deploys deep learning models on visual data sets, helping computers better recognize and process images to inform decision making. But I'm curious as someone who's just as interested in what didn't work as opposed to what ultimately does. Why did you decide to use Intel open Veno. Were there are other methods you tried first?

So we did try a lot of techniques, and we found that open Veno worked perfectly with our project, especially with the goal that we were trying to achieve. So we saw that the hardware requirements as well as the software requirements did completely match. Also, we had mentorship from Intel and we were able to properly and in a better way adapt to those systems to our project, and that's the reason which it was open.

So we did try a lot of techniques, and we found that open Veno worked perfectly with our project, especially with the goal that we were trying to achieve. So we saw that the hardware requirements as well as the software requirements did completely match. Also, we had mentorship from Intel and we were able to properly and in a better way adapt to those systems to our project, and that's the reason which it was open.

We know, we actually tried to degler images through some deep learning algorithms, but those algorithms was actually not satisfying the accuracy that we actually wanted, so open Veno just suited out the case perfectly.

We know, we actually tried to degler images through some deep learning algorithms, but those algorithms was actually not satisfying the accuracy that we actually wanted, so open Veno just suited out the case perfectly.

One thing I'm interested in is the pests that were being detected. Am I right in saying that it had a unique therm signature?

One thing I'm interested in is the pests that were being detected. Am I right in saying that it had a unique therm signature?

Yeah?

Yeah?

And how did you discover that?

And how did you discover that?

In twenty seventeen, Once we identified the problem, we actually tried to create a physical device through a thermal camera set up and microprocesses. We were rotating that device among the periphery of the crop fields to understand what exactly the thermal traces are in the leaf of the crop plants. And once we understood what are the thermal signatures for different crop plants, we understood there is a concept that whenever there is a pathogen or a plant disease, there is a certain increase in the leaf temperature. And if we identify that leaf temperature increases in the particular or in a particular duration of time, we can actually significantly say that there is a best attack or a plant disease in the crop area. Once we had the theory, we tried to incorporate that similar formula in the vegetative index of the satellite setup. So in twenty nineteen we had the physical setup, we tried the same literature to understand it to the satellites.

In twenty seventeen, Once we identified the problem, we actually tried to create a physical device through a thermal camera set up and microprocesses. We were rotating that device among the periphery of the crop fields to understand what exactly the thermal traces are in the leaf of the crop plants. And once we understood what are the thermal signatures for different crop plants, we understood there is a concept that whenever there is a pathogen or a plant disease, there is a certain increase in the leaf temperature. And if we identify that leaf temperature increases in the particular or in a particular duration of time, we can actually significantly say that there is a best attack or a plant disease in the crop area. Once we had the theory, we tried to incorporate that similar formula in the vegetative index of the satellite setup. So in twenty nineteen we had the physical setup, we tried the same literature to understand it to the satellites.

Hearing Rishikish and Aharika elaborate on how they design their imaging tool reminded me of my own experience attempting to develop systems to work remotely in the jungles of Africa. It's not an easy feat though, as there's no real infrastructure for these sorts of products, especially when they are limited by internet access and availability in the area. Hearing how much progress these two had made with their program, maybe wonder about the challenges that went into making this tool available in the rural farmlands of India.

Hearing Rishikish and Aharika elaborate on how they design their imaging tool reminded me of my own experience attempting to develop systems to work remotely in the jungles of Africa. It's not an easy feat though, as there's no real infrastructure for these sorts of products, especially when they are limited by internet access and availability in the area. Hearing how much progress these two had made with their program, maybe wonder about the challenges that went into making this tool available in the rural farmlands of India.

There has always been a digital divide in India, as we can see, but now it's been narrowing and that's a very good news for all of us, and that infrastructure is also becoming better. There's also research that India has one of the cheapest internet out there in the world, so I mean, it's being adapted and we are glad that it is. But when we were working on it, we did face a lot of infrastructure issues regarding internet services as well and internet connectivity exactly, and sort of having that satellite imagery. Gaining access to the satellite imagery was very difficult for us because that area wasn't mapped. Remote areas aren't usually mapped with that much precision as that of let's say, an urban area, so we did have some issues with that, but then we did try our best to solve those and gain satellite images from the areas that we neated.

There has always been a digital divide in India, as we can see, but now it's been narrowing and that's a very good news for all of us, and that infrastructure is also becoming better. There's also research that India has one of the cheapest internet out there in the world, so I mean, it's being adapted and we are glad that it is. But when we were working on it, we did face a lot of infrastructure issues regarding internet services as well and internet connectivity exactly, and sort of having that satellite imagery. Gaining access to the satellite imagery was very difficult for us because that area wasn't mapped. Remote areas aren't usually mapped with that much precision as that of let's say, an urban area, so we did have some issues with that, but then we did try our best to solve those and gain satellite images from the areas that we neated.

