Podcasts about farmbeats

Headlines on today's episode include: NCGA president says UK trade deal a good sign, UK trade deal could create opportunity for U.S beef, Rep. Hinson lauds Trump and the UK trade deal, U.S. farmers & Purdue University helping solve corn storage concerns in Mexico, Chairman says Congress plans to deliver the President's “one big, beautiful bill”, and National FFA and Microsoft expanding FarmBeats for students to all 50 states.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Brownfield's Erin Anderson interviews Ankur Anand, Microsoft program manager, about FarmBeats for Students for at the 97th National FFA Convention in Indianapolis, Indiana.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Headstorm: https://headstorm.com/AGPILOT: https://headstorm.com/agpilot/Azure Data Manager for Agriculture (ADMA): https://azure.microsoft.com/en-us/products/data-manager-for-agricultureAg Powered Services: https://agpoweredservices.com/"Scaling Sustainability Through Bayer & Microsoft Partnership": https://www.bayer.com/en/agriculture/bayer-and-microsoft-partnership Today's episode features conversations with Claudia Roessler and Mark Pendergrast. A quick heads up on a couple of things before we dive in: first, both of these interviews were recorded at World Agri-Tech in San Francisco and they were other conversations happening in the media room for part of the time, so I hope you'll forgive a little bit of background noise. Second, similar to Amie Thesingh's episode last month, I originally recorded these interviews to be spotlight episodes featuring the work Headstorm does. Just like in Amie's case I thought this story warranted a full-length episode, so we will focus on the work Microsoft and Bayer are doing together, but I will also include the role Headstorm is playing in all of this as well. Just a heads up on that. You heard from both Claudia and Mark as part of our Generative AI episode which was #409, but the focus today is on this initiative started by Microsoft with their Azure Data Manager for Agriculture, or ADMA. We'll also explore the collaboration with Bayer Cropscience, in particular they're Ag Powered Services Platform that brings together agronomic data for a variety of applications. Because sometimes this data stuff can get a little abstract, I think it's probably helpful to level-set with some basics. Starting with cloud services. I think most of us intuitively know what a massive leap forward cloud computing has been for technology in general. From software applications to file storage to other sources of data - cloud computing is how we are able to power digitization. The cloud is not new obviously. But what has become clear is that just giving people access to the cloud isn't enough to really tap into the power of all of this information - it's just a place to store it. Moving from stored data to actionable data is a very very heavy lift - especially in an industry like agriculture. So, Microsoft started creating industry-specific data management platforms. They describe this as “industry-specific data connectors and capabilities to connect farm data from disparate sources.” They've been successful with similar efforts in other industries like retail, finance and healthcare, and last year they unveiled Azure Data Manager for Agriculture, a continuation of the work they were doing with FarmBeats, which you might remember from episode 266 with Microsoft's Ranveer Chandra. So when it comes to making data more valuable, the cloud is a massive step forward, now we have another massive step forward in ADMA, and we're also going to talk about what could be yet another massive step forward Bayer's Ag Powered Services. Bayer is providing additional data infrastructure that they first developed to use internally, and now are offering to other companies that rely on agronomic data to power their various digital applications. The ultimate goal here though is that data no longer becomes the bottleneck to progress. If a buyer, for example, wants to pay a farmer more for certain agronomic practices, all they need is...

Ever wonder how the worlds leading innovators think, spend their time, and the projects they are working on? If you do, you're in luck!  Mike J. Walker speaks to Ranveer Chandra , Managing Director Research for Industry at Microsoft. Listen in to hear advise and perspectives from Ranveer. He is truly a leader in the innovation industry.About FarmBeatsRanveer started the FarmBeats project at Microsoft in 2015, and has been leading it since then. He is also leading the battery research project, and the white space networking project at Microsoft Research. He was invited to the USDA to present his work on FarmBeats, and this work was featured by Bill Gates in GatesNotes, and was selected by Satya Nadella as one of 10 projects that inspired him in 2017. Ranveer has also been invited to the FCC to present his work on TV white spaces, and spectrum regulators from India, China, Brazil, Singapore and US (including the FCC chairman) have visited the Microsoft campus to see his deployment of the world's first urban white space network. As part of his doctoral dissertation, Ranveer developed VirtualWiFi. The software has over a million downloads and is among the top 5 downloaded software released by Microsoft Research. It is shipping as a feature in Windows since 2009.About Ranveer ChandraRanveer Chandra is the Chief Scientist of Microsoft Azure Global, where he is leading a team driving innovations across different industries on Azure. Ranveer's research has shipped as part of multiple Microsoft products, including VirtualWiFi and low-power algorithms in Windows 7, Windows 8, and Windows 10, Energy Profiler in Visual Studio, and the Wireless Controller Protocol in XBOX One. Ranveer started the FarmBeats project at Microsoft Research in 2015, and has been leading it since then. He is also leading the battery research project, and the white space networking project at Microsoft Research. He was invited to the USDA to present his work on FarmBeats, including to the Secretary, this work was featured by Bill Gates in GatesNotes, and was selected by Satya Nadella as one of 10 projects that inspired him in 2017. Ranveer has also been invited to the FCC to present his work on TV white spaces, and spectrum regulators from India, China, Brazil, Singapore and US (including the FCC chairman) have visited the Microsoft campus to see his deployment of the world's first urban white space network. As part of his doctoral dissertation, Ranveer developed VirtualWiFi. The software has been downloaded more than 750,000 times and is among the top 5 downloaded software released by Microsoft Research. It is shipping as a feature in Windows since 2009.Ranveer has published more than 85 papers, and filed over 100 patents, more than 80 of which have been granted. His research has been cited by the popular press, such as MIT Technology Review, The Economist, New York Times, WSJ, among others. He has won several awards, including best paper awards, and the MIT Technology Review's Top Innovators Under 35, TR35. Ranveer has an undergraduate degree from IIT Kharagpur, India and a PhD from Cornell University. 

