Market Research Report
IoT in Agriculture Market by Technology, Automation (Robots, Drones, and Smart Equipment), Sensor Types, Hardware, Software and Solutions 2022 - 2027
|IoT in Agriculture Market by Technology, Automation (Robots, Drones, and Smart Equipment), Sensor Types, Hardware, Software and Solutions 2022 - 2027|
Published: May 9, 2022
Content info: 189 Pages
Delivery time: 1-2 business days
This report assesses the technologies, companies, and solutions for IoT in agriculture. The report evaluates the overall marketplace and provides forecasts for sensors (and other devices), services, solutions, and data analytics globally, and regionally for the period 2022 to 2027. Forecasts include precision agriculture, indoor farming, livestock, and fisheries.
Forecasts cover IoT in agriculture solutions globally and regionally including: Intelligent Farm Equipment, Smart Sensor Systems, Intelligent Drones, Smart Farm Robots, and Software. Within the Smart Sensor area, the report forecasts the following: Sensors for Detecting Physical Properties, Sensors for Chemical Analysis and Applications, Sensors for General Monitoring, Sensors for Quality, Sensors for Autonomous Agriculture, and others.
There is currently an acute need for greater agricultural efficiency and effectiveness in the week of the recent pandemic. Many agricultural commodities such as corn, soy, and cotton are in backwardation as of the publication of this report, which means that the current price of an underlying asset is higher than prices trading in the futures market. This is atypical for commodities as inflation generally tends to make their price increase over time.
However, recent material and supply chain related shortages, coupled with an uptick in economic activity, has led to unbalanced supply and demand dynamics. This is reflected in the Bloomberg Agriculture Spot Index, which measures the price movements of agricultural commodities, which has risen from 227.38 on May 15th, 2020 to a high of 386.47 on April 23rd, 2021, representing a 70% increase during that time period.
While the aforementioned commodity price and supply challenges represent a more near-term acute issue, there remain longer-term structural market drivers for improvements in agricultural technologies. As the world population grows, so does the demand for food. The UN estimates that Earth will need to produce 70% more food by 2050 to support these growing populations. Complicating matters, natural resources are slowly being depleted and usable agricultural land is shrinking.
There is an ever-increasing need for intelligent and highly scalable agriculture solutions. Increasingly, the agriculture business is becoming controlled by companies that are not conventional agriculture experts. The publisher sees a shift from conventional agriculture to farm management. With this shift, software developers and predictive data analytics companies will take control of end-to-end agricultural operations.
Agriculture has transformed in the last few decades from small to medium farming operations to highly industrialized, commercial farming that is concentrated among a few large corporations. However, as various Internet of Things (IoT) technologies mature beyond the R&D phase and go into general production, costs for everything from drones/UAVs to sensors will continually decrease, making connected agriculture more accessible to smaller farms and third world countries.
With this agricultural transformation, farming operations are increasingly a highly mechanized and computer-driven operation. This allows corporations to treat agriculture like manufacturing in the sense that measurements, data, and control is very important to manage costs, maximize yields, and boost profits. This shift in managing agricultural operations will bring various benefits to farming and livestock management, including enhanced crop quality and quantity, improved use of resources and farm equipment, real-time monitoring of farms, animals, and machines, automated irrigation systems, fertilizer spraying, and pest control.
The general term, AgriTech, represents the use of technology in agriculture, horticulture, and aquaculture for purposes of improving yield, efficiency, and profitability. The commercial agriculture industry is rapidly becoming one of the most IoT data-driven markets. With the emergence of M2M, IoT, and advanced data analytics technologies, data is becoming available that was previously uncollectible. The application of various AgriTech analytics tools and methodologies, such as predictive analytics will provide substantial enhancements to agriculture operations.
IoT in Agriculture (IoTAg) represents a more specific use of technology wherein agricultural planning and operations becomes connected in ways previously impossible if it were not for advances in sensors, communications, data analytics, and other areas. Virtually every aspect of agriculture that can be automated, digitally planned, and managed will benefit from IoT technologies and solutions.
Accordingly, we see IoTAg fundamentally transforming the way agricultural operations and farms are managed, which will bring various benefits to farming, including enhanced crop quality and quantity; improved use of resources and farm equipment; real-time monitoring of farms, animals, and machines; and automated irrigation systems, fertilizer spraying, and pest control.
The implementation of IoTAg is intended to facilitate greater agricultural efficiency and effectiveness. Essentially, IoTAg solutions, coupled with artificial intelligence and a few other supporting technologies, enable smart agriculture. IoTAg solutions provide many intelligent agriculture benefits such as increase of yields, monitoring crops, automating operations, and reducing waste.
IoT technologies allow farmers and ranchers to enhance productivity. For example, if part of the irrigation system malfunctions, sensors can provide alerts, allowing the problem to be addressed in a timely fashion. These technologies also allow agricultural staff to view operational conditions from anywhere and make changes with real-time solutions.
Illustrative examples of smart agriculture solutions include the following:
One of the foundational elements of the IoTAg market are sensors, which may be used in a variety of different use cases and applications, such as precision agriculture scenarios in which moisture is carefully monitored to ensure that crops receive sufficient water with minimal human intervention. Examples of sensors in action include the following:
Another emerging element of IoTAg and smart agriculture in general is the use of aerial drones as UAVs may be used for a variety of purposes that minimize manual labor while improving the overall efficacy of farming, aquaculture and/or ranching operations such as detecting differences in heat signatures and use of robotics for planting, spraying, and harvesting.
Mapping farms using aerial drones and terrestrial robots is rapidly becoming table-stakes for connected agriculture. Agribusiness will also deploy drones/robots to obtain real-time data regarding many aspects of farming operations. This will be a combination of aerial and land perspectives/images captured using multi-spectrum cameras and sensors installed on agricultural drones/robots. Additional examples of UAVs and autonomous terrestrial robot use cases in smart agriculture include the following:
The implementation of combined AI and IoT solutions for agriculture will provide a substantial lift for both operational efficiency and effectiveness. These Artificial Intelligence of Things (AIoT) solutions will transform the interpretation and use of IoT data from a largely human-based activity to one that is primarily machine-oriented.
This will lead to fewer errors and savings in operational costs such as data analytics visualization for the sake of human viewing, interpretation, and decision-making. For example, the Artificial Intelligence of Things (AIoT) Solutions: AIoT Market by Application, Service, and Industry Vertical 2022 - 2027 report Identifies a $2.38 billion global opportunity for AIoT solutions in agricultural monitoring alone.