Farmers in the villages particularly, they were quite a bit skeptical to try this out, and the farms because in India particularly didn't back that time, we didn't have that much of agritechnology tools or products, and going as a youngster something around in class ninth or tenth and trying out as some different new projects or new census in the field, they were quite a bit skeptical. So managing that side of that, Okay, we are doing something good, we are doing something better for your own crops, we are doing something for the best of the society. Convincing them was one of the very huge challenge over there in India.

Farmers in the villages particularly, they were quite a bit skeptical to try this out, and the farms because in India particularly didn't back that time, we didn't have that much of agritechnology tools or products, and going as a youngster something around in class ninth or tenth and trying out as some different new projects or new census in the field, they were quite a bit skeptical. So managing that side of that, Okay, we are doing something good, we are doing something better for your own crops, we are doing something for the best of the society. Convincing them was one of the very huge challenge over there in India.

What kind of data or training processes were involved in training the model to recognize microbiopests in the crops.

What kind of data or training processes were involved in training the model to recognize microbiopests in the crops.

Initially it was only deep learning algorithms.

Initially it was only deep learning algorithms.

Further on, when we had a lot of thermal praise data and we had did the d blood images, we were just focused on the CNN models to train the data. And it hadn't given a good accuracy of for around ninety points something percentage, so it was a pretty good accurate to start with for a particular set of crops.

Further on, when we had a lot of thermal praise data and we had did the d blood images, we were just focused on the CNN models to train the data. And it hadn't given a good accuracy of for around ninety points something percentage, so it was a pretty good accurate to start with for a particular set of crops.

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

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

Conventional neural network.

Conventional neural network.

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

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

Yeah, yes, a machine learning okay, okay.

Yeah, yes, a machine learning okay, okay.

And you just mentioned about the accuracy that you achieved. Would you say that's typical for the Intel Open Veno platform to get that sort of result.

And you just mentioned about the accuracy that you achieved. Would you say that's typical for the Intel Open Veno platform to get that sort of result.

The accuracy is for the total accuracy of the model for a particular set of crops, for example, tomatoes and wheat. For those two crops we had an accuracy fround ninety point two eight percentage, and for other crops it's still in the process of getting more accurate and all. So for these two crops, overly, it was the accuracy that we measured out and.

The accuracy is for the total accuracy of the model for a particular set of crops, for example, tomatoes and wheat. For those two crops we had an accuracy fround ninety point two eight percentage, and for other crops it's still in the process of getting more accurate and all. So for these two crops, overly, it was the accuracy that we measured out and.

In terms of the Intel Open Veno technology, can you think of anything any other farming use cases beyond pest management and crop protection.

In terms of the Intel Open Veno technology, can you think of anything any other farming use cases beyond pest management and crop protection.

Currently, we were trying to work on crop genome analysis where we were actually trying to understand because of the climate change to the new variants of crops are needed to adapt to the new climatic conditions. So we were trying to understand how exactly we can use machine learning algorithms to create new genomes in the crops the microbiology side of it.

Currently, we were trying to work on crop genome analysis where we were actually trying to understand because of the climate change to the new variants of crops are needed to adapt to the new climatic conditions. So we were trying to understand how exactly we can use machine learning algorithms to create new genomes in the crops the microbiology side of it.

So yeah, that's one area that I was completely focused on in this past recent days.

So yeah, that's one area that I was completely focused on in this past recent days.

I would like to add on to that, and as Education mentioned, convolutional neural network model that we used, it was at that point not something that was used by the AI community, but then we now see a lot of use cases for that and that's something that we are very glad about. And also some of the use cases that I have at least found as an AI enthusiast that models like these could have is in real time data, especially as the climatic change has become a huge issue. It is something that can help a lot of farmers with when there is excessive rains or when there is no rain at all, to predict these through AIML technologies. And I believe that the limit is boundless when it comes to AI technologies. Right we are seeing a start of a new era of AI, and I am very glad to see how I was being used by lots of companies, and we also hope to go contribute to that, and I hope for a very bright future.

I would like to add on to that, and as Education mentioned, convolutional neural network model that we used, it was at that point not something that was used by the AI community, but then we now see a lot of use cases for that and that's something that we are very glad about. And also some of the use cases that I have at least found as an AI enthusiast that models like these could have is in real time data, especially as the climatic change has become a huge issue. It is something that can help a lot of farmers with when there is excessive rains or when there is no rain at all, to predict these through AIML technologies. And I believe that the limit is boundless when it comes to AI technologies. Right we are seeing a start of a new era of AI, and I am very glad to see how I was being used by lots of companies, and we also hope to go contribute to that, and I hope for a very bright future.