Microsoft Farm Beats is data-driven techniques that will help improve farm productivity while cutting agricultural losses.

Microsoft Farm Beats is data-driven techniques that will help improve farm productivity while cutting agricultural losses.

With FarmBeats, Microsoft wants to build the necessary infrastructure for other companies to operate.

Microsoft's Ranveer Chandra says we are starting to see the democratization of data-driven agriculture.

Ranveer Chandra: https://www.microsoft.com/en-us/research/people/ranveer/ Overview of Azure FarmBeats: https://docs.microsoft.com/en-us/azure/industry/agriculture/overview-azure-farmbeats  FarmBeats: https://www.microsoft.com/en-us/research/publication/bill-gates-features-farmbeats-on-gatesnotes/ Microsoft has been making waves in the agtech industry with its FarmBeats project and Azure cloud computing service. That effort can be traced back to 2015 when today's guest, Ranveer Chandra, wrote a memo which led to him starting and running the FarmBeats project. FarmBeats for those who do not know, provides a way to collect on-farm data and track that data using cloud computing models. It's not a product that farmers buy, but it's a platform that agtech companies build upon. In fact, previous guests of this show are customers of Microsoft to power their technology.    Ranveer is the Chief Scientist of Microsoft Azure Global, and Partner Researcher at Microsoft Research. He started the FarmBeats project at Microsoft in 2015, and has been leading it since then. He is also leading the battery research project, and the white space networking project at Microsoft Research. That is a project where he provided rural connectivity using unused TV channels. He was invited to the USDA to present his work on FarmBeats, and this work was featured by Bill Gates in GatesNotes, and was selected by Satya Nadella as one of 10 projects that inspired him in 2017.  Ranveer has published more than 80 papers, and filed over 100 patents, more than 85 of which have been granted by the USPTO.  Both FarmBeats and the TV white spaces projects started with memos, and as you'll hear, Ranveer wrote his 2020 memo on sustainability. So we also get into the discussion about sustainability metrics and Microsoft's big open source carbon purchase from a few months ago. 

