▶ 調査レポート


• 英文タイトル:Artificial Intelligence (AI) Market in Agriculture - Growth, Trends, COVID-19 Impact, and Forecasts (2021 - 2026)

Mordor Intelligenceが調査・発行した産業分析レポートです。農業における人工知能(AI)の世界市場2021-2026:成長・動向・新型コロナの影響・市場予測 / Artificial Intelligence (AI) Market in Agriculture - Growth, Trends, COVID-19 Impact, and Forecasts (2021 - 2026) / MRC2103D120資料のイメージです。• レポートコード:MRC2103D120
• 出版社/出版日:Mordor Intelligence / 2021年1月
• レポート形態:英文、PDF、120ページ
• 納品方法:Eメール(受注後2-3営業日)
• 産業分類:農業
• 販売価格(消費税別)
  Single User¥561,000 (USD4,250)▷ お問い合わせ
  Team User¥627,000 (USD4,750)▷ お問い合わせ
  Corporate License¥990,000 (USD7,500)▷ お問い合わせ
• ご注文方法:お問い合わせフォーム記入又はEメールでご連絡ください。
• お支払方法:銀行振込(納品後、ご請求書送付)

The Artificial Intelligence (AI) Market in Agriculture was valued at USD 766.41 million in 2020 and is expected to reach USD 2468.02 million by 2026, at a CAGR of 21.52% over the forecast period 2021 – 2026. Driverless tractor is trending in market as these tractor can steer automatically using GPS-based technology, lift tools from the ground, recognize the boundaries of a farm, and can be operated remotely using a tablet. A fleet of smaller automated tractors could lift farmer revenue by more than 10 percent and can reduce farm labor costs.

– Maximize crop yield using machine learning technique is driving the market. Species selection is a tedious process of searching for specific genes that determine the effectiveness of water and nutrients use, adaptation to climate change, disease resistance, as well as nutrients content or a better taste. Machine learning, in particular, deep learning algorithms, take decades of field data to analyze crops performance in various climates and based on this data one can build a probability model that would predict which genes will most likely contribute a beneficial trait to a plant.
– Increase in the adoption of cattle face recognition technology is driving the market. Through the application of advanced metrics, including cattle facial recognition programs and image classification incorporated with body condition score and feeding patterns, dairy farms are now being able to individually monitor all behavioral aspects in a group of cattle.
– Increase use of Unmanned Aerial Vehicles (UAVs) across agricultural farms is driving the market as the use of drones in the agriculture industry can be use in crop field scanning with compact multispectral imaging sensors, GPS map creation through onboard cameras, heavy payload transportation, and livestock monitoring with thermal-imaging camera-equipped drones, which increases the demand of UAVs.
– However, lack of standardization is restraining the market growth as lack of standards in data collection, and lack of data sharing is high, and machine learning and artificial intelligence and advanced algorithm design have moved so fast, but the collection of well-tagged, meaningful agricultural data is way behind.

Key Market Trends

Agricultural Drones to Drive the Growth of Market

– As global population projected to reach over 9 billion by 2050, agricultural consumption is expected to increase by a massive 70%, where drones have now been mainstreamed for smart farming assisting farmers in a range of tasks from analysis and planning to the actual planting of crops, and the subsequent monitoring of fields to ascertain health and growth.
– Drones equipped with hyperspectral, multispectral, or thermal sensors are able to identify areas that require changes in irrigation. Once crops have started growing, these sensors are able to calculate their vegetation index, and indicator of health through AI, by measuring the crop’s heat signature.
– No one likes the idea of chemical spraying, but, for the time being, it’s a necessary part of large-scale agriculture. Fortunately, smart farming drones are helping reduce its environmental impact. Specialized UAVs (Unmanned aerial vehicle) are equipped with sprayers, with various kinds of technology, like ultrasonic echoing devices and lasers, which can measure distance with extreme precision. The result is a massive reduction in overall spray and a much lower chemical level reaching the groundwater.
– Agrobotix LLC is a drone enabled software company that provides quality imaging and data analysis for sustainable and precision agriculture, and is supporting over more than 53 crops, including corn, grape, apple, sugarcane, and so on for sustainable and precision farming across 50 countries. AgEagle Aerial Systems (firm who acquired Agrobotix LLC) is planning to develop new products with new technologies, such as weather data, advanced image recognition, and precise analysis, to provide better recommendation to the farmers/consumers using it.