AI has been the focus of a lot of discourse over the last couple of decades. While many of us experience it as virtual assistance in our phones and computers, AI has been giving us listening, watching, and reading recommendations for years and we continue to see it evolve and even create content like images and written stories. But that's all just the beginning. AI has so much potential to positively impact the way we work and live. It can be used to detect new variants and threats in agriculture brought on by climate change conditions. The Intel Open Vino technology played an essential role in this, providing higher accuracy for detection. I'd just like to switch a little bit to the agribusiness side of things. And maybe I can get Shuita to comment on this in terms of the Intel Open Vino and its app cation here for pest detection. Do you see it complementing other pest control methods in agriculture and does it have the potential to replace pesticides and insecticides and farming replace.

AI has been the focus of a lot of discourse over the last couple of decades. While many of us experience it as virtual assistance in our phones and computers, AI has been giving us listening, watching, and reading recommendations for years and we continue to see it evolve and even create content like images and written stories. But that's all just the beginning. AI has so much potential to positively impact the way we work and live. It can be used to detect new variants and threats in agriculture brought on by climate change conditions. The Intel Open Vino technology played an essential role in this, providing higher accuracy for detection. I'd just like to switch a little bit to the agribusiness side of things. And maybe I can get Shuita to comment on this in terms of the Intel Open Vino and its app cation here for pest detection. Do you see it complementing other pest control methods in agriculture and does it have the potential to replace pesticides and insecticides and farming replace.

Is a little on the harsher terms.

Is a little on the harsher terms.

What I would actually look at it is AI and agricultures really helping farmers make data driven decisions, optimize crop yields conserved resources like water and energy. The challenge here is not just the solution part of it is also kind of encouraging next generation technologists student innovators to come together, look at the local problems like what Neharikan risikation have done, and then create a solution using all the skills they've learned as part of their formal education as well as as part of being a part of Intel programs the Interdigital Rediness Program portfolio, come together and democratize AI skills in a way which gets a common person a farmer, to understand trust and emergingology like artificial intelligence and hopefully become comfortable in applying it to solve the daily problems they would be facing as part of their community.

What I would actually look at it is AI and agricultures really helping farmers make data driven decisions, optimize crop yields conserved resources like water and energy. The challenge here is not just the solution part of it is also kind of encouraging next generation technologists student innovators to come together, look at the local problems like what Neharikan risikation have done, and then create a solution using all the skills they've learned as part of their formal education as well as as part of being a part of Intel programs the Interdigital Rediness Program portfolio, come together and democratize AI skills in a way which gets a common person a farmer, to understand trust and emergingology like artificial intelligence and hopefully become comfortable in applying it to solve the daily problems they would be facing as part of their community.

I love that term democratization of technology, and I think that's ultimately what technology does is get it more accessible and cheaper to get it to the far regions of the world. I'd just like to expand a little bit more, maybe if you could explain some of the programs that are available through inter Open VENO to help farmers or businesses with limited resources to get access to this sort of technology and expertise.

I love that term democratization of technology, and I think that's ultimately what technology does is get it more accessible and cheaper to get it to the far regions of the world. I'd just like to expand a little bit more, maybe if you could explain some of the programs that are available through inter Open VENO to help farmers or businesses with limited resources to get access to this sort of technology and expertise.

I'll just take a step back here, right because we keep talking about increasing digitization, which today a lot of governments and countries are going towards. But what it really means is when we focus on increased digitization, we also need to invest more in building the digital readiness of people, specifically in terms of emerging in critical technologies like AI or what you spoke about, like the usage of open Wino. How do we get person to understand how they can utilize the technology like open we know to be able to solve their local problem and create indigender solutions. So all this kind of comes together through a whole program portfolio which we have which is called the Intel Digital Readiness Programs, which is driven in collaboration with government, academia, civil society, and the industry and focuses around building shared value, shared responsibility so that we can really demystify democratize these superpowers which we keep talking about, like artificial intelligence for a very broader and a diverse audience for young budding technologists like Neiharika Ushikish but also for those who are going to be consuming the technology at the other end of the spectrum. The programs are a lot, they're many. They range from you know, programs like AI for Citizens, which talks about getting a citizen to understand how to navigate his or her life in an AI driven world. AI for Youth, which really allows us to empower youth with not just the technical skills associated with AI, but also the social skills in a very inclusive manner. And then we have AI for Future Workforce, which is for vocational community college students, engineering students, which really helps them to understand how to be prepare themselves for becoming a part of the future workforce. So a huge spectrum, lots of programs, but the one which is very special to all three of us in this case, and I'm sure Education Aherka would agree with that is our EI Global Impact Festival, because this is where we work with all these student innovators. We get them together and we get them to celebrate their AI innovations with a huge passage which does not just allow them to showcase what they've built, but also helps them hone their skills by getting mentored by Intel technologists because at the end of the day, these young students are the next generation technologists, so we want to make sure we work for them to support and build an AI ready generation.