Episode 009 | June 15, 2021 The Internet of Things has been around for a few years now and many businesses and organizations depend on data from these systems to make critical decisions. At the same time, it is also well recognized that this data- even up to 40% of it- can be spurious, and this obviously can have a tremendously negative impact on an organizations' decision making. But is there a way to evaluate if the sensors in a network are actually working properly and that the data generated by them are above a defined quality threshold? Join us as we speak to Dr Akshay Nambi and Ajay Manchepalli, both from Microsoft Research India, about their innovative work on making sure that IoT data is dependable and verified, truly enabling organizations to make the right decisions. Akshay Nambi is a Senior Researcher at Microsoft Research India. His research interests lie at the intersection of Systems and Technology for Emerging Markets broadly in the areas of AI, IoT, and Edge Computing. He is particularly interested in building affordable, reliable, and scalable IoT devices to address various societal challenges. His recent projects are focused on improving data quality in low-cost IoT sensors and enhancing performance of DNNs on resource-constrained edge devices. Previously, he spent two years at Microsoft Research as a post-doctoral scholar and he has completed his PhD from the Delft University of Technology (TUDelft) in the Netherlands.  Ajay Manchepalli, as a Research Program Manager, works with researchers across Microsoft Research India, bridging Research innovations to real-world scenarios. He received his Master's degree in Computer Science from Temple University where he focused on Database Systems. After his Masters, Ajay spent his next 10 years shipping SQL Server products and managing their early adopter customer programs. For more information about the Microsoft Research India click here. Related Microsoft Research India Podcast: More podcasts from MSR India iTunes: Subscribe and listen to new podcasts on iTunes Android RSS Feed Spotify Google Podcasts Email Transcript Ajay Manchepalli: The interesting thing that we observed in all these scenarios is how the entire industry is trusting data, and using this data to make business decisions, and they don't have a reliable way to say whether the data is valid or not. That was mind boggling. You're calling data as the new oil, we are deploying these things, and we're collecting the data and making business decisions, and you're not even sure if that data that you've made your decision on is valid. To us it came as a surprise that there wasn't enough already done to solve these challenges and that in some sense was the inspiration to go figure out what it is that we can do to empower these people, because at the end of the day, your decision is only as good as the data. [Music] Sridhar Vedantham: Welcome to the Microsoft Research India podcast, where we explore cutting-edge research that's impacting technology and society. I'm your host, Sridhar Vedantham. [Music] The Internet of Things has been around for a few years now and many businesses and organizations depend on data from these systems to make critical decisions. At the same time, it is also well recognized that this data- even up to 40% of it- can be spurious, and this obviously can have a tremendously negative impact on an organizations' decision making. But is there a way to evaluate if the sensors in a network are actually working properly and that the data generated by them are above a defined quality threshold? Join us as we speak to Dr Akshay Nambi and Ajay Manchepalli, both from Microsoft Research India, about their innovative work on making sure that IoT data is dependable and verified, truly enabling organizations to make the right decisions. [Music] Sridhar Vedantham: So, Akshay and Ajay, welcome to the podcast. It's great to have you guys here. Akshay Nambi: Good evening Sridhar. Thank you for having me here. Ajay Manchepalli: Oh, I'm excited as well. Sridhar Vedantham: Cool, and I'm really keen to get this underway because this is a topic that's quite interesting to everybody, you know. When we talk about things like IoT in particular, this has been a term that's been around for quite a while, for many years now and we've heard a lot about the benefits that IoT can bring to us as a society or as a community, or as people at an individual level. Now you guys have been talking about something called Dependable IoT. So, what exactly is Dependable IoT and what does it bring to the IoT space? Ajay Manchepalli: Yeah, IoT is one area we have seen that is exponentially growing. I mean, if you look at the number of devices that are being deployed it's going into the billions and most of the industries are now relying on this data to make their business decisions. And so, when they go about doing this, we have, with our own experience, we have seen that there are a lot of challenges that comes in play when you're dealing with IoT devices. These are deployed in far off locations, remote locations and in harsh weather conditions, and all of these things can lead to reliability issues with these devices. In fact, the CTO of GE Digital mentioned that, you know, about 40% of all the data they see from these IoT devices are spurious, and even KPMG had a report saying that you know over 80% of CEOs are concerned about the quality of data that they're basing their decisions on. And we observed that in our own deployments early on, and that's when we realized that there is, there is a fundamental requirement to ensure that the data that is being collected is actually good data, because all these decisions are being based on the data. And since data is the new oil, we are basically focusing on, ok, what is it that we can do to help these businesses know whether the data they're consuming is valid or not and that starts at the source of the truth, which is the sensors and the sensor devices. And so Akshay has built this technology that enables you to understand whether the sensors are working fine or not. Sridhar Vedantham: So, 40% of data coming from sensors being spurious sounds a little frightening, especially when we are saying that you know businesses and other organizations base a whole lot of the decisions on the data they're getting, right? Ajay Manchepalli: Absolutely. Sridhar Vedantham: Akshay, was there anything you wanted to add to this? Akshay Nambi: Yeah, so if you see, reliability and security are the two big barriers in limiting the true potential of IoT, right? And over the past few years you would have seen IoT community, including Microsoft, made significant progress to improve security aspects of IoT. However, techniques to determine data quality and sensor health remain quite limited. Like security, sensor reliability and data quality are fundamental to realize the true potential of IoT which is the focus of our project- Dependable IoT. Sridhar Vedantham: Ok, so you know, once again, we've heard these terms like IoT for many years now. Just to kind of demonstrate what the two of you have been speaking about in terms of various aspects or various scenarios in which IoT can be deployed, could you give me a couple of examples where IoT use is widespread? Akshay Nambi: Right, so let me give an example of air pollution monitoring. So, air pollution is a major concern worldwide, and governments are looking for ways to collect fine grained data to identify and curb pollution. So, to do this, low-cost sensors are being used to monitor pollution levels. There have been deployed in numerous places on moving vehicles to capture the pollution levels accurately. The challenge with these sensors are that these are prone to failures, mainly due to the harsh environments in which they are deployed. For example, imagine a pollution sensor is measuring high pollution values right at a particular location. And given air pollution is such a local phenomenon, it's impossible to tell if this sensor data is an anomaly or a valid data without having any additional contextual information or sensor redundancy. And due to these reliability challenges the validity and viability of these low-cost sensors have been questioned by various users. Sridhar Vedantham: Ok, so it sounds kind of strange to me that sensors are being deployed all over the place now and you know, frankly, we all carry sensors on ourselves, right, all the time. Our phones have multiple sensors built into them and so on. But when you talk about sensors breaking down or being faulty or not providing the right kind of data back to the users, what causes these kind of things? I mean, I know you said in the context of, say, air pollution type sensors, you know it could be harsh environments and so on, but what are other reasons for, because of which the sensors could fail or sensor data could be faulty? Akshay Nambi: Great question, so sensors can go bad for numerous reasons, right? This could be due to sensor defect or damage. Think of a soil moisture sensor deployed in agricultural farm being run over by a tractor. Or it could be sensor drift due to wear and tear of sensing components, sensor calibration, human error and also environmental factors, like dust and humidity. And the challenge is, in all these cases, right, the sensors do not stop sending data but still continues to keep