The integration of AI and the IoT with conventional farming practices provide an opportunity to bring agriculture into the digital age...
Information is money. And for farmers, information is often the deciding factor between a good and a bad harvest. As artificial intelligence (AI) and the internet of things (IoT) increasingly become mainstays of various industries, it’s fitting that one of humanity’s oldest professions, farming, would be poised for a 21st-century makeover.
Not surprisingly, the agricultural technology market is exploding. A recent market research report predicted that the AI agriculture market would see a compounded annual growth rate of 28.38% between 2019 and 2024.
With the farm labor supply shrinking among other problems, this reimaging could not be more timely. Today, agribusinesses are looking towards AI and the IoT to make their work more efficient and sustainable.
By using the IoT to gather data on their crops and then processing that data with technologies like machine learning, agribusinesses are able to better monitor the status of their crops and receive recommendations that can help them reduce pollution and pesticide use. Not only are these technologies helping to make farming more sustainable, they’re making it more productive and profitable as well.
Monitoring crops with the IoT
Until now, farmers had primarily gathered information about their crops manually during their work hours. This posed two significant problems. For one, there’s generally more room for error when doing something by hand. Secondly, manual monitoring can only be performed in intervals, lest the farmer spend their entire day checking in on the crops and shirking the rest of their work.
Figure 1. Until now, farmers had to gather information about their crops manually. (Source: SigFox)
The IoT provides a potential solution to both of these issues. By connecting monitoring devices and sending the data back to a central hub that the farmer can access, agribusinesses can monitor their crops in realtime and with better accuracy. Farmers can receive alerts immediately when action is required instead of remaining unaware until their next check.
John Deere has already begun implementing some of these technologies. It’s working on connecting its tractors to the internet so that they can gather data about crop yields. Additionally, the company is attempting to develop self-driving tractors, meaning data collection and a significant portion of farm work could be completely automated. This is a welcome change during a time when we are facing a major issue of farm labor shortages across the world.
But smart-farming tech has the potential to go beyond individual farms with the help of Software-as-a-Service cloud services. The Rwandan Ministry of Agriculture and Animal Resources recently implemented a smart-farming system built by N-Frnds, a cloud-based digital distribution platform operating on an SaaS cloud-based model. The system provides information to Rwandan farmers by giving them access to a searchable knowledge-base and sending them push notifications, among several other features.
Eventually, as the adoption of IoT farming tech increases, pairing it with SaaS distribution services has the potential to greatly improve farming efficiency on a nationwide or even international scale. This is because the SaaS model offers a number of important advantages over traditional IT infrastructure, such as greater accessibility, higher cost savings, and ease of scalability.
Specifically, SaaS and IoT hardware can be employed together to capture data and give insights into how operations on a farm should be managed. This includes information on crop patterns, weather cycles, harvesting, and soil quality to name a few examples.
All of this data will then be stored in the cloud to be neatly organized and always accessible, thus enabling field operations to be monitored from anywhere and at any time. This is also vital information to have for actually running a farm-based business as well. Cloud-based accounting solutions, for instance, will ensure that financial operations are streamlined through collecting data in real-time, storing that data on remote servers, and automating financial processes.
Analyzing data and automating processes with AI
International spending on artificial intelligence is expected to hit $57.6 billion in 2021, so it should come as no surprise that companies are looking into how AI can work in tandem with more traditional farming practices. Currently, the focus is on two aspects of AI: drawing insights from data and automating processes.
While the IoT makes real time monitoring and widespread distribution of information possible, AI can improve productivity and efficiency by helping farmers wade through that data and make decisions.
Meanwhile, both AI and the IoT are often used in conjunction with virtually unlimited cloud-based servers, because such solutions are designed to shoulder some of the weight that would instead normally be relegated to multiple computers. What’s better, is that the cloud is intrinsically not reliant on only one system, and even if one computer or server crashes, the cloud won’t go down with it.
For example, computer vision and machine learning technologies may be able to help agribusinesses detect weeds around their crops. Farmers would connect field cameras to the internet, the cameras would send data to cloud storage, and an AI model would send notifications upon weed detection so farmers could take action. The same concept applies to disease detection, crop quality monitoring, yield prediction, etc.
However, AI may eventually make complete automation of farming systems possible. In the above weed-detection example, a farmer would still need to manually de-weed their crops upon receiving a notification from the AI system. But as specific AI technologies (like self-driving and computer vision recognition) improve, the system would be able to send out a piece of machinery to remove the weeds without any human involvement.
In addition, AI-controlled machinery could pick crops, automate irrigation systems, and automate monitoring systems. With the elimination of human errors, AI may be able to improve the sustainability, profitability, and productivity of agricultural businesses.
Case study: TalentCloud
One of the first successful implementations of smart-farming tech using AI and the IoT comes from the Chinese company TalentCloud. The premise is simple: farmer education improves productivity, but everyday farmers don’t always have access to education and the latest research. Additionally, farmers that don’t keep up to date with research may overuse pesticides and pollutants that contribute to climate change.
Figure 2. TalentCloud’s Agro-Brain system creates real-time data to provide farmers with customized crop management recommendations. (Source: Microsoft)
To fix this, TalentCloud created an Agro-Brain system built on Microsoft’s Azure platform that combines realtime data from the fields with the latest agricultural research to provide farmers with customized crop management recommendations such as on how to control pests and diseases, how to optimize growing conditions, and the life cycle of your crops.
TalentCloud uses IoT sensors to collect quantitative data on soil conditions, humidity, and air temperature and employs cameras to collect qualitative data. The data is sent to Microsoft Azure’s IoT hub, and it’s processed by Azure Machine Learning to train TalentCloud’s proprietary models.
Essentially, this gives farmers similar productivity benefits to farming education, but in a turnkey solution. It may also improve sustainability, which is important in today’s environmental crisis.
Modernizing an ancient practice
The integration of AI and the IoT with conventional farming practices provide an opportunity to bring agriculture into the digital age. With so many problems facing the industry, this is, by all means, a welcome change. As more companies enter the growing agro-tech space, the prospect of using AI and the IoT to create fully-automated farming solutions draws ever closer.
— Ludovic Rembert is a security analyst, researcher, and the founder of PrivacyCanada.net.