I'll just take a step back here, right because we keep talking about increasing digitization, which today a lot of governments and countries are going towards. But what it really means is when we focus on increased digitization, we also need to invest more in building the digital readiness of people, specifically in terms of emerging in critical technologies like AI or what you spoke about, like the usage of open Wino. How do we get person to understand how they can utilize the technology like open we know to be able to solve their local problem and create indigender solutions. So all this kind of comes together through a whole program portfolio which we have which is called the Intel Digital Readiness Programs, which is driven in collaboration with government, academia, civil society, and the industry and focuses around building shared value, shared responsibility so that we can really demystify democratize these superpowers which we keep talking about, like artificial intelligence for a very broader and a diverse audience for young budding technologists like Neiharika Ushikish but also for those who are going to be consuming the technology at the other end of the spectrum. The programs are a lot, they're many. They range from you know, programs like AI for Citizens, which talks about getting a citizen to understand how to navigate his or her life in an AI driven world. AI for Youth, which really allows us to empower youth with not just the technical skills associated with AI, but also the social skills in a very inclusive manner. And then we have AI for Future Workforce, which is for vocational community college students, engineering students, which really helps them to understand how to be prepare themselves for becoming a part of the future workforce. So a huge spectrum, lots of programs, but the one which is very special to all three of us in this case, and I'm sure Education Aherka would agree with that is our EI Global Impact Festival, because this is where we work with all these student innovators. We get them together and we get them to celebrate their AI innovations with a huge passage which does not just allow them to showcase what they've built, but also helps them hone their skills by getting mentored by Intel technologists because at the end of the day, these young students are the next generation technologists, so we want to make sure we work for them to support and build an AI ready generation.

Platforms like these have been really instrumental and I have seen the impact on ground that they make in supporting technologists, young technologists like us, and we have always been very grateful for the opportunities and mentorship as well that Intel has provided. And that's something that we wish that every budding technologist in India and all over the globe can at least experience, because mentorship and guidance is an important pillar of one's journey and having someone who can teach you more about AI, how to use AI, and how to benefit from AI, especially with the immense potential it has that is life changing.

Platforms like these have been really instrumental and I have seen the impact on ground that they make in supporting technologists, young technologists like us, and we have always been very grateful for the opportunities and mentorship as well that Intel has provided. And that's something that we wish that every budding technologist in India and all over the globe can at least experience, because mentorship and guidance is an important pillar of one's journey and having someone who can teach you more about AI, how to use AI, and how to benefit from AI, especially with the immense potential it has that is life changing.

You're listening to technically speaking, an Intel podcast will be right back. Welcome back to technically speaking an Intel podcast shweeta last episode of this podcast, we talked with Reachhuvu, one of your colleagues, about the ethics and responsibility of AIM, wondering if we could get your thoughts on how you're working with governments and industry leaders around AI and trying to help them navigate some of the ethics and responsibilities around AI development.

You're listening to technically speaking, an Intel podcast will be right back. Welcome back to technically speaking an Intel podcast shweeta last episode of this podcast, we talked with Reachhuvu, one of your colleagues, about the ethics and responsibility of AIM, wondering if we could get your thoughts on how you're working with governments and industry leaders around AI and trying to help them navigate some of the ethics and responsibilities around AI development.

That's a very interesting question for us, right because when we speak about digital reddiness or how do we build digital readiness, we look at three pillars. Largely, one is, of course learning the skills of emerging technologies like AI, but more importantly, getting to understand and trust those skills, So getting to understand not just what the advantages are, but also what the pitfalls are. Getting to understand which situation should we apply the emerging technology in and which ones we should abstain from. So our programs, in fact, inculcate a lot of discussions around these there is, which range from the ethics piece of it, which range from how how do we make it more inclusive, how do we make it more diverse? And so much so that if you kind of package it all together, it comes under the larger umbrella of responsible AI. So how do we really encourage not just youth, but every citizen, which includes the governments who we collaborate with and partner with to understand what is the responsible use of these superpowers like AI to gain broader socioeconomic benefits for everybody.

That's a very interesting question for us, right because when we speak about digital reddiness or how do we build digital readiness, we look at three pillars. Largely, one is, of course learning the skills of emerging technologies like AI, but more importantly, getting to understand and trust those skills, So getting to understand not just what the advantages are, but also what the pitfalls are. Getting to understand which situation should we apply the emerging technology in and which ones we should abstain from. So our programs, in fact, inculcate a lot of discussions around these there is, which range from the ethics piece of it, which range from how how do we make it more inclusive, how do we make it more diverse? And so much so that if you kind of package it all together, it comes under the larger umbrella of responsible AI. So how do we really encourage not just youth, but every citizen, which includes the governments who we collaborate with and partner with to understand what is the responsible use of these superpowers like AI to gain broader socioeconomic benefits for everybody.