sending some data which is garbage or dirty, right? And the key challenge is it is nontrivial to detect if a remote sensor is working or faulty because of the following reasons. First a faulty sensor can mimic a non-faulty sensor data which is very hard to now distinguish. Second, to detect sensor faults, you can use sensor redundancy which becomes very expensive. Third, the cost and logistics to send a technician to figure out the fault is expensive and also very cumbersome. Finally, time series algorithms like anomaly detectors are not reliable because an anomaly need not imply it's a faulty data. Sridhar Vedantham: So, a quick question on one of the things that you've said. When you're talking about sensor redundancy, this just means that deploy multiple sensors, so if one fails then you use the other one or do you use data from the other one. Is that what that means? Akshay Nambi: Yeah, so sensor redundancy can be looked at both the ways. When one fails you could use the other, but it also be used to take the majority voting of multiple sensors in the same location. Going back to my air pollution example, if multiple sensors are giving very high values right, then you have high confidence in the data as opposed to thinking that is a faulty data. So that's how sensory redundancy is typically used today. Sridhar Vedantham: OK, and there you just have to take on faith that the data that you're getting from multiple sensors is actually valid. Akshay Nambi: Exactly, exactly. You never know that if all of them could have undergone the same fault. Sridhar Vedantham: Right. Ajay Manchepalli: It's interesting that when we think about how the industry tries to figure out if the sensors are working or not. There are three distinct approaches that we always observe, right? One is you have the sense of working, but you also try to use additional surrounding data. For example, let's say it's raining heavily, but your moisture sensor is indicating that the moisture level is low. That data doesn't align, right. The weather data indicates there's rains, but the moisture sensor is not giving you the right reading, so that's one way people can identify it's working or not. The other is what we just talked about, which is sensor redundancy- just to increase the number of sensors in that area and try to poll among a bunch of sensors. That also makes sense. And the third one is what typically you really can trust and that is you deploy someone out there physically, go look at the sensor and then have it tested. And if you start thinking about the scenarios we are talking about, which is remote locations, far away locations- imagine deploying sensors across the country, having to send people out and validate and things like that. There is cost associated to sending people as well as you have that sort of a down time, and so being able to, you know, remotely and reliably be able to say that the sensor is at fault, is an extremely empowering scenario. And as we look at this, it's not just sensor reliability, right? For example, if you think of a telephone, a landline, right, you have the dial tone which tells you if the phone is working or not, right? Similarly, we are trying to use certain characteristics in these sensors, that tells us if it's working or not. But the beauty of this solution is it's not just limited to being a dial tone for sensors, it is more than that. It not only tells you whether it is working or not, it can tell you if it is the sensor you intended to deploy. I mean, think of it this way. A company could work with a vendor and procure certain class of sensors and they have an agreement for that. And when these sensors are deployed, the actual sensors that get deployed may or may not be in that class of devices, intentionally or unintentionally, right? How do you know that? If we understand the nature of the sensor, we can actually remotely identify the type of sensor that is deployed and help industries essentially figure out whether the sensor that's deployed is the sensor you intended to. So it's more than just whether the sensor is working, you can identify it, you can even figure out things like data drift. This is a pretty powerful scenario that we are going after. Sridhar Vedantham: Right, and that's a lovely teaser for me to ask my next question. What exactly are you guys talking about and how do you do this? Akshay Nambi: Right, so our key value proposition right is basically a simple and easy way to remotely measure and observe the health of the sensor. The core technology behind this value proposition is the ability to automatically generate a fingerprint. When I say a fingerprint, what I'm referring to is the unique electrical characteristic, exhibited by these sensors, both analog and digital. Let me give an example. So, think of analog sensors which produce continuous output signal proportional to the quantity being measured. Our key insight here is that a sensor's voltage response right after powering down exhibits a unique characteristic, what we refer to as fall curve? This fall curve is dependent upon the sensor circuitry and the parasitic elements present in the sensor, thereby making it unique for each sensor type. So think of this as basically as fall curve acts as a reference signature for the sensor when it is working. And when the sensor goes bad, this fall curve drastically changes, and now by just comparing this fingerprint, we can tell whether a sensor is working or faulty. Ajay Manchepalli: The interesting part about the fingerprint that Akshay just mentioned is that it is all related to the physical characteristics of the sensors, right? You have a bunch of capacitors, resistors, all of those things put together to build the actual sensor device. And each manufacturer or each sensor type or each scenario would have a different circuit and because of that, when you power down this, because of its physical characteristics, you see different signatures. So this is a unique way of being able to identify not just what type of sensor, but even based on the manufacturer, because the circuitry for that particular manufacturer will be different. Sridhar Vedantham: So, just to clarify, when you're saying that sensors have unique fingerprints, are you talking about particular model of a sensor or a particular class of a sensor or a particular type of sensor? Akshay Nambi: Right, great question again. So, these fingerprints are unique for that particular type of sensors. For example, take soil moisture sensor from Seed Studio, for that particular type of sensor from that manufacturer, this signature remains the same. So all you have to do is for that manufacturer and for that sensor type you collect the fingerprint once. And then you can use that to compare against the operational fingerprints. Similarly, in case of digital sensors we use current drawn as a reference fingerprint to detect whether the sensor is working or not, and the key hypothesis here behind these fingerprints, is that when a sensor accumulates damage, we believe its physical properties also change, leading to a distinct current profile compared to that of a working sensor? And that's the key property behind developing these fingerprints and one of the key aspects of these fingerprints is also that this is unaffected by the external factors like environmental changes like temperature, humidity, right. So these fingerprints are unique for each sensor type and also are independent of the environmental changes. In that way, once you collect a fingerprint that should hold good irrespective of your scenario where you are deploying the sensor. Ajay Manchepalli: One other thing that I want to call out there is the beauty of this electrical signatures is based on the physical characteristics, right? So, it's not only when this device fails that the physical characteristics changes, and hence the signature changes, but also the beauty of this is that over time, when things degrade, that implies that the physical characteristics of that sensor or the device is also degrading, and when that happens, your electrical signature also shows that kind of degradation, and that is very powerful, because now you can actually identify or track the type of data drift that people are having. And when you observe such data drift, you can have calibration mechanisms to kind of recalibrate the data that you're getting and continue to function while you deploy people out and get it rectified, and things like that. So, it almost gives you the ability to have a planned downtime because you're not only seeing that the sensor has failed, but you are observing that the sensor will potentially fail down the line and you can take corrective actions. Sridhar Vedantham: Right, so basically you're getting a heads up that something bad is going to happen with the sensor. Ajay Manchepalli: Exactly. Sridhar Vedantham: Great. And have you guys actually deployed this out in the field in real world scenarios and so on to figure out whether it works or not? Akshay Nambi: Yeah, so this technology is already deployed in hundreds of devices in the space of agricultural farms, water monitoring and air pollution monitoring. To give you a concrete example, we are working with a company called Respirer who is using dependable IoT technology to provide reliable high fidelity pollution data to its customers and also