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IoT in Agriculture Market by Technology, Automation (Robots, Drones, and Smart Equipment), Sensor Types, Hardware, Software and Solutions 2021 - 2026

  • ID: 5321662
  • Report
  • May 2021
  • Region: Global
  • 186 Pages
  • Mind Commerce
Integration of AI and IoT in Agriculture Market a Key Initiative for Leading Players

FEATURED COMPANIES

  • Accenture
  • Atos
  • Decisive Farming
  • FluxFarm Inc.
  • Monsanto (Bayer)
  • Semios Technologies
1h Free Analyst Time

Speak directly to the analyst to clarify any post sales queries you may have.

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 2021 to 2026. 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.

Select Report Findings:

  • The largest IoTAg application by revenue is monitoring, which will reach $6.99 billion globally by 2026
  • The fastest growing IoT solution area by revenue is automation, which will grow at a CAGR of 63.8% through 2026
  • The market for smart sensor systems will reach $2.48 billion globally by 2026, growing at a CAGR of 66.0% through 2026
  • The global market for agriculture drones will reach $1.12 billion by 2026 with an average price of $1,250 per unit for UAVs
  • Intelligent solutions for aquaculture operations will $980 million globally by 2026, which we see as a significantly underserved market
  • The implementation of combined AI and IoT solutions for agriculture will provide a substantial lift for both operational efficiency and effectiveness

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 labor shortages due to the COVID-19 pandemic, coupled with an uptick in economic activity associated with economies reopening after the initial pandemic shut-downs, has led to supply chain discontinuities and resulting unbalanced supply and demand dynamics. This is reflected in the Bloomberg Agriculture Spot Index, which measures the price movements of agricultural commodities, has risen from 227.38 on May 15th, 2020 to a recent high of 386.47 on April 23rd, 2021, representing a 70% increase in a little more than one year.

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:

  • Greenhouse Automation: With IoT and sensors, greenhouses can be almost entirely automated. Real-time data on greenhouse conditions, including temperature, lighting, soil condition, and humidity, can be examined and modified automatically. Users can input desired parameters, and automation systems adjust the ecosystem to match them.
  • Cattle Monitoring/Management: IoT sensors can be attached to animals to monitor and record their health. These solutions can collect data on livestock health, well-being, and location. Sensors can identify sick animals quickly, allowing farmers to separate them from the herd and reduce the spread of contagion. These sensors also save farmers cost in staffing expenses while making better use of their time.
  • Harvesting Robotics: Agribots are being used to harvest crops, helping to fill the void of workers. These bots can pick fruits and vegetables 24/7, using robotic arms and digital image processing to do so. Companies can control the quality of their products better by utilizing these bots, as they determine when to harvest based on programmed parameters.

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:

  • Optical Sensors: These sensors can be placed on vehicles, drones, or satellites, and use light to measure the properties of soil. Optical sensors measure various light frequencies in multiple light spectrums and provide farmers with insights to plant color data and soil reflectance.
  • Location Sensors: These sensors use signals from GPS satellites to determine precise location. Precision accuracy relies upon location sensors, as nearly all other functions are based on location.
  • Mechanical Sensors: Mechanical sensors determine soil compaction using a probe that records resistive force. This type of technology is already deployed on large tractors, used to gauge pulling requirements.
  • Electrochemical Sensors: These sensors provide farmers with pH and soil nutrient levels. Insights provide users with knowledge of soil conditions, allowing them to make changes accordingly.
  • Airflow Sensors: Airflow sensors measure the permeability of the air. These measurements can be taken either in motion or at specific locations. Soil properties can be determined with airflow sensors, including soil type, structure, moisture level, and compaction.
  • Dielectric Soil Moisture Sensors: These sensors measure moisture using the dielectric constant. an electrical property. This property changes depending on the concentration of moisture in the soil.
  • Accelerometer Sensors: These sensors detect variations in movement and vibration and are used for predictive maintenance. Accelerometer sensors are typically used on motors and moving components, such as tractors. Slight changes in movement and vibration alert users to a need for maintenance or part replacement.

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:

  • Spraying/Crop-dusting: Agricultural drones can weigh over 55 pounds, applying fertilizers and pesticides to crops with far greater accuracy than traditional uses. This not only saves farmers money through saved resources but reduces labor and exposure to chemicals.
  • Precision Agriculture: Drones can constantly monitor crop conditions from the air and find problems that farmers may not otherwise see. Precision agriculture promotes maximum efficiency and the use of resources on a farm.
  • Irrigation: Drones, aided by IoT sensors, can detect when different crops need to be watered based on soil conditions and history. These drones precisely provide the required amount of water, saving farmers time and money.
  • Planting: Many hours of back-breaking labor can be saved by using drones to plant crops. IoT solutions provide drones with insights on when to plant, how far apart to plant, and when to harvest for optimal yield.
  • Weeding: Land-based drones are robots utilized for weeding around crops. Using digital image processing, these bots sort through an array of images, detecting weeds and removing them either manually or with pesticides. For example, a company named ecorobotix has developed an autonomous robot for precision weeding.
  • Mapping/Surveying: Agricultural drone software typically prompts users to select the area that needs to be covered. Once selected, the software automates a drone flight path to survey the area most effectively. Advanced drones can use GPS functions to automatically take pictures with their built-in cameras.
  • Predictive Analytics: Farmers use information collected by sensors on the farm to make decisions based on real-time data. These insights allow farmers to predict production rates, manage risks for individual crops, and plan for storage.