As a youth igffellow. That is exactly what I focus on Internet governance right and how AI governance works and how AI can be regulated. But then what about AI innovation? It shouldn't be regulated or stifled due to laws coming into place that can have that effect where people continuate and they can't contribute to new technologies, so that there's a delicate balance between them, and that is what I also do look into. And the whole area of how becoming emerging technology is like even robotics which has a huge artificient intelligence ethics background out there, So how do we harness this without harming humanity? And that is something that I believe all stakeholders, including the youth of our country or the globe, should be focusing on because there also tends to be the whole bias of youth not being given a voice when it comes to these emerging technologies. But I believe if they do understand what it is about and what potential risks it has and what potential benefits it has, that gives them the knowledge to use it responsibly and ethically.

As a youth igffellow. That is exactly what I focus on Internet governance right and how AI governance works and how AI can be regulated. But then what about AI innovation? It shouldn't be regulated or stifled due to laws coming into place that can have that effect where people continuate and they can't contribute to new technologies, so that there's a delicate balance between them, and that is what I also do look into. And the whole area of how becoming emerging technology is like even robotics which has a huge artificient intelligence ethics background out there, So how do we harness this without harming humanity? And that is something that I believe all stakeholders, including the youth of our country or the globe, should be focusing on because there also tends to be the whole bias of youth not being given a voice when it comes to these emerging technologies. But I believe if they do understand what it is about and what potential risks it has and what potential benefits it has, that gives them the knowledge to use it responsibly and ethically.

Using AI can be as complicated as Niharika has pointed out, but the tool she and Wishikish have been able to create from this place of innovation and AI have changed the world for the better and they have the results to prove it. In terms of the Kisheno technology that you have developed, do you have any stats on the crop that has been saved or the reduction in crop loss?

Using AI can be as complicated as Niharika has pointed out, but the tool she and Wishikish have been able to create from this place of innovation and AI have changed the world for the better and they have the results to prove it. In terms of the Kisheno technology that you have developed, do you have any stats on the crop that has been saved or the reduction in crop loss?

In twenty to twenty, we actually piloted this around in eight districts in Orissa and more than around seventy two villages. We actually serve it upon and piloted upon and for one season we tried it particularly on wheats and tomatoes. Once we had data that we could actually predict that there is a pest attack or plant this is coming up, we use that data to try to save those fifty villages. We used pesticides and fertilizers just before whenever the pest and pest attack could have happened, So it actually saved around those fifty villages.

In twenty to twenty, we actually piloted this around in eight districts in Orissa and more than around seventy two villages. We actually serve it upon and piloted upon and for one season we tried it particularly on wheats and tomatoes. Once we had data that we could actually predict that there is a pest attack or plant this is coming up, we use that data to try to save those fifty villages. We used pesticides and fertilizers just before whenever the pest and pest attack could have happened, So it actually saved around those fifty villages.

I'm really interested in how the technology actually is deployed and distributed to as many villages as possible. To me, the innovation is part of that as well. How do you deploy it, how do you scale it? And you said you went to seventy two villages, how did you get to all of them and provide this service and this knowledge.

I'm really interested in how the technology actually is deployed and distributed to as many villages as possible. To me, the innovation is part of that as well. How do you deploy it, how do you scale it? And you said you went to seventy two villages, how did you get to all of them and provide this service and this knowledge.

In the local districts.

In the local districts.

We contacted the administrations and with the recognitions we had with until it was really easy to contact the administrations. So once we had contacted the administration the local villagers, they were actually understood, Okay, there is someone who is coming to do something in their villages and it won't harm them, So they were at least a relaxed that nothing is going to be happening.

We contacted the administrations and with the recognitions we had with until it was really easy to contact the administrations. So once we had contacted the administration the local villagers, they were actually understood, Okay, there is someone who is coming to do something in their villages and it won't harm them, So they were at least a relaxed that nothing is going to be happening.

And also they actually co operated out.

And also they actually co operated out.

So we had to draw the plots, We had to map it on the satellite software that we had and it would actually give us a satellite based crop image. And for each crop images, we just needed to market around the perimeters of that particular individual farmer and the work is done. We just needed to understand how what area that particular farmer has, what is the crop type? When did so what is the raining patterns and what is the soil type. With these certain parameters understood, the farmer had to do nothing. We were sitting on a room played server and we were training these images and it was again the process kept on going. We had the results each week, we just to share them. Okay, this is the condition, this is what your crop health is, and your crop is safe and if not, we will at least give them some predictions.

So we had to draw the plots, We had to map it on the satellite software that we had and it would actually give us a satellite based crop image. And for each crop images, we just needed to market around the perimeters of that particular individual farmer and the work is done. We just needed to understand how what area that particular farmer has, what is the crop type? When did so what is the raining patterns and what is the soil type. With these certain parameters understood, the farmer had to do nothing. We were sitting on a room played server and we were training these images and it was again the process kept on going. We had the results each week, we just to share them. Okay, this is the condition, this is what your crop health is, and your crop is safe and if not, we will at least give them some predictions.