policymakers. So, for example, Respirer today is able to provide for every data point what they measure, they are able to provide the status of the sensor, whether a sensor is working or faulty. This way users can filter out faulty or drifted data before consuming them. This has significantly increased the credibility of such low-cost sensors and the data that it is generating. And the key novelty to highlight again here is that we do this without any human intervention or redundancy. And in fact, if you think about it, we are not even looking at the sensor data. We are looking at these electrical characteristics, which is completely orthogonal to data, to determine whether the sensor is working, faulty, or drifted. Ajay Manchepalli: The interesting part of this work is that we observed in multiple real-world scenarios that there was a real need for reliability of such sensors, and it was really impacting their function. For example, there is a team that's working on smart agriculture, and the project is called FarmBeats. And in that case, we observed that they had these sensors deployed out in the fields and out there in the farms, you have harsh conditions, and sensors could easily get damaged, and they had to actually deploy people to go and figure out what the issue is. And it became very evident and clear that it was important for us to be able to solve that challenge of helping them figure out if the sensor is working or not, and the ability to do that remotely. So that that was sort of the beginning and maybe Akshay, you can talk about the other two projects that led after that. Akshay Nambi: Right, so another example is Respirer who is using dependable IoT technology to provide reliable, high-fidelity pollution data to its customers and policymakers. So they are now measuring the sensor status for every time they measure pollution data to determine whether the data which was measured, as from a working or a faulty or a drifted sensor. This way the users can filter out faulty or drifted data before they consume them. And this has significantly increased the credibility of low-cost sensors and the data it is measuring. To give another example, we're also working with Microsoft for Startups and Accenture for a particular NGO called. Jaljeevika, which focus on improving livelihood of small-scale fish farmers. They have a IoT device that monitors temperature, TDS, pH of water bodies to provide advisories for fish farmers. Again, since these sensors are deployed in remote locations and farmers are relying on this data and the advice being generated, it is very critical to collect reliable data. And today Jaljeevika is using dependable IoT technology to ensure the advices generated is based on reliable IoT data. [Music] Sridhar Vedantham: Right, so this is quite inspiring, that you've actually managed to deploy these things in, you know, real life scenarios and it's already giving benefits to the people that you're working with. You know, what always interests me with research, especially when you have research that's deployed in the field- is there anything that came out of this that surprised you in terms of learning, in terms of outcome of the experiments that you conducted? Akshay Nambi: Yeah, so I can give you one concrete learning, right, going back to air pollution sensors, so we have heard partners identifying these sensors going bad within just few weeks of deployment. And today they have no way to figure out what was wrong with these sensors. Using out technology, in many cases they were able to pinpoint, yes, these are faulty sensor which needed replacement right? And there was also another interesting scenario where the sensor is working well- it's just that because of dust, the sensor was showing wrong data. And we were able to diagnose that and inform the partner that all you have to do is just clean the sensor, which should bring back to the normal state as opposed to discarding that. So that was a great learning in the field what we had. Ajay Manchepalli: The interesting thing that we observed in all these scenarios is how the entire industry is trusting data, and using this data to make business decisions, and they don't have a reliable way to say whether the data is valid or not. That was mind boggling. You're calling data as the new oil, we are deploying these things, and we're collecting the data and making business decisions, and you're not even sure if that data that you've made your decision on is valid. To us it came as a surprise that there wasn't enough already done to solve these challenges and that in some sense was the inspiration to go figure out what it is that we can do to empower these people, because at the end of the day, your decision is only as good as the data. Sridhar Vedantham: Right. So, you know, one thing that I ask all my guests on the podcast is, you know, the kind of work that you guys do and you're talking about is truly phenomenal. And is there any way for people outside of Microsoft Research or Microsoft to actually be able to use the research that you guys have done and to be able to deploy it themselves? Akshay Nambi: Yeah. Yeah. So all our work right is in public domain. So we have published numerous top conference papers in the areas of IoT and sensors. And all of these are easily accessible from our project page aka.ms/dependableIoT. And in fact, recently we also made our software code available through a SDK on GitHub, which we call as Verified Telemetry. So today IoT developers can now seamlessly integrate this SDK into their IoT device and get sensor status readily. We have also provided multiple samples on how do you integrate with the device, how do you use a solution sample and so on. So if you are interested, please visit aka.ms/verifiedtelemetry to access our code. Sridhar Vedantham: Right, and it's also very nice when a research project name clearly and concisely says what it is all about. Verified Telemetry- it's a good name. Akshay Nambi: Thank you. Sridhar Vedantham: All right, so we're kind of coming to the end of the podcast. But before we, you know, kind of wind this thing up- what are you looking at in terms of future work? I mean, where do you go with this? Akshay Nambi: So, till now we are mostly focused on some specific scenarios in environmental monitoring and so on, right? So, one area which we are deeply thinking is towards autonomous and safety critical systems. Imagine a faulty sensor in a self-driving vehicle or an autonomous drone, right? Or in an automated factory floor, where data from these sensors are used to take decisions without human in the loop. In such cases, bad data leads to catastrophic decisions. And recently we have explored one such safety critical sensor, which is smoke detectors. And as we all know, smoke detectors are being deployed in numerous scenarios right from hospitals to shopping malls to buildings, and the key question which we went after, right, is how do you know if your smoke detector is working or not, right? To address this, what today people do is, especially in hospitals, they do a manual routine maintenance check where a person uses a aerosol can, to trigger the smoke alarm and then turn them off in the back end. Sridhar Vedantham: OK, that does not sound very efficient. Akshay Nambi: Exactly, and it's also a very laborious process and significantly limits the frequency of testing? And the key challenge, unlike other sensors here, is you cannot notice failures until unless there is a fire event or smoke. Sridhar Vedantham: Right. Akshay Nambi: Thus it is very imperative to know whether your detector is working or not in a non-smoke condition. We have again developed a novel fingerprint which can do this and this way we can detect if a sensor is working or faulty even before a fire event occurred and alert the operators in a timely manner. So for those who are interested to understand and curious of how would you do that, please visit our webpage and access the manuscript. Sridhar Vedantham: Yeah, so I will add links to the web page as well as to the GitHub repository in the transcript of this podcast. Akshay Nambi: Thank you. Sridhar Vedantham: Ajay, was there something you wanted to add to that? Ajay Manchepalli: Yeah, in all our early deployments that we have made, we have seen that sensor fault is one of the primary issues that comes in play and that's what this has been addressing. But there are many other scenarios that come up that are very relevant and can empower the scenarios even further and that is things like, when you have the data drift or when you observe that the sensors are not connected correctly to the devices and so is some sort of sensor identification. These are some of the things that we can extend on top of what we already have. And while they are incremental changes in terms of the capability, the impact and the potential it can have for those scenarios is tremendous. And that's what keeps it exciting is that all the work that we are doing is driven by the actual needs that we are seeing out there in the field. Sridhar Vedantham: Excellent work. And Akshay and Ajay, thank you so much once again for your time. Akshay Nambi: Thank you Sridhar. Great having this conversation with you. Ajay Manchepalli: Yep, thanks Sridhar. This is exciting work, and we can't wait to do more and share more with the world. [Music]  