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 2021 - 2026 report Identifies a $1.96 billion global opportunity for AIoT solutions in agricultural monitoring alone.

Note: Product cover images may vary from those shown

FEATURED COMPANIES

  • Accenture
  • Atos
  • Decisive Farming
  • FluxFarm Inc.
  • Monsanto (Bayer)
  • Semios Technologies

1 Executive Summary

2 Overview
2.1 State of the Agriculture Industry
2.2 Smart Agriculture Market Outlook
2.3 Smart Agriculture Systems and Functionality

3 IoT in Agriculture Market Dynamics
3.1 IoT for Provides Farming Scalability
3.2 IoT in Agriculture Market Drivers
3.3 Integrated Artificial Intelligence of Things Solutions in Agriculture

4 IoT in Agriculture Market Analysis and Forecasts 2021 - 2026
4.1 Global IoT Agriculture Revenue by Application 2021 - 2026
4.2 Global IoT Agriculture Revenue by Segment 2021 - 2026
4.2.1 Global Intelligent Farm Equipment Market 2021 - 2026
4.2.2 Global Smart Sensors in Agriculture Market 2021 - 2026
4.2.3 Market by Smart Sensor Type for IoT in Agriculture 2021 - 2026
4.2.4 Global Agricultural Drone Market 2021 - 2026
4.2.5 Global Smart Farm Robot Market 2021 - 2026
4.2.6 Global Agriculture Software Solutions Market 2021 - 2026
4.3 Global IoT in Agriculture Solution Market 2021 - 2026
4.3.1 Global Revenue for IoT Precision Agriculture
4.3.2 Global Revenue for IoT Indoor Farming
4.3.3 Global Revenue for IoT Livestock Farming
4.3.4 Global Revenue for IoT Fisheries and Aquaculture
4.4 IoT in Agriculture Revenue by Region 2021 - 2026
4.4.1 North American Market for IoT in Agriculture
4.4.2 European Market for IoT in Agriculture
4.4.3 APAC Market for IoT in Agriculture
4.4.4 Rest of World Market for IoT in Agriculture
4.5 Global Managed Services in IoT Agriculture
4.6 Predictive Analytics and Artificial Intelligence in Smart Agriculture

5 IoT in Agriculture Vendor Analysis
5.1 Smart Agriculture Vendor Ecosystem
5.2 IoT in Agriculture Vendor Strategies
5.3 Select Smart Agriculture Solutions
5.3.1 Accenture
5.3.2 AgEagle
5.3.3 Cattle Watch
5.3.4 Monsanto (Bayer)
5.3.5 Decagon (METER Environment)
5.3.6 Deepfield Robotics (Bosch)
5.3.7 DroneDeploy
5.3.8 FarmersEdge
5.3.9 IBM
5.3.10 Euravka
5.3.11 FluxFarm Inc.
5.3.12 John Deere
5.3.13 Kaa Open Source IoT Platform
5.3.14 Libelium Comunicaciones Distribuidas S.L.
5.3.15 MTN
5.3.16 SWIM
5.3.17 SlantRange
5.3.18 Telit
5.3.19 PTC
5.3.20 TopCon
5.3.21 Intrinsyc Technologies Corp
5.3.22 Raven Industries
5.3.23 Semtech
5.3.24 Trackit

6 Appendix One: IoT Data Analytics
6.1 IoT in Agriculture Data Analytics Market
6.2 IoT in Agriculture Data Analytics 2021 - 2026
6.3 IoT in Agriculture Opportunity Analysis

