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IoT and Smart Agriculture

Continuing the Green Revolution

Innovations in the industrial Internet-of-Things (IoT) promise to change farming—they can increase production, lower costs, create sustainable solutions, and optimize usage of dwindling resources. With the implementation of the new technologies in this article, agriculture could potentially meet the demand for higher food production, even while addressing the challenges of environmental regulations and climate change.

  • How is the industrial IoT being used in agriculture?
    How can technology help the agricultural industry be more sustainable?
    What are the latest technological developments in farming?
  • Raspberry Pi
  • Arduino
  • ESP32
  • STEMinds Eduponics Mini Kit
  • Eduponics mini board
  • soil moisture sensor
  • water quality sensors
  • FarmCommand
  • Soil Scout soil sensor system
  • Dragino LLMS01 LoRaWan Leaf Moisture Sensor
  • air flow sensor
  • Sentera ag drones and sensors
  • electro-chemical sensors
  • SMART nanosensors
  • acoustic sensors
  • thermography sensors
  • Trimble
  • BX992 dual-antenna receiver enclosure BD992-INS
  • John Deere autonomous tractor
  • Agrobot bug vacuum
  • LiDAR sensors
  • Spot robot from Boston Dynamics
  • Annie from Corteva Agriscience
  • Ted from Naio Technologies
  • Eve from Ripe Robotics
  • Dogtooth Technologies soft fruit picking robots

When thinking of IoT and smart gadgets, their applications in farming aren’t usually the first thing that comes to mind. After all, growing plants seems relatively low-tech: plant a seed, water it, give it nutrients, protect it from pests, and harvest it. But ask anyone who has done it and they will tell you it is anything but simple. Growing crops is hard work—it’s labor-intensive, and you are at the mercy of an often merciless Mother Nature.

Farming to produce the high volume of crops needed to support the world’s expanding population compounds the difficulty. Months or even years of work can be wiped out with a single storm, heat wave, drought, insect infestation, etc. Nature isn’t the only challenge, either—environmental regulations, transportation logistics, and labor shortages all affect the eventual harvest output.

The agricultural industry has continually developed and embraced innovations to counter these challenges. Mechanical innovations such as tractors, seed planters, and harvesters started mass food production. IoT and smart inventions just might be what keeps it going.

DATA AND MORE DATA

As network providers broaden the reach of reliable IoT to remote areas through satellites and expanded cellular networks, sensor technology takes on new importance. The value of sensor data increases exponentially when made available in real-time. Thus, in response to the ever-growing IoT, more advanced sensors are being developed and put to use in the agricultural industry.

A simple setup might consist of an individual sensor sending a notification to a smart phone—for example, a soil moisture sensor can alert you when the soil moisture changes. This can be done on a small scale by integrating a moisture sensor to a Raspberry Pi, Arduino, or ESP32 board, each of which comes with large communities and how-to resources that make implementation even easier. There are also several kits available, such as the STEMinds Eduponics Mini Kit (Figure 1). The kit contains the Eduponics mini board, a soil-moisture sensor, a water quality sensor, and other parts to help you start your own smart garden or IoT-powered hydroponics solution. The kit comes with a hackable smart agriculture mobile app.

Figure 1
The STEMinds Eduponics Mini smart agriculture IoT kit gets you started building your own smart garden or IoT-powered hydroponics solution. The kit is powered by an ESP32 module, with plenty of built-in sensors that work together.
Figure 1
The STEMinds Eduponics Mini smart agriculture IoT kit gets you started building your own smart garden or IoT-powered hydroponics solution. The kit is powered by an ESP32 module, with plenty of built-in sensors that work together.

Outside the home garden, irrigation requires a greater level of monitoring. Several companies specialize in irrigation management, deploying a mix of soil sensors, weather monitoring and drone surveillance monitoring. The level of complexity of these systems vary depending on the amount of data that is needed.