If rural connectivity challenges are going to be solved, private sector investment and innovation will be necessary. Dr. Ranveer Chandra, the Chief Scientist of Microsoft Azure Global and Partner Researcher at Microsoft Research, joins the FarmBits Podcast to discuss Microsoft's investment in enabling data connectivity and interoperability in agriculture. Ranveer initiated the FarmBeats project at Microsoft Research which has recently led to the launch of Azure FarmBeats, which is a cloud platform enabling data-driven insights in agricultural applications. The FarmBeats project has also resulted in innovative approaches for establishing broadband connectivity on farms, such as using vacant TV white spaces to facilitate data transfer in rural areas. In this episode, Ranveer covers everything from the development of this TV white space technology, to emerging technologies in the developing world, to how value can be extracted from data once it is consolidated and made available. While Microsoft is not a traditional player in the agricultural industry, the data pipelines and enabling technology they provide have significant potential to enhance agriculture alongside the expertise of experienced agricultural professionals. "For growers, it would be more about thinking, 'Hey, I know a lot about the farm, which are the parts if I had this additional data that I could do better?'" - Ranveer Chandra, Ph.D. FarmBeats Information: FarmBeats Website: https://www.microsoft.com/en-us/research/project/farmbeats-iot-agriculture/ Azure FarmBeats: https://www.microsoft.com/en-in/campaign/azure-farmbeats/ GatesNotes Wi-Fi Chip Blog: https://www.gatesnotes.com/Development/FarmBeats White Space Deployment in Africa: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/main-5.pdf Economist Article: https://www.economist.com/science-and-technology/2016/09/17/tv-dinners USDA Using FarmBeats: https://news.microsoft.com/features/feed-the-world-how-the-usda-is-using-data-and-ai-to-address-a-critical-need/ Ranveer Chandra's Info: LinkedIn: https://www.linkedin.com/in/ranveer-chandra-79bb9b/ Bio: https://www.microsoft.com/en-us/research/people/ranveer/ FarmBits Team Contact Info: E-Mail: farmbits@unl.edu Twitter: https://twitter.com/NEDigitalAg Samantha's Twitter: https://twitter.com/SamanthaTeten Jackson's Twitter: https://twitter.com/jstansell87 Opinions expressed by the hosts and guests on this podcast are solely their own, and do not reflect the views of Nebraska Extension or the University of Nebraska - Lincoln.