Europe Expected to Account for the Largest Market Growth

– Farmers manage almost half of the European land area, making agriculture a dominant industry in Europe. Trend in monitoring and reporting tools for indoor and outdoor farms, and providing a visualization of the farmer’s entire production using computer vision and artificial intelligence is increasing the AI market in agriculture.
– Row crop cultivation is done by AI in various countries in Europe, where robot uses 20x less herbicide due to its accuracy for weeding of row crops.
– The European Soil Data Centre (ESDAC) is the thematic centre for soil related data in Europe, where its ambition is to be the single reference point for and to host all relevant soil data and information at European level. AI firms are managing ‘Internet of the Soil’, which is a software and hardware solution for monitoring soil conditions like humidity, temperature, electrical conductivity, and more in European countries. Their sensors connect wirelessly to a cloud-based platform where it can be accessed by any internet-connected device.
– Berlin-based InFarm has developed a vertical indoor farming system using IoT, Big Data, and cloud analytics, that can be implemented in supermarkets, restaurants, local distribution warehouses, or even schools, allowing businesses to grow their own fresh produce on site to deliver to customers. It is already opening indoor farms in 1,000 locations in Germany, and expanding in other European markets, which increases the AI in agriculture market.

Competitive Landscape

The AI market in agriculture is fragmented, as a number of players supplying same product on lower-cost make market competition stiff. Also technological advancements by players and high presence of local and regional players pose a major threat in a price-sensitive market. Key players are Microsoft Corp., IBM Corp. (NITI Aayog), Agribotix LLC, etc. Recent developments in the market are –

– June 2019 – XAG, a Chinese firm, presented its innovative solutions of combining drones with AI and IoT technology to achieve precision agriculture and induce transformational changes to the food system in 3rd AI for Good Global Summit, in Geneva. XAG is driving AI-powered intelligent devices such as drones and sensors to establish digital farming infrastructure in rural areas and enable precision agriculture which, for example, accurately target pesticides, seeds, fertilizers and water to wherever it is needed.
– April 2019 – Yara and IBM Services joined forces to innovate and commercialize digital agricultural solutions that will help increase global food production. Yara and IBM will develop digital solutions that empower professional and smallholder farmers to optimize farming practices to increase yields, crop quality, and incomes in a sustainable way. The partnership will focus on all aspects of farm optimization and apply AI, machine learning, and in-field data to unlock new insights for farmers, specifically in the area of weather data, where weather company will provide hyperlocal weather forecasts with real-time actionable recommendations tailored to the specific needs of individual fields/crops.

Reasons to Purchase this report:

– The market estimate (ME) sheet in Excel format
– 3 months of analyst support


1.1 Study Deliverables
1.2 Study Assumptions
1.3 Scope of the Study



4.1 Market Overview
4.2 Introduction to Market Drivers and Restraints
4.3 Market Drivers
4.3.1 Maximize Crop Yield Using Machine Learning technique
4.3.2 Increase in the Adoption of Cattle Face Recognition Technology
4.3.3 Increase Use of Unmanned Aerial Vehicles (UAVs) Across Agricultural Farms
4.4 Market Restraints
4.4.1 Lack of Standardization in Data Collection
4.5 Value Chain Analysis
4.6 Industry Attractiveness – Porter’s Five Force Analysis
4.6.1 Threat of New Entrants
4.6.2 Bargaining Power of Buyers/Consumers
4.6.3 Bargaining Power of Suppliers
4.6.4 Threat of Substitute Products
4.6.5 Intensity of Competitive Rivalry


6.1 By Application
6.1.1 Weather Tracking
6.1.2 Precision Farming
6.1.3 Drone Analytics
6.2 By Deployment
6.2.1 Cloud
6.2.2 On-premise
6.2.3 Hybrid
6.3 Geography
6.3.1 North America
6.3.2 Europe
6.3.3 Asia-Pacific
6.3.4 Rest of the World

7.1 Company Profiles
7.1.1 Microsoft Corporation
7.1.2 IBM Corporation
7.1.3 Granular Inc.
7.1.4 aWhere Inc.
7.1.5 Prospera Technologies Ltd.
7.1.6 Gamaya SA
7.1.7 ec2ce
7.1.8 PrecisionHawk Inc.
7.1.9 Cainthus Corp.
7.1.10 Tule Technologies Inc.