One of the other plus points or advantages of our innovation was how cost effective it was. So now this is a huge issue when it comes to India that technologies are out there, but they can be very expensive and that's not reachable to a conventional Indian farmer. They need solutions that are cost effective because of budget constraints and that's what we provided. So that also helped in the reach for them to know that there is a device out there which is very cost effective, which won't cost thousands and lacks of rupees for them, just a dollar which is a meal a day, right, So that amount of money to protect their crops that was huge for them. So that also helped us make them acquainted with the technology and the benefits of it.

One of the other plus points or advantages of our innovation was how cost effective it was. So now this is a huge issue when it comes to India that technologies are out there, but they can be very expensive and that's not reachable to a conventional Indian farmer. They need solutions that are cost effective because of budget constraints and that's what we provided. So that also helped in the reach for them to know that there is a device out there which is very cost effective, which won't cost thousands and lacks of rupees for them, just a dollar which is a meal a day, right, So that amount of money to protect their crops that was huge for them. So that also helped us make them acquainted with the technology and the benefits of it.

At the cost of one dollar to use kishan No. The America and Rishikish have made these resources accessible to those who need it most, but being cost effective is only half the battle. They had to work hand in hand with the farmers to teach them how the technology worked. But this technology had a more profound impact in identifying the source of the crop loss. It also led to revelations about the dangerous fertilizers and pesticides they were using. How have you found the process of having the farmers actually take some action based on the results that you give them.

At the cost of one dollar to use kishan No. The America and Rishikish have made these resources accessible to those who need it most, but being cost effective is only half the battle. They had to work hand in hand with the farmers to teach them how the technology worked. But this technology had a more profound impact in identifying the source of the crop loss. It also led to revelations about the dangerous fertilizers and pesticides they were using. How have you found the process of having the farmers actually take some action based on the results that you give them.

Initially, like they didn't understand what exactly we were trying to do.

Initially, like they didn't understand what exactly we were trying to do.

They just were, Okay, there's nothing harm in it, but there's nothing good in it. So that's how it was. So we actually startle if some visual based learning. Each weekends we try to un make them understand what exactly we were doing in just some graphics, cartoon type animations, just to understand what exactly we are trying to do, so that they're also getting literate about Okay, this is a technology that they are paying for the cost of for one acre of land in crop area was just costing them around seventy troopees. That's around one dollar near to one dollar, and it was a monthly based service, so they were giving for each acre seventy troopees.

They just were, Okay, there's nothing harm in it, but there's nothing good in it. So that's how it was. So we actually startle if some visual based learning. Each weekends we try to un make them understand what exactly we were doing in just some graphics, cartoon type animations, just to understand what exactly we are trying to do, so that they're also getting literate about Okay, this is a technology that they are paying for the cost of for one acre of land in crop area was just costing them around seventy troopees. That's around one dollar near to one dollar, and it was a monthly based service, so they were giving for each acre seventy troopees.

Each farmer would have been paying us.

Each farmer would have been paying us.

The cost was just to handle out the server that we were trying to maintain, and these informations that we are trying to literate them with.

The cost was just to handle out the server that we were trying to maintain, and these informations that we are trying to literate them with.

They understood at least some parts of the technology.

They understood at least some parts of the technology.

They understood how exactly the pest and plant disease affect the crop, and what kind of pesticides, what kind of fertilizers are actually affecting both the crops and both.

They understood how exactly the pest and plant disease affect the crop, and what kind of pesticides, what kind of fertilizers are actually affecting both the crops and both.

As humans when we consume that product.

As humans when we consume that product.

So they also started to understand and they started to stop using those pest sets and fertilizers for a particular duration of time because in India, in particular crops, they farmers just used to spray pesticides and fertilizers even if they have not been attacked by any pests. This is used to spray it before any pest infestation, just to understand that it should be protected. But actually it's was hampings as human beings because even if there is no pest attack, we were actually consuming that pesticides and fertilizers.

So they also started to understand and they started to stop using those pest sets and fertilizers for a particular duration of time because in India, in particular crops, they farmers just used to spray pesticides and fertilizers even if they have not been attacked by any pests. This is used to spray it before any pest infestation, just to understand that it should be protected. But actually it's was hampings as human beings because even if there is no pest attack, we were actually consuming that pesticides and fertilizers.

It matters on how we present the data to farmers, and this also ties into the whole digital literacy programs that we wanted to run. And as the Religash mentioned, we were trying to present the data to them in a way that they could understand as an individual. Anne I impact enthusiast. I believe that having that AI accessible in regional languages is very important and that is something that we try to incorporate as well. And even as Retigish mentioned, like pesticides, when used unnecessarily, they do drive the costs also, so the farmers, if you don't talk money, they do understand that, right, So you can see, you know, like all the pesticides that you have been using, you don't have to use those much. You can just use on the base of the data that we're giving you, and that too in a very accessible form.