As more and different types of sensors are used, the resulting volume of data rapidly increases, as does the accompanying need for software to make sense of it all. An example of this is the comprehensive digital platform FarmComand from FarmersEdge (Figure 2). The software comes with tools to manage field status, crop health, weather risks, and equipment assets. It incorporates satellite imagery with rapid data processing to identify changes in crops and weather via cloud and shadow analysis. It provides timely notifications on real-time conditions, 48-hour forecasts, 10-day forecasts, and even historical weather data for operational decision support.

Figure 2
FarmCommand makes discovering and sharing insights faster. You can manage all farms, fields, equipment and other assets in one view. The software provides information on all aspects of crop status, crop health, risk exposure and equipment activity to support effective planning and management.
Figure 2
FarmCommand makes discovering and sharing insights faster. You can manage all farms, fields, equipment and other assets in one view. The software provides information on all aspects of crop status, crop health, risk exposure and equipment activity to support effective planning and management.

These software packages also analyze trends in crop yield, pest problems, and weather to enable quicker, more informed decisions for daily and year-over-year farm management.

BEYOND MOISTURE SENSORS

Soil moisture sensors seem to be the most widely used sensor in agriculture at the moment, and with good reason. They provide the measurements needed for comprehensive irrigation management and water conservation, and they allow you to precisely target the ideal moisture level for plant growth and yield. Basic soil moisture sensors work with two probes that act together as a variable resistor. Soil Scout Ltd. makes a version that is fully buried (up to 6 feet), with multiple sensors distributed across an area for a full underground weather map (Figure 3). With so many available options, there is a cost-effective solution for most situations.

Figure 3
Soil Scout is a soil sensor system that allows you to place any number of fully buried wireless sensors across a chosen area to get a complete, real-time, underground weather map at your fingertips.
Figure 3
Soil Scout is a soil sensor system that allows you to place any number of fully buried wireless sensors across a chosen area to get a complete, real-time, underground weather map at your fingertips.

But their domination in IoT farming is changing, as technological improvements and innovations are beginning to address other areas of crop production. Consider the Dragino LLMS01 LoRaWan Leaf Moisture Sensor (Figure 4). Its leaf-shaped probe senses the dielectric constant created by moisture on the surface, and it measures leaf moisture and temperature using the frequency domain reflectometry (FDR) method.

Figure 4
The Dragino LLMS01 LoRaWAN Leaf Moisture Sensor is designed to measure and analyze leaf moisture and temperature.
Figure 4
The Dragino LLMS01 LoRaWAN Leaf Moisture Sensor is designed to measure and analyze leaf moisture and temperature.

Air flow sensors are used in both indoor and outdoor growing areas, albeit in very different ways. In the field they record the gaseous substances in the soil after cultivation or irrigation to determine the pump pressure required for soil aeration. Or they can measure soil compaction, moisture capacity and other properties that help determine which crops to plant in specific areas. Greenhouses use IoT-connected airflow sensors in automated ventilation systems to send alerts when the ventilation stops or has problems, improving response time and limiting damage to crops.

Optical sensors monitor everything from plant health to growth rates. They can provide early disease detection, and check ripeness levels for ideal harvest times. When incorporated into drone surveillance, vast areas can be monitored with more accuracy and less manpower, and analytics on aerial imagery enable optimization of time and resource management. Companies such as Sentera in Minnesota have developed an extensive software package to capture, monitor, and analyze optical data (Figure 5), so as to reduce both guess work and decision time. With the data it provides you can target areas in need of fertilizer, fungicides, or pesticides with precision, reducing overall usage and problems associated with overapplication.

Figure 5
Sentera ag drones and sensors capture detailed analytics to provide the data supported by high-quality images needed to make timely decisions and optimize performance.
Figure 5
Sentera ag drones and sensors capture detailed analytics to provide the data supported by high-quality images needed to make timely decisions and optimize performance.

Field equipment and drones with location sensors are improving cultivable land. By analyzing 3-dimensional imagery data of plots, problem areas can be addressed and maximized for production. Tracking equipment operating speeds provides insights on scheduling and reduces overall downtime.