7 Appendix Two: Additional Company Analysis
7.1 AG Leader Technology
7.2 AGCO Corporation
7.3 AgJunction Inc.
7.4 Agribotix
7.5 Amber Agriculture
7.6 Antelliq Corporation
7.7 Arable
7.8 Atos
7.9 aWhere
7.10 Bext360
7.11 C2M
7.12 Caterpillar
7.13 Cisco Systems, Inc.
7.14 CNH Industrial
7.15 Connecterra
7.16 CropMetrics LLC
7.17 Decisive Farming
7.18 Deere and Company
7.19 Delaval, Inc.
7.20 Dickey John
7.21 E-peas
7.22 Everynet
7.23 Farmers Edge, Inc.
7.24 Fasal
7.25 GEA Farm Technology
7.26 Hitachi, Ltd
7.27 IBM
7.28 Intel Corporation
7.29 Komatsu Forest AB
7.30 Lely
7.31 Libelium
7.32 Lindsay Corporation
7.33 Monsanto
7.34 NTT DoCoMo
7.35 OnFarm Systems Inc.
7.36 Plenty
7.37 Ponsse
7.38 Precision Hawk
7.39 Pycom
7.40 Raven Industries, Inc.
7.41 Scriptr
7.42 SemiosBIO Technologies
7.43 Semtech Corporation
7.44 Skylo Technologies
7.45 SlantRange, Inc.
7.46 Spectrum Technologies, Inc.
7.47 SST Development Group Inc.
7.48 Swift Navigation
7.49 TeeJet Technologies
7.50 Telit
7.51 The Climate Corporation
7.52 Tigercat
7.53 Topcon Positioning Systems
7.54 Treemetrics
7.55 Trimble Inc.
7.56 UpTake Networks

Figures
Figure 1: Global Aggregate Revenue for IoT in Agriculture 2021 - 2026
Figure 2: Global IoT Agriculture Revenue by Application 2021 - 2026
Figure 3: Global IoT Agriculture Revenue by Segment 2021 - 2026
Figure 4: Global IoT Agriculture Solution Deployment 2021 - 2026
Figure 5: Global Intelligent Farm Equipment Unit Deployment vs. Revenue 2021 - 2026
Figure 6: Intelligent Farm Equipment Revenue by Region 2021 - 2026
Figure 7: Intelligent Farm Equipment Deployment by Region 2021 - 2026
Figure 8: Global Smart Sensors in Agriculture Unit Deployment vs. Revenue 2021 - 2026
Figure 9: Smart Sensors in Agriculture Revenue by Region 2021 - 2026
Figure 10: Regional Smart Sensors in Agriculture Deployment 2021 - 2026
Figure 11: Global Revenue by Smart Sensors Type 2021 - 2026
Figure 12: Global Smart Sensors in Agriculture Deployment by Type 2021 - 2026
Figure 13: Global Agricultural Drone Unit Deployment vs. Revenue 2021 - 2026
Figure 14: Agricultural Drone Revenue by Region 2021 - 2026
Figure 15: Agricultural Drones by Region 2021 - 2026
Figure 16: Global Agricultural Revenue by Drone Type 2021 - 2026
Figure 17: Global Agricultural Drone Deployment by Type 2021 - 2026
Figure 18: Many High ROI AgriTech Drone Applications
Figure 19: Drones as a Service in Agriculture Market
Figure 20: Global Smart Farm Robot Unit Deployment vs. Revenue 2021 - 2026
Figure 21: Smart Farm Robot Regional Revenue 2021 - 2026
Figure 22: Smart Farm Robot Deployment by Region 2021 - 2026
Figure 23: Production Yield Increases due to Greater Efficiency and Effectiveness
Figure 24: Global Agriculture Software Solutions Deployment vs. Revenue 2021 - 2026
Figure 25: IoT Software Solutions in Agriculture Revenue by Region 2021 - 2026
Figure 26: IoT Software Solutions in Agriculture Deployment by Region 2021 - 2026
Figure 27: Global IoT in Agriculture Revenue by Solution Type 2021 - 2026
Figure 28: IoT in Agriculture by Region 2021 - 2026
Figure 29: IoT in Agriculture Deployment by Region 2021 - 2026
Figure 30: North America IoT in Agriculture Deployment vs. Revenue 2021 - 2026
Figure 31: European IoT in Agriculture Deployment vs. Revenue 2021 - 2026
Figure 32: APAC IoT in Agriculture Deployment vs. Revenue 2021 - 2026
Figure 33: RoW IoT in Agriculture Deployment vs. Revenue 2021 - 2026
Figure 34: Big Data and Analytics Framework for Agriculture