It matters on how we present the data to farmers, and this also ties into the whole digital literacy programs that we wanted to run. And as the Religash mentioned, we were trying to present the data to them in a way that they could understand as an individual. Anne I impact enthusiast. I believe that having that AI accessible in regional languages is very important and that is something that we try to incorporate as well. And even as Retigish mentioned, like pesticides, when used unnecessarily, they do drive the costs also, so the farmers, if you don't talk money, they do understand that, right, So you can see, you know, like all the pesticides that you have been using, you don't have to use those much. You can just use on the base of the data that we're giving you, and that too in a very accessible form.

And Sweeta we talked a little bit about previously around regulations and how Intel can assist the adoption of these sorts of technologies. I mean, we heard from Risha, Kisha and Erica that they had to sort of contact the local administration bureaus to get permission. Maybe you could talk a little bit about the way Intel can actually help that process to get the technology down locally.

And Sweeta we talked a little bit about previously around regulations and how Intel can assist the adoption of these sorts of technologies. I mean, we heard from Risha, Kisha and Erica that they had to sort of contact the local administration bureaus to get permission. Maybe you could talk a little bit about the way Intel can actually help that process to get the technology down locally.

So all countries governments, both at the central government level and at the local government level today are developing strategies on how do you really take emerging technology to the last mile or to the grassroot level. Nindia specifically has a very rapus Daia strategy on how do you really develop a sustainable, inclusive, positive impact on citizens by improving public awareness, by helping them access public services which would allow technology to become a part of their regular routine.

So all countries governments, both at the central government level and at the local government level today are developing strategies on how do you really take emerging technology to the last mile or to the grassroot level. Nindia specifically has a very rapus Daia strategy on how do you really develop a sustainable, inclusive, positive impact on citizens by improving public awareness, by helping them access public services which would allow technology to become a part of their regular routine.

The way they work, the way they.

The way they work, the way they.

Function, such as what Niharika and Nishikisha developed can be driven in a larger way, can be scaled with the help of the local state government and we're already working with multiple state governments to ensure that they create platforms where these can be taken further. The idea or the objective of our collaboration with the government is how do we really bring AI everywhere in an extremely inclusive and responsible manner. But a large obstacle which I see is the availability of infrastructure right because for the adoption of technology, we have to make sure that precision farming requires investments in digital infrastructure at scale and now there are multiple schemes and initiators which coment to in India is doing. They're trying their best to improve the living standards of Indian farmers, trying to support them in smart farming practices. But apart from this, there are tax benefits, there are financial grants, etc. Which can help accelerate the cost of technology adoption.

Function, such as what Niharika and Nishikisha developed can be driven in a larger way, can be scaled with the help of the local state government and we're already working with multiple state governments to ensure that they create platforms where these can be taken further. The idea or the objective of our collaboration with the government is how do we really bring AI everywhere in an extremely inclusive and responsible manner. But a large obstacle which I see is the availability of infrastructure right because for the adoption of technology, we have to make sure that precision farming requires investments in digital infrastructure at scale and now there are multiple schemes and initiators which coment to in India is doing. They're trying their best to improve the living standards of Indian farmers, trying to support them in smart farming practices. But apart from this, there are tax benefits, there are financial grants, etc. Which can help accelerate the cost of technology adoption.

In terms of AI, and it's becoming obviously more popular across multiple industries. What's the number one thing you'd like to try and solve using AI technology in the in farming. I'll start with the Ahurica.

In terms of AI, and it's becoming obviously more popular across multiple industries. What's the number one thing you'd like to try and solve using AI technology in the in farming. I'll start with the Ahurica.

Thank you for the question.

Thank you for the question.

So it's a wonderful question and I could think of a million things that I could solve, and I'm pretty sure the farmers would also agree with me. But one of the things that I believe would be a very huge issue that AI could potentially solve is protecting farmers and their farms from climate change. Now, this is a huge issue that's cropping. Our global climatic changes are worsening every year. There's droughts everywhere, there's floods in some places, So things like that farmers should be protected from natural calamities disasters like that that could potentially just endanger their livelihoods and destroy their economic and social levels, and that is something that we should look into as AI enthusiast on how to protect far from that, and that I believe would be one way that AI could totally revolutionize the agricultural industry.

So it's a wonderful question and I could think of a million things that I could solve, and I'm pretty sure the farmers would also agree with me. But one of the things that I believe would be a very huge issue that AI could potentially solve is protecting farmers and their farms from climate change. Now, this is a huge issue that's cropping. Our global climatic changes are worsening every year. There's droughts everywhere, there's floods in some places, So things like that farmers should be protected from natural calamities disasters like that that could potentially just endanger their livelihoods and destroy their economic and social levels, and that is something that we should look into as AI enthusiast on how to protect far from that, and that I believe would be one way that AI could totally revolutionize the agricultural industry.

