Close Welcome writers, influencers and dreamers, make the world a greener place
Register here
Forgot password
Forgot password
or
or

Close
Close Stay Updated on Environmental Improvements And Global Innovations
Close Stay Updated on Environmental Improvements And Global Innovations
Close Reset password
your profile is 33% complete:
33%
Update profile Close
Close WhatsOrb Global Sustainability X-Change

For writers, influencers and dreamers who want to make the world a greener place.

WhatsOrb reaches monthly about 28.000 thousand visitors who want - like you - to make the world a greener place. Share your expertise and all can benefit.

Become an influencer and write and share sustainable news and innovations globally
Are you a writer or do you have ideas about sustainability which you want to share? Register and share your green knowledge and news. WhatsOrb offers you global exposure for your article.

If your article meets certain standards, you receive promotional gains like Facebook promotions and Google Ads advertising.

Agri & Gardening smart agriculture will be data  ai  driven | Upload General

Smart Agriculture Will Be Data (AI) Driven

by: Hans van der Broek
smart agriculture will be data  ai  driven | Upload

Scandal and headlines about data farming abound today, thanks to the alleged irresponsibility of the world’s biggest media platforms regarding consumer data. But new applications for AI in the industrial space prove that there’s a positive role yet for data farmers – although, perhaps not the kind you’re thinking of.

Drone with camera above cornfield

Data Farming For Agricultural Activities Could Be Very Beneficial

As businesspeople grapple with the challenges of minimal infrastructure and effective use of data, the hunt for valuable use cases for AI and IoT technology continues – and finding the right answers to their business problems could lead them to some unusual places.

Modern agriculture has long been technology-driven, but many of the challenges associated with farming in the 21st-century fall against the backdrop of growing food insecurity and a booming population outgrowing the rate of agricultural efficiency. By 2050, increases of 70% in global food production is the bare minimum required to feed the world’s population – a challenge even more severe if that population is to be fully nourished. One of the most promising approaches to solve this global issue is data-driven agriculture – and FarmBeats is an end-to-end IoT platform for agriculture, which puts AI and machine learning at its core. 

Recommended: Future Food: Would You Like To Eat Lab-Meat?

Intelligent Agriculture Is Sustainable Agriculture 

“If we could augment the farmer with insights, then this could drive techniques such as precision agriculture, which has been shown to reduce costs, improve yields, and help with sustainable agriculture,” argues Ranveer Chandra, Principal Researcher for FarmBeats.

Chandra headed a small project team of nine people, who were tasked with finding a way to boost yields and farm efficiency AI. That’s easier said than done, especially in a sector that remains mostly undigitized at production.

“The need for AI is significant in agriculture. However, in most agricultural settings – especially in the developing world – farmers don’t have the same IT expertise as someone working in an industrial IoT setting,” Chandra says. “Hence, we need to take additional steps and apply the AI techniques to provide actionable insights on top of the raw data and imagery that we collect from the farms. Based on these models, we can predict what is likely to happen in the future with some degree of confidence.” 

Recommended: Agriculture And Farming: Digital Tech Rules

Faced with little to no Internet coverage out on rural farms, Chandra’s team was challenged to develop low-cost connectivity solutions on which IoT sensors and AI hardware could operate. “We designed a system that used new technologies, such as TV white spaces, to gather data from the farms at a meager cost,” Chandra says. “This technology allows several Mbps connection over a few miles, which we can use to collect data not only from sensors but also from drones and cameras.” 

An Effective Case Study For AI And The IoT 

By applying machine vision algorithms to drone footage, FarmBeats can provide farmers with a digital heatmap of crop health and ground moisture areal photo heat red, green
Photo by VeryDrone

The result is an incredibly sophisticated Industrial Internet of Things (IIoT) solution that provides farmers with real-time data, insights, and actionable recommendations using AI and sensor technology. Ground sensors measure inputs such as soil moisture and nutrients; temperature and humidity are monitored in food storage and livestock shelters; while drones are used to help farmers map their fields, monitor crop canopy remotely, and check for anomalies.

“IoT is a way to capture enormous amounts of data that was previously just not available to us. However, this deluge of data can be hard to parse. The key challenge here is how to transform data from IoT systems and satellites into actionable insights, and this is really where AI and machine learning come in,” Chandra explains. 

Corn, graphic, blue sky
Photo by GeoSpatialWorld

Farm Beats uses AI techniques to fuse aerial imagery from drones with ground sensor data, while also leveraging deep learning and machine vision on video streams to identify pests, diseases, and nutritional deficiencies in crops. Here, edge computing became necessary to overcome any connectivity barriers of working in the cloud.  
A PC running Microsoft Azure IoT Edge on Windows 10 uses computer vision algorithms to stitch together drone images into a panoramic print, perform machine learning on images from drones and cameras, and is also able to run offline – syncing data to the cloud so that the farmer can access the data remotely.  