The future of agriculture is going to be a high-tech dream, and Microsoft has plans to be part of it.

The future of agriculture is going to be a high-tech dream, and Microsoft has plans to be part of it.

We take a look at Azure FarmBeats, which was initially made available in late 2019. It's a solution to managing your crops, and what Microsoft calls democratizing agriculture intelligence. It's definitely an interesting industry solution that runs natively on Azure!

Dr. Ranveer Chandra, chief scientist at Microsoft Azure Global, joins us to explain the company's FarmBeats project and how it is taking the guesswork out of agriculture. According to Chandra, data-driven agriculture makes operations more profitable and sustainable — but some farmers rely simply on their instincts and personal experience to make decisions about their crops or animals. Chandra and the FarmBeats project seek to provide these farmers with data and insights about their operations that will help them make informed decisions for their production.

Ranveer Chandra Chief Scientist Microsoft Azure and FarmBeats researcher is looking towards a not-too-distant future where some of the world's poorest farmers can access low cost data-capture technology using something as ubiquitous as a mobile phone. And how about those old TV aerials? Turns out they're really handy too. This Season 2 interview was recorded at evokeAG 2020, the Asia Pacific region's largest Agrifood tech event, brought to you by AgriFutures Australia. This podcast is produced in partnership with the University of Adelaide, Ramona Dalton Communications and Basem3nt Enterprises.

Zerina Kapetanovic is at the intersection of technological innovation and food production. She’s working with Microsoft’s FarmBeats team to enable data-driven farming. Microsoft believes that data, coupled with the farmer’s knowledge and intuition about their own farm, can help increase farm productivity, and also help reduce costs. The team are building several unique solutions using low-cost sensors, drones, and vision and machine learning algorithms.

Dr. Ranveer Chandra gives Scott Hanselman an introduction to Azure FarmBeats at Microsoft Ignite 2019. FarmBeats is a business-to-business offering available in Azure Marketplace. It enables the aggregation of data from farms -- across sensors, drones, robots, satellites, and weather providers -- and generation of actionable insights using artificial intelligence (AI) and machine learning (ML) models built on fused datasets.Overview of Azure FarmBeats (Preview)Democratizing agriculture intelligence: Introducing Azure FarmBeatsFarmBeats: AI, Edge & IoT for AgricultureAzure FarmBeats on Azure MarketplaceCreate a free account (Azure)

Dr. Ranveer Chandra gives Scott Hanselman an introduction to Azure FarmBeats at Microsoft Ignite 2019. FarmBeats is a business-to-business offering available in Azure Marketplace. It enables the aggregation of data from farms -- across sensors, drones, robots, satellites, and weather providers -- and generation of actionable insights using artificial intelligence (AI) and machine learning (ML) models built on fused datasets.Overview of Azure FarmBeats (Preview)Democratizing agriculture intelligence: Introducing Azure FarmBeatsFarmBeats: AI, Edge & IoT for AgricultureAzure FarmBeats on Azure MarketplaceCreate a free account (Azure)

Welcome to episode #31 of IoT Product Leadership, a podcast featuring in-depth conversations with product leaders on what it takes to build great IoT products. I’m your host, Daniel Elizalde.     IoT can be a force of good to help fight some of the biggest problems in the world. That’s why I’m so excited about this episode. My guest today is Ranveer Chandra, Principal Researcher, Microsoft.     In this episode, Ranveer walks us through the FarmBeats project, where he leads research on applying IoT to improve yields in farms.    This is the fourth episode in my IoT series with Microsoft and it’s a great one. To learn more about Ranveer, about Microsoft FarmBeats, and to access the resources mentioned in this episode, visit iotproductleadership.com.      I’m excited to share with you that I’m embarking on a new IoT journey. I’m thrilled to be joining a successful company and have the opportunity to contribute to their IoT efforts.     Therefore, this is the last episode I’ll release for some time. I’ll continue to create great IoT content for you, but in a different way. I’m excited to share more details with you soon.    But for now, I want to take a moment to thank all the people that have contributed to the podcast. First of all, thank you to all my guests for your time and your wisdom. Also, thank you to my team, Erin Russell and Nina Pollock. Without you, there would be no podcast! And of course, thank you for listening and for all those emails you sent me with your support.    Thank you from the bottom of my heart.     About Ranveer Chandra:  Ranveer Chandra is the Chief Scientist at Azure Global. His research has shipped in multiple Microsoft products, including Windows, XBOX, Azure, Visual Studio, and Surface. Ranveer is leading the FarmBeats, battery research, and TV white space projects at Microsoft. He has published over 80 papers, and filed over 100 patents, of which over 85 granted by the USPTO. He has won several awards, including the MIT Technology Review’s Top Innovators Under 35 (TR35). Ranveer has a PhD from Cornell University.   Topics we discuss in this episode:  Ranveer shares his background and about Azure Global. The FarmBeats project, how it got started and what its goal is. The components of the solution: sensors, gateway, edge software, cloud platform, AI, front-end applications. How Ranveer used TV white spaces to enable Wi-Fi connectivity. The use of drones in the FarmBeats project. Top learnings discovered throughout the FarmBeats project. Ways to generalize these learnings so that more farmers can benefit from this research and those IoT solutions. The impact of FarmBeats so far and how Ranveer envisions it helping farmers globally. Advice for Product Leaders who are new at developing IoT solutions.    To learn more about Ranveer:  Ranveer on LinkedIn On Twitter @RanveerChandra FarmBeats tracks soil, moisture data 24/7   Free download: Don’t forget to download my IoT product strategy template, for free.   Related Resources: What Is An IoT Product Manager? IoT Framework for Product Managers How to Build an IoT Product Roadmap

