Kateryna describes how remote monitoring using satellites is revolutionizing how farmers combat crop infestations and diseases through the analysis of high-resolution images and advanced data analytics. I’ll let her explain the rest. As always your comments are welcomed.
Pest and disease management is crucial in agriculture, protecting crops from seen and unseen threats that devastate harvests and livelihoods. Traditionally, farmers have relied on their experience and natural remedies. Today, geospatial data availability and analytics have transformed agricultural practices using tools like satellite imagery, drones, and a form of artificial intelligence called machine learning to detect pest and disease crop infestations allowing for early and precise interventions. Tools include smart sensors deployed to monitor soil and plant health in real time and data analytics to offer insights into effective pest control. Biotechnological advances using genetic modifications are producing disease-resistant crops and reducing the need for chemical pesticides.
Although satellites may seem far away and incapable of spotting something as small as an insect, other pests and early stages of disease, they are proving to be quite effective in these tasks.
Two technological advances, one an Earth observation instrument and the second, EOS Data Analytics when combined represent the current state-of-the-art to help farms monitor different crop and soil parameters from advanced remote satellite imaging and data collection.
Traditional methods while rooted in time-honoured wisdom and experience face many limitations and challenges in today’s complex agricultural landscape.
One of the primary issues is the unpredictability of pest and disease outbreaks. Growers often rely on manual inspections and past experiences, which can be subjective and inconsistent. This reactive approach means that infestations are often identified only after they have caused damage that is already impossible to avert completely.
Another limitation is the reliance on chemical inputs. Although they can be beneficial in the short term, these substances cause detrimental effects on the environment, including soil degradation, water contamination, and harm to beneficial insects and wildlife.
Another issue to consider is pest resistance to chemical treatments over time, which means even stronger hence more harmful analogues need to be applied with negative environmental consequences.
The labour-intensive nature of manual monitoring and intervention requires substantial time and effort which can be particularly burdensome for small-scale farm operations. Furthermore, these methods often lack precision and lead to the over-application or under-application of treatments.
Climate change also doesn’t work in our favour here. Warmer temperatures and changing precipitation patterns create better conditions for pests and diseases to thrive often in areas where previously they were of no concern.
Given these limitations, the need for innovative solutions becomes clear. Precision agriculture tools such as satellite imagery provide farms with real-time data and early warnings of potential threats. Machine learning and data analytics enable more accurate predictions and tailored interventions reducing reliance on chemicals.
Satellite technology with its far-reaching eyes offers growers a way to manage fields in ways that no other methods can. By analyzing high-resolution images farmers can see early indications of changes in vegetation patterns, colour and growth no matter the weather conditions and distance. The application of predictive analytics along with these images gives farmers the tools needed to make smart decisions.
Satellites identify signs of disease or pest outbreaks utilizing vegetation indices like NDVI, NDMI, ReCl, and NDRE. These track the state of health of plants at different stages of growth.
EOSDA Crop Monitoring from EOS Data Analytics leverages these indices to provide real-time insights into crop health. A crop monitoring system can assign scouting tasks to farmers based on satellite data. The system can confirm crop damage, identify specific diseases (biotic or abiotic), and communicate detailed descriptions to agronomists and farmers for timely interventions.
Using GPS coordinates a crop monitoring system can precisely target an area for intervention rather than address the problem as field-wide.
The predictive analytics in the Disease Risk feature of EOSDA Crop Monitoring spots nascent pest infestations and diseases before they become a larger problem. The analytics can assess risk levels across different zones allowing farmers to prioritize and address the most endangered crops.
A Data Manager feature provides data analysis comparisons to make accurate yield assessments during harvesting. It produces maps that highlight areas of varying productivity and can help identify and manage low-productivity plots resulting from biotic pathogens.
EOSDA Crop Monitoring also offers data on soil moisture levels, temperature extremes, and weather conditions to ensure that farmers can manage both biotic and abiotic factors to safeguard their yields.
Using real-time remote sensing data gives farmers the means to make smarter decisions when using pesticides to target affected crops. Detailed satellite imagery can identify exactly where these infestations are happening allowing for precision rather than blanket application of chemicals and pesticides. Cutting down on chemical and pesticide use lowers costs and reduces environmental risk.
Satellite tracking of soil health and moisture levels means farmers can use less of both more effectively ensuring these resources get applied where needed most to promote sustainable farming practices.
Expect satellite technology to continue to play a critical role in precision agriculture. Biotechnological innovation is also paving the way for safer pest control methods. A good example is the Wolbachia project which breeds mosquitoes that can’t transmit diseases. We are already seeing the use of biological weather stations to identify and track pest species and diseases providing valuable data to predict and manage outbreaks. At the same as in the Wolbachia project, we are using the science of genetic modification to deal with these invasive threats.
The Internet of Things is being deployed today to support agriculture. It is revolutionizing pest monitoring using millions of sensors to collect environmental, optical, and sound data. This real-time information enables early detection and facilitates targeted interventions.
Big Data analytics combine information from various sources including pest biology, weather patterns, and control activities to optimize strategies and provide holistic insights into pest dynamics. AI is enhancing analytics tools by adding the capacity to offer recommendations or even apply autonomous decision-making to a perceived threat.
Robotics and drones are bringing precision to pest management enabling their detection and control regardless of location. Drones and field robots can precisely target the use of pesticides or biological agents minimizing chemical use and environmental harm, and they are only getting more sophisticated and smarter.
Augmented reality and virtual reality are playing a part as well. Virtual reality can be used to train farmers. Augmented reality can give farmers a heads-up view of all the data and imaging information to help make decisions about where the problems lie and best practice solutions.