                                                        How Singapore Farms Use Artificial Intelligence

Learning From FarmBeats: AI And IoT For Industry 

Chandra believes that the FarmBeats system is a unique showcase for how IoT and AI can be used in a challenging scenario to solve some of the world’s hardest problems. The core principles of connectivity, IoT, and AI at the edge involve innovations that he believes can help drive the digital transformation of several other challenging verticals, including mining, construction, and forestry. So how can legacy businesses look to start implementing AI and the Industrial IoT into their industrial processes? 

“Never start with the technology angle, i.e., asking how you can use AI and IoT,” Chandra argues. “Go back to the drawing board, think about your business processes and challenges, and identify areas of improvement – and don’t have your technology teams in the room while you do this. They’re going to hate me for saying that, but we tend to limit ourselves within the boundaries of existing technologies. There’s no silver bullet – you need first to identify your business challenges and future aspirations in a technology-agnostic way.”

“Secondly, get some data on Azure – data is the new oil, and cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions, and less than 1% of unstructured data is analyzed or used at all. Data consolidation, cleansing, standardization would be the right starting point – we call this building the data infrastructure for digital transformation. The next steps would be providing a data governance platform, where we provide the right search mechanisms to make the data findable, monitor the usage and store in immutable ledgers, build a billing mechanism where you can charge internal and external users, build a secure data-sharing mechanism with external untrusted parties, and finally, develop a security and privacy platform. This is what we at MS call a Trusted Data Platform.”                      

From there, businesses should look at turning this data into actionable information for use by AI. Here, MS recommends tapping into your organization’s unique IP, where you can apply in-depth knowledge optimization of business processes to developing artificial intelligence assets. “Providing connected by utilizing the digital feedback loops possible with the introduction of IoT and cloud-based capabilities could bring new revenue streams. One of the fastest-growing areas we see is connected field services where this continuous dataflow, combined with internal information assets, makes new service models possible – leading to higher margins with special service contracts.”

Before you go!

Recommended: New Foodscape Alternatives Gets Lots Of Attention In The Netherlands

Did you find this an interesting article or do you have a question or remark? Leave a comment below. We try to respond the same day.

Like to write your article about growing your own food?
Click on 'Register' or push the button 'Write An Article' on the 'HomePage'.

Messange
You
Share this post

Hans van der Broek, founder

Founder and CEO of WhatsOrb, world traveller, entrepreneur and environmental activist. Hans has countless ideas and has set up several businesses in the Netherlands and abroad. He also has an opinion on everything and unlimited thoughts about how to create a better world. He likes hiking and has climbed numerous five-thousanders (mountain summits of at least 5000m or 16,404 feet in elevation)

 

Hans van der Broek, founder

Founder and CEO of WhatsOrb, world traveller, entrepreneur and environmental activist. Hans has countless ideas and has set up several businesses in the Netherlands and abroad. He also has an opinion on everything and unlimited thoughts about how to create a better world. He likes hiking and has climbed numerous five-thousanders (mountain summits of at least 5000m or 16,404 feet in elevation)

 

Stay Updated on Environmental Improvements And Global Innovations
SIGN UP FOR MONTHLY TIPS & TRICKS
More like this:

Smart Agriculture Will Be Data (AI) Driven

Scandal and headlines about data farming abound today, thanks to the alleged irresponsibility of the world’s biggest media platforms regarding consumer data. But new applications for AI in the industrial space prove that there’s a positive role yet for data farmers – although, perhaps not the kind you’re thinking of. Data Farming For Agricultural Activities Could Be Very Beneficial As businesspeople grapple with the challenges of minimal infrastructure and effective use of data, the hunt for valuable use cases for AI and IoT technology continues – and finding the right answers to their business problems could lead them to some unusual places. Modern agriculture has long been technology-driven, but many of the challenges associated with farming in the 21st-century fall against the backdrop of growing food insecurity and a booming population outgrowing the rate of agricultural efficiency. By 2050, increases of 70% in global food production is the bare minimum required to feed the world’s population – a challenge even more severe if that population is to be fully nourished. One of the most promising approaches to solve this global issue is data-driven agriculture – and FarmBeats is an end-to-end IoT platform for agriculture, which puts AI and machine learning at its core.  Recommended:  Future Food: Would You Like To Eat Lab-Meat? Intelligent Agriculture Is Sustainable Agriculture  “If we could augment the farmer with insights, then this could drive techniques such as precision agriculture, which has been shown to reduce costs, improve yields, and help with sustainable agriculture,” argues Ranveer Chandra, Principal Researcher for FarmBeats. Chandra headed a small project team of nine people, who were tasked with finding a way to boost yields and farm efficiency AI. That’s easier said than done, especially in a sector that remains mostly undigitized at production. “The need for AI is significant in agriculture. However, in most agricultural settings – especially in the developing world – farmers don’t have the same IT expertise as someone working in an industrial IoT setting,” Chandra says. “Hence, we need to take additional steps and apply the AI techniques to provide actionable insights on top of the raw data and imagery that we collect from the farms. Based on these models, we can predict what is likely to happen in the future with some degree of confidence.”  Recommended: Agriculture And Farming: Digital Tech Rules Faced with little to no Internet coverage out on rural farms, Chandra’s team was challenged to develop low-cost connectivity solutions on which IoT sensors and AI hardware could operate. “We designed a system that used new technologies, such as TV white spaces, to gather data from the farms at a meager cost,” Chandra says. “This technology allows several Mbps connection over a few miles, which we can use to collect data not only from sensors but also from drones and cameras.”  An Effective Case Study For AI And The IoT  By applying machine vision algorithms to drone footage, FarmBeats can provide farmers with a digital heatmap of crop health and ground moisture  Photo by VeryDrone The result is an incredibly sophisticated Industrial Internet of Things (IIoT) solution that provides farmers with real-time data, insights, and actionable recommendations using AI and sensor technology. Ground sensors measure inputs such as soil moisture and nutrients; temperature and humidity are monitored in food storage and livestock shelters; while drones are used to help farmers map their fields, monitor crop canopy remotely, and check for anomalies. “IoT is a way to capture enormous amounts of data that was previously just not available to us. However, this deluge of data can be hard to parse. The key challenge here is how to transform data from IoT systems and satellites into actionable insights, and this is really where AI and machine learning come in,” Chandra explains.  Photo by GeoSpatialWorld Farm Beats uses AI techniques to fuse aerial imagery from drones with ground sensor data, while also leveraging deep learning and machine vision on video streams to identify pests, diseases, and nutritional deficiencies in crops. Here, edge computing became necessary to overcome any connectivity barriers of working in the cloud.   A PC running Microsoft Azure IoT Edge on Windows 10 uses computer vision algorithms to stitch together drone images into a panoramic print, perform machine learning on images from drones and cameras, and is also able to run offline – syncing data to the cloud so that the farmer can access the data remotely.   {youtube}                                                         How Singapore Farms Use Artificial Intelligence Learning From FarmBeats: AI And IoT For Industry  Chandra believes that the FarmBeats system is a unique showcase for how IoT and AI can be used in a challenging scenario to solve some of the world’s hardest problems. The core principles of connectivity, IoT, and AI at the edge involve innovations that he believes can help drive the digital transformation of several other challenging verticals, including mining, construction, and forestry. So how can legacy businesses look to start implementing AI and the Industrial IoT into their industrial processes?  “Never start with the technology angle, i.e., asking how you can use AI and IoT,” Chandra argues. “Go back to the drawing board, think about your business processes and challenges, and identify areas of improvement – and don’t have your technology teams in the room while you do this. They’re going to hate me for saying that, but we tend to limit ourselves within the boundaries of existing technologies. There’s no silver bullet – you need first to identify your business challenges and future aspirations in a technology-agnostic way.” “Secondly, get some data on Azure – data is the new oil, and cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions, and less than 1% of unstructured data is analyzed or used at all. Data consolidation, cleansing, standardization would be the right starting point – we call this building the data infrastructure for digital transformation. The next steps would be providing a data governance platform, where we provide the right search mechanisms to make the data findable, monitor the usage and store in immutable ledgers, build a billing mechanism where you can charge internal and external users, build a secure data-sharing mechanism with external untrusted parties, and finally, develop a security and privacy platform. This is what we at MS call a Trusted Data Platform.”                        From there, businesses should look at turning this data into actionable information for use by AI. Here, MS recommends tapping into your organization’s unique IP, where you can apply in-depth knowledge optimization of business processes to developing artificial intelligence assets. “Providing connected by utilizing the digital feedback loops possible with the introduction of IoT and cloud-based capabilities could bring new revenue streams. One of the fastest-growing areas we see is connected field services where this continuous dataflow, combined with internal information assets, makes new service models possible – leading to higher margins with special service contracts.” Before you go! Recommended:  New Foodscape Alternatives Gets Lots Of Attention In The Netherlands Did you find this an interesting article or do you have a question or remark? Leave a comment below. We try to respond the same day. Like to write your article about growing your own food? Click on  'Register'  or push the button 'Write An Article' on the  'HomePage'.
Stay Updated on Environmental Improvements And Global Innovations