Another area of development and improvements in technology is that of electro-chemical sensors. Traditionally, electro-chemical sensors have been used to measure and analyze soil quality. They monitor nitrogen, phosphorous, iron, calcium, amd sodium levels to aid in the application of fertilizers and determination of which crops to plant. Singapore-MIT Alliance for Research and Technology (SMART) have developed nanosensors that rapidly detect synthetic auxin plant hormones (Figure 6). SMART’s nano solution provides a less tedious and safer approach to test plants’ responses to compounds such as herbicides. Further, it allows for the testing of plant information in real-time without harm to the plant. Farmers can thus determine the vulnerability of specific plants to herbicides without having to spend days monitoring crops or weeds. The research team’s goal is “to fundamentally change how plant biosynthetic pathways are discovered, monitored, engineered, and ultimately translated to meet the global demand for food and nutrients.”

Figure 6
Nanosensors developed by the Singapore-MIT Alliance for Research and Technology (SMART) can detect synthetic auxin plant hormones NAA and 2,4-D.
Figure 6
Nanosensors developed by the Singapore-MIT Alliance for Research and Technology (SMART) can detect synthetic auxin plant hormones NAA and 2,4-D.

Meanwhile, developments in acoustic sensors are addressing pest control. Acoustic sensors can monitor insect noise levels and provide alerts when pests have reached a level that requires intervention.

Early disease detection is also a high priority in crop protection. Thermography sensors capture infrared radiation emitted from the plant’s surface to measure and monitor changes in plant temperature. According to Agrivi Farm Management, a pathogen infection will increase a plant’s surface temperature, so thermography sensors can detect disease before it appears to the human eye (Figure 7).

Figure 7
The left image shows the thermographic method that is used to detect disease and pests, in this case on wheat. As seen in the right image, florescence can also detect pathogens on a leaf.
Figure 7
The left image shows the thermographic method that is used to detect disease and pests, in this case on wheat. As seen in the right image, florescence can also detect pathogens on a leaf.

Remedying pest, disease and nutrient problems is also improved with sensors. Proximity sensors detect the space between crops to allow for better coverage and target delivery with less waste.

Sensor value doesn’t even end at harvest. Biosensors detect pathogens and pesticide residue on harvested plants. Once again this early detection is crucial, as you can significantly limit contamination and address the problem before a large amount of produce is wasted.

AUTONOMOUS TECHNOLOGY

Satellite imagery, drones, and GPS tracking can monitor the use and productivity of equipment assets. And now, those equipment assets are increasingly automated. With precision location and motion sensing, autonomous agriculture robots are preparing fields, planting seeds, weeding, providing pest control, and harvesting crops.

Global Navigation Satellite Systems (GNSS) solutions such as the Trimble BX992 dual-antenna receiver enclosure, powered by the BD992-INS (Figure 8), makes autonomous tractor technology feasible. Autonomous tractors minimize use of labor and thus reduce accidents, and they increase production—indeed, robot tractors can provide 24-hour operations. Obstacle detection and path-generating algorithms allow autonomous equipment to easily work in conjunction with manned machinery.

Figure 8
The BD992-INS is a dual-antenna receiver with an integrated inertial navigation system.
Figure 8
The BD992-INS is a dual-antenna receiver with an integrated inertial navigation system.

At CES 2022, John Deere revealed a fully autonomous tractor that is ready for large scale production. The tractor can calculate distance and has 360-degree obstacle-detection thanks to its six pairs of stereo cameras. The machine uses a deep neural network to rapidly process each image passed through it and determine if the tractor needs to stop due to an obstacle. It also checks its position, within less than an inch of accuracy, relative to a geofence to make sure it stays within its boundaries of operation. Configuration is simple—farmers simply transport the machine to a field, set it up for autonomous operation, and it gets to work. They can then use the John Deere Operations Center Mobile to remotely start the machine, and can monitor its status from their mobile device while they attend to other tasks.