Excellent, Rishi, Kishi, You've had extra time to think, so yeah.

Excellent, Rishi, Kishi, You've had extra time to think, so yeah.

So basically the area that I'm also currently working on, that's the genomics selection of particular varieties in crop farms, and that's one area that AI can be used to analyze vast genomic data to identify genes associated with desirable crop traits that can adapt to the climate change. Because as you're proceeding, like we all know like where exactly we are proceeding on, so the only way is to adapt to the upcoming situations and to prevent it. So I'm working on the adaption side of the climate change in particularly farming. So we are trying to understand how these AI tools and AI can be used. Machine learning algorithms can be used to understand this various genomic data and create new genomes that can actually accelerate breeding programs, resulting in crops that are more disease resistant, nutritious.

So basically the area that I'm also currently working on, that's the genomics selection of particular varieties in crop farms, and that's one area that AI can be used to analyze vast genomic data to identify genes associated with desirable crop traits that can adapt to the climate change. Because as you're proceeding, like we all know like where exactly we are proceeding on, so the only way is to adapt to the upcoming situations and to prevent it. So I'm working on the adaption side of the climate change in particularly farming. So we are trying to understand how these AI tools and AI can be used. Machine learning algorithms can be used to understand this various genomic data and create new genomes that can actually accelerate breeding programs, resulting in crops that are more disease resistant, nutritious.

And adaptable to changing emitic conditions.

And adaptable to changing emitic conditions.

So that's one area that can be a very huge factor to revolutionize the farming sector.

So that's one area that can be a very huge factor to revolutionize the farming sector.

And Shwita, what's the number one area of AI technology you'd like to see.

And Shwita, what's the number one area of AI technology you'd like to see.

Actually focus on most sustainable and economical farming which as a result provides or becomes climate smart farming. So that is where adoption of smart farming practices right, which would really help grow India and the farmer and the community to which they belong.

Actually focus on most sustainable and economical farming which as a result provides or becomes climate smart farming. So that is where adoption of smart farming practices right, which would really help grow India and the farmer and the community to which they belong.

Excellent, excellent. I would like to thank my guests Rishi kish Ahmit Nayak, Swita Karuna and Niharika Haridas for joining me on this episode of Technically Speaking and Intel podcast. This was a very enjoyable discussion for me as I love talking with actual innovators, engineers and developers with fruits on the ground deploying technology and making a difference. What amakes me about the efforts was the use of the Intel Open Vino platform and it seemingly casual use of it. It was only a few years ago that running machine learning in AR models was a massive undertaking. The kishan No initiative that Ushikish and Erica have developed is a testament to the ability for new AI tools like Intel open Vino to speed up the development and deployment of industry changing technology. It was also important to understand the larger governmental impact on AI development. We heard from our guests the importance of ensuring that we strive to reduce any barriers to innovators from exploring, experimenting, and succeeding in their AI efforts democratization of technology. By continually striving to reduce the cost of AI deployments, two tools like Intel open Vino will be a boomed not only to the remote villages of India, but also in the tallest skyscrapers of New York. Please join us on Tuesday, October thirty first for the next episode of technically Speaking, an Intel podcast. Technically Speaking was produced by Ruby Studios from iHeartRadio in partnership with Intel, and hosted by me Graham Class. Our executive producer is Molly Sosher, our EP of Post Production is James Foster, and our supervising producer is Nikair Swinton. This episode was edited by Cierra Spreen and written and produced by Tyree Rush

Excellent, excellent. I would like to thank my guests Rishi kish Ahmit Nayak, Swita Karuna and Niharika Haridas for joining me on this episode of Technically Speaking and Intel podcast. This was a very enjoyable discussion for me as I love talking with actual innovators, engineers and developers with fruits on the ground deploying technology and making a difference. What amakes me about the efforts was the use of the Intel Open Vino platform and it seemingly casual use of it. It was only a few years ago that running machine learning in AR models was a massive undertaking. The kishan No initiative that Ushikish and Erica have developed is a testament to the ability for new AI tools like Intel open Vino to speed up the development and deployment of industry changing technology. It was also important to understand the larger governmental impact on AI development. We heard from our guests the importance of ensuring that we strive to reduce any barriers to innovators from exploring, experimenting, and succeeding in their AI efforts democratization of technology. By continually striving to reduce the cost of AI deployments, two tools like Intel open Vino will be a boomed not only to the remote villages of India, but also in the tallest skyscrapers of New York. Please join us on Tuesday, October thirty first for the next episode of technically Speaking, an Intel podcast. Technically Speaking was produced by Ruby Studios from iHeartRadio in partnership with Intel, and hosted by me Graham Class. Our executive producer is Molly Sosher, our EP of Post Production is James Foster, and our supervising producer is Nikair Swinton. This episode was edited by Cierra Spreen and written and produced by Tyree Rush