Tables
Table 1: Global IoT Agriculture Revenue by Application 2021 - 2026
Table 2: Global IoT Agriculture Revenue by Segment 2021 - 2026
Table 3: Global IoT Agriculture Solution Deployment 2021 - 2026
Table 4: Global Intelligent Farm Equipment Unit Deployment vs. Revenue 2021 - 2026
Table 5: Intelligent Farm Equipment Revenue by Region 2021 - 2026
Table 6: Intelligent Farm Equipment Deployment by Region 2021 - 2026
Table 7: Global Smart Sensors in Agriculture Unit Deployment vs. Revenue 2021 - 2026
Table 8: Global Smart Sensors in Agriculture Revenue 2021 - 2026
Table 9: Smart Sensors in Agriculture Regional Deployment 2021 - 2026
Table 10: Global Revenue by Smart Sensor Type 2020 - 2028
Table 11: Global Smart Sensors in Agriculture Deployment by Type 2021 - 2026
Table 12: Global Agricultural Drone Unit Deployment vs. Revenue 2021 - 2026
Table 13: Agricultural Drone Revenue by Region 2021 - 2026
Table 14: Agricultural Drone Deployment by Region 2021 - 2026
Table 15: Global Agricultural Revenue by Drone Type 2021 - 2026
Table 16: Global Agricultural Drone Deployment by Type 2021 - 2026
Table 17: Global Smart Farm Robot Unit Deployment vs. Revenue 2021 - 2026
Table 18: Smart Farm Robot Regional Revenue 2021 - 2026
Table 19: Smart Farm Robot Deployment by Region 2021 - 2026
Table 20: Global Agriculture Software Solutions Deployment vs. Revenue 2021 - 2026
Table 21: IoT Software Solutions in Agriculture Revenue by Region 2021 - 2026
Table 22: IoT Software Solutions in Agriculture Deployment by Region 2021 - 2026
Table 23: Global IoT in Agriculture Revenue by Solution Type 2021 - 2026
Table 24: Global Revenue for IoT Precision Agriculture by Solution 2021 - 2026
Table 25: Global Revenue for IoT Indoor Farming by Solution 2021 - 2026
Table 26: Global Revenue for IoT Livestock Farming by Solution 2021 - 2026
Table 27: Global Revenue for IoT Fisheries and Aquaculture by Solution 2021 - 2026
Table 28: IoT in Agriculture by Region 2021 - 2026
Table 29: IoT in Agriculture Deployment by Region 2021 - 2026
Table 30: North America IoT in Agriculture Deployment vs. Revenue 2021 - 2026
Table 31: North American IoT in Agriculture Revenue by Segment 2021 - 2026
Table 32: North American IoT in Agriculture Deployment by Segment 2021 - 2026
Table 33: European IoT in Agriculture Deployment vs. Revenue 2021 - 2026
Table 34: European IoT in Agriculture Revenue by Segment 2021 - 2026
Table 35: European IoT in Agriculture Deployment by Segment 2021 - 2026
Table 36: APAC IoT in Agriculture Deployment vs. Revenue 2021 - 2026
Table 37: APAC IoT in Agriculture Revenue by Segment 2021 - 2026
Table 38: APAC IoT in Agriculture Deployment by Segment 2021 - 2026
Table 39: RoW IoT in Agriculture Deployment vs. Revenue 2021 - 2026
Table 40: RoW IoT in Agriculture Revenue by Segment 2021 - 2026
Table 41: RoW IoT in Agriculture Deployment by Segment 2021 - 2026
Table 42: Global Managed IoT Services in Agriculture 2021 - 2026
Table 43: Services offered by Deepfield Robotics
Table 44: OnFarm Data Management Platform Economics
Table 45: IoT Driven Big Data Analytics in Agriculture Revenue 2021 - 2026

Note: Product cover images may vary from those shown
  • Accenture
  • AG Leader Technology
  • AG-NAV INC.
  • AGCO Corporation
  • AgEagle
  • AgJunction Inc.
  • Agribotix
  • Amber Agriculture
  • Antelliq Corporation
  • Arable
  • Atos
  • aWhere
  • Bext360
  • C2M
  • Caterpillar
  • Cattle Watch
  • Cisco Systems, Inc.
  • CNH Industrial
  • Connecterra
  • CropMetrics LLC
  • Decagon (METER Environment)
  • Decisive Farming
  • Deepfield Robotics (Bosch)
  • Deere and Company
  • Delaval, Inc.
  • Dickey John
  • DroneDeploy
  • e-peas
  • Euravka
  • everynet
  • Farmers Edge, Inc.
  • Fasal
  • FluxFarm Inc.
  • GEA Farm Technology
  • Hitachi, Ltd
  • IBM
  • Intel Corporation
  • Intrinsyc Technologies Corp
  • John Deere
  • Kaa Open Source IoT Platform
  • Komatsu Forest AB
  • Lely
  • Libelium
  • Lindsay Corporation
  • Monsanto (Bayer)
  • NTT DoCoMo
  • OnFarm Systems Inc.
  • Plenty
  • Ponsse
  • Precision Hawk
  • PTC
  • Pycom
  • Raven Industries, Inc.
  • scriptr
  • Semios Technologies
  • Semtech Corporation
  • Skylo Technologies
  • SlantRange, Inc.
  • Spectrum Technologies, Inc.
  • SST Development Group Inc.
  • Swift Navigation
  • SWIM
  • TeeJet Technologies
  • Telit
  • Tigercat
  • Topcon Positioning Systems
  • Trackit
  • Treemetrics
  • Trimble Inc.
  • UpTake Networks
Note: Product cover images may vary from those shown

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