All of these advancements are creating more sustainable, efficient, and resilient agricultural practices that can meet the challenges of pest and disease management here in the 21st century.
]]>How? A global network of connectivity from drones, satellites, devices, and sensors are creating an Internet of Everything. Diamandis describes the four different levels in this evolution:
As always feel free to share your comments.
This is the coming age of microsatellite constellations brought to you by SpaceX and OneWeb.
OneWeb is working on almost 900 satellites, while SpaceX will be deploying over 12,000 mini-fridge-sized ones. Both have plans to create a global 5G Internet, but that represents only a fraction of the potential of what similar deployments will bring: microsatellite equipped with high-resolution cameras will extend the Internet of Things (IoT), providing a massive amount of data to help solve the world’s grand challenges.
As of August 2017, there were nearly 1,800 operational satellites in orbit. Of these, 742 were communications satellites, 596 were used for Earth observation, and 108 were used for navigation. And a massive increase in operational satellites is on the way at they become smaller and as launch costs plummet. Today private companies all over the world are building satellite technology. Chinese companies plan to place 60 commercial high-resolution Jilin 1 imaging satellites in orbit by 2020. Planet Labs, a disruptive company that uses milk-carton sized imaging satellites to help industries obtain game-changing data, is showcasing over 175 satellites in orbit that can image anywhere on the globe with up to 3.72-meter resolution. The company is also offering a specialized, targeted satellite option called SkySats. Thirteen of these can achieve up to 72-centimeter resolution. The SkySats can also capture video, which can be used to extrapolate 3D models. These satellites are built using the same technology Google deployed to capture crisp 3D-image views for its Google Maps.
Closer to Earth’s surface, a few hundred feet above our heads, we’re developing an extensive network of autonomous drones to collect valuable information for farmers, wind turbine surveyors, financial institutions, and many others. At this year’s Consumer Electronics Show in Las Vegas, the U.S. Department of Transportation announced that over 1 million drones were registered with the Federal Aviation Administration (FAA). By 2020, the FAA predicts 7 million drones will be flying in North American skies. While private and commercial drone flight rules remain moderately restrictive, this past October, the U.S. announced plans for the federal government to work with companies to start deploying large fleets of drones with more relaxed restrictions.
During 2017 drone data collection was invaluable for saving lives in natural disasters (hurricanes and wildfires), surveying damage and providing search and rescue operations with crucial footage of hard-to-reach locations. And as drones fly more and collect more data, a small fleet will easily generate up to 100 terabytes of data per day.
Intel predicts that the self-driving car industry will grow to $7 trillion by 2050. On the ground the arrival of these vehicles will represent a data collection revolution as they image and sample their surroundings all the time. Imagine millions of autonomous vehicles on streets, each packed with dozens of cameras, LiDAR and radar to help them navigate. Waymo, one of the leaders in developing this technology, uses three different LiDAR sensors, five radar sensors, and eight optical cameras. The sensor suites on a single car generate more than 4 terabytes of data per day. Just one technology, LiDAR, a laser detection system that bounces its light beams off surrounding objects can create up to 9 million data points per second according to market leader Velodyne.
Tesla, unlike Waymo, avoids using LiDAR altogether, and instead opts for an ultrasonic, radar and camera approach to autonomous vehicles. Here’s a description from Tesla about the sensors on each of their cars:
“Eight surround cameras provide 360 degrees of visibility around the car at up to 250 meters of range. Twelve updated ultrasonic sensors complement this vision, allowing for detection of both hard and soft objects at nearly twice the distance of the prior system. A forward-facing radar with enhanced processing provides additional data about the world on a redundant wavelength that is able to see through heavy rain, fog, dust and even the car ahead.”
The bottom line is this: when we enter the era of autonomous cars, there will never be a car, pedestrian, accident, or street-side pick-pocket that isn’t being imaged. These cars will record an extraordinary number of images in detail.
And then there is augmented reality. While the world now has more mobile phones than humans with smartphone numbers alone reaching 3.5 billion by 2021, we will soon see the emergence of augmented reality (AR) headsets. AR glasses will feature a multitude of forward-looking cameras that image everything at a sub-millimeter resolution as you travel through the day. And by the end of 2020 these AR glasses, and smartphones, plus medical wearables and smart dust will amount to 50 billion connected devices hosting more than a trillion sensors.
There has been an exponential growth in the number of sensors per connected device over the last decade. Sensors on phones have doubled every four years. This means expect to see 160 per mobile device by 2027, and a world jam-packed with nearly 100 trillion in total. And almost every one of these sensors will be accessible to artificial intelligence delivered over the Internet or in specific devices.
I can envision waking up in the morning, putting on my augmented reality contact lenses, and forgetting about them for the rest of the day while they record every conversation, every person crossing the street, and everything I look at. Drawing from this constant stream of observational data, I’ll be able to train my social graph and preferences into my AI using the collected data.
The bottom line is we are heading towards a future where you can know anything you want, any time you want, anywhere you want. In this future, it’s not “what you know,” but rather “the quality of the questions you ask” that will be most important.
If you are in the fashion business and want to know the average spectral color of women’s blouses on Madison Avenue this morning, ask your AI and it will gather the image data and provide an accurate answer in seconds. Then you can ask the AI if any recent advertising campaign correlates to blouse colour choices.
Such abundance of data is what I call “Radical Transparency” and it leads to a few interesting conclusions, which will no doubt be the topic of a future posting.
In the world of IoT and 1oo trillion sensors privacy as we have known it will truly be a thing of the past.
And it will be harder and harder to do anything in secret without leaving a traceable digital record.