Climate change, food shortages, and droughts. The future looks bleak. In this episode we look at two papers and talk about the role technology is playing in making the future look a bit brighter. As always feel free to get involved with the show at @whatsindienews on twitter or whatsindienews@gmail.com https://ijcsi.org/papers/IJCSI-10-1-1-7-12.pdf https://www.microsoft.com/en-us/research/wp-content/uploads/2017/03/FarmBeats-webpage-1.pdf https://www.actionagainsthunger.org/ https://www.heifer.org/ --- Send in a voice message: https://anchor.fm/whats-indie-news/message

Hello and welcome back to Tech Forward! On this week’s episode, I spoke with Jennifer Marsman, the Principal Software Engineer of Microsoft’s “AI for Earth” group. Working with the group, she uses data science, machine learning, and artificial intelligence to aid with clean water, agriculture, biodiversity, and climate change. Jennifer has been recognized as one of the “top 100 most influential individuals in artificial intelligence and machine learning” by Onalytica, reaching the #2 slot in 2018. We discussed the work she has led with AI for Earth, the issue of bias, and her insights about building a welcoming culture in the tech sector. By running a global grant program, developing their own APIs, and funding projects, AI for Earth is, in Jennifer’s words, “a chance to use my passion for machine learning to make a difference.” One such project is FarmBeats, which is interested in sustainable, long-term solutions to maximize yield and, ultimately, reduce world hunger. To do this, they synthesize IoT, drones, machine learning, and cutting edge networking research to create precision agricultural techniques. A combination of smart sensors and aerial imagery — from drones or large helium balloons — can create a moisture map of any given field, and enable precision irrigation. Similarly, precision pesticide application benefits the environment, the farmer’s budget, and the consumer. Another initiative, Project Premonition, puts mosquitoes to work in order to predict outbreaks of diseases. Mosquitoes collect a large distribution of blood samples, and by analyzing those samples within their genomics pipeline, Project Premonition can determine the animal the blood came from and any diseases it might be carrying to generate a real-time health map of an area. Under the current model, where doctors report local cases of disease to a larger agency such as the Centers for Disease Control, it can take weeks or even a month to identify an outbreak. Using machine learning every step of the way — from placement of the traps, to making sure mosquitoes are trapped with 90% accuracy, to analyzing the data of the blood samples — Project Premonition aims to streamline and accelerate that process. While machine learning is obviously a powerful tool, there is always the danger of bias. “Any bias in the historical data that you use to make future predictions will be captured in your machine learning algorithm. You have to make sure that your users know how the data was collected. Your model is only as  good as the data you train it with.” Good intentions, such as trying to identify at-risk students and preemptively give them more support, can turn into a self-fulfilling prophecy. This is yet another strong argument in favor of diverse perspectives at all stages of product development. When it comes to tech’s diversity issues, Jennifer identified two distinct, but related issues: attracting a diverse workforce, and maintaining that diversity. She emphasized the importance of media representation — from computer games with playable female protagonists, to television shows with women in tech roles — to her own personal career journey as well as the journeys of young women today. From her internship days at Ford Motor Company, she has witnessed firsthand how crucial teamwork is: not only to coding, but also to creating a welcoming environment that everyone wants to be a part of. Jennifer, thank you so much for bringing your passion and insights to the show today. Thank you also to everyone tuning in, leaving reviews, and sharing the show with your friends and colleagues. See you next week! Connect with us Website | Twitter | Instagram | Facebook

Ryan Locicero, environmental engineer and AAAS Science & Technology Policy Fellow at the National Science Foundation, speaks with Ranveer Chandra at the Microsoft Research Lab. As a principal researcher, Chandra leads an Incubation on IoT Applications. His research has shipped as part of multiple Microsoft products, including VirtualWiFi in Windows 7 onwards, low power Wi-Fi in Windows 8, Energy Profiler in Visual Studio, Software Defined Batteries in Windows 10, and the Wireless Controller Protocol in XBOX One. He has published more than 80 papers, and has been granted more than 85 patents by the USPTO. His research has been cited by the media including The Economist, MIT Technology Review, BBC, Scientific American, New York Times, and the WSJ. He also leads the battery research project and the white space networking projects. Here he discusses Microsoft’s FarmBeats project, which is building several unique solutions to enable data-driven farming, including low-cost sensors, drones, machine vision, and machine learning algorithms.   This podcast does not necessarily reflect the views of AAAS, its Council, Board of Directors, officers, or members. AAAS is not responsible for the accuracy of this material. AAAS has made this material available as a public service, but this does not constitute endorsement by the association.

Peggy says achieving an increase in food production is challenging for a number of reasons including receding water levels, climate change, and a shrinking amount of land. She explains that in general data-driven techniques can help boost agriculture productivity by increasing yields, reducing losses, and cutting down input costs. However, these techniques have seen low adoption because of the high costs of manual data collection and limited connectivity. She says Microsoft FarmBeats can help, as it enables data collection from various sensor types and it leverages recent work in unlicensed TV white spaces.

Peggy says achieving an increase in food production is challenging for a number of reasons including receding water levels, climate change, and a shrinking amount of land. She explains that in general data-driven techniques can help boost agriculture productivity by increasing yields, reducing losses, and cutting down input costs. However, these techniques have seen low adoption because of the high costs of manual data collection and limited connectivity. She says Microsoft FarmBeats can help, as it enables data collection from various sensor types and it leverages recent work in unlicensed TV white spaces.