John Deere has also purchased Bear Flag Robotics in an endeavor to develop the software necessary to retrofit much of its existing equipment with autonomous technology.

ROBOTICS IN THE FIELD

Autonomous tractors are essentially the same as traditional equipment, but without the need for a driver. They remain oblivious to most of their surrounding environment while performing the same specific jobs as manned tractors. However, this is rapidly changing with the introduction of specialized robotics that are able to incorporate sensor data and adjust tasks accordingly.

Agrobot’s Bug Vacuum is an autonomous vacuum robot for pest control (Figure 9). It comes equipped with LiDAR sensors for identification of obstacles, people, and guidance references. With this detection technology it can navigate along bed furrows, cross roads, and turn around. And as the name suggests, it provides a chemical-free pest control via its double-fan vacuum system with precision fan height over the full bed width.

Figure 9
Autonomous Bug Vaccum by Agrobot
Figure 9
Autonomous Bug Vaccum by Agrobot

Corteva Agriscience is using Boston Dynamic’s agile Spot robot—which has been named Anatoly, or Annie—along with GPS guidance from Trimble to walk crop rows. Annie has 360-degree detailed imagery of crops, and provides data superior to that which can be collected manually from field perimeters and even that of aerial drones. This mid-field, individual plant data improves the evaluation of new hybrid plant performance deep within the plot when competing with neighboring plants.

Naio Technologies makes a vineyard tending robot, named Ted (Figure 10). Ted performs precise mechanical weeding, and is thus an eco-conscious alternative to the use of herbicides. Its autonomous operation makes this otherwise time-consuming chore work with your designated schedule. Naio Technologies also makes a version called Dino that handles weeding of traditional crops.

Figure 10
Ted, the vineyard tending robot by Naio Technolgies
Figure 10
Ted, the vineyard tending robot by Naio Technolgies

Ripe Robotics’ latest generation robot, named Eve (Figure 11), is a commercial-scale harvesting robot currently in the trial phase. It’s being tested on apples, oranges, and plums. Eve is equipped with advanced artificial intelligence and is connected to the cloud for easy monitoring. It uses a soft suction system to pick the fruit, then places the fruit in a bin which it drops off at the end of the row. Eve’s suction system is especially gentle with the fruit, which results in a higher quality, higher value produce, and reduces the amount of manual labor needed.

Figure 11
Eve, a fruit-harvesting robot by Ripe Robotics
Figure 11
Eve, a fruit-harvesting robot by Ripe Robotics

Dogtooth Technologies is developing soft fruit picking robots with an initial focus on strawberries (Figure 12). Locating the ripe strawberries using 3D vision, the machines grasp the fruit, snip it from the plant, and then place it in the bin, all with a human picker’s efficiency.

Figure 12
Dogtooth Technologies' strawberry picking robot
Figure 12
Dogtooth Technologies’ strawberry picking robot

These are but a few of the products being developed as a greener solution to the world’s increasing need for agricultural production. An ever-growing human population guarantees that the problems facing agriculture will remain complex, but these evolutionary leaps in agricultural tools, incorporating the latest cutting edge smart and IoT technology, are an encouraging sign that we will be able to meet and conquer those challenges. 

RESOURCES
Agrobot | www.agrobot.com
Corteva Agriscience | www.corteva.com
Dogtooth Technologies | dogtooth.tech
Dragino | www.dragino.com
Farmers Edge | www.farmersedge.ca
Naio Technologies | www.naio-technologies.com
Ripe Robotics | www.riperobotics.com
Sentera | www.sentera.com
Soil Scout Ltd. | www.soilscout.com
Trimble | www.trimble.com

PUBLISHED IN CIRCUIT CELLAR MAGAZINE • NOVEMBER 2022 #388 – Get a PDF of the issue

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IoT and Smart Agriculture

by Circuit Cellar Staff time to read: 10 min