Global Artificial Intelligence in Agriculture Market

Global Artificial Intelligence in Agriculture Market was valued at US$ X4X Mn in 2019 and is expected to reach US$ X1X6 Mn by 2027, at a CAGR of X5. 3X% during a forecast period.

Global Artificial Intelligence in Agriculture Market

Global Artificial Intelligence in Agriculture Market

According to UN Food and Agriculture Organization, the population will rise by 9.8 billion by 2050. Conversely, only 4% further land will come under farming by then. In this perspective, use of advance technological solutions to make cultivation more efficient, remains one of the greatest requirements. While, AI sees many direct use across sectors, i.e. AI-powered solutions will not only empower farmers to do better with less, it will also increase quality and assure faster go to market for crops. The report directed towards how AI can transform the agriculture landscape, the use of drone-made image processing techniques, exactitude farming landscape, the future of agriculture, challenges and overall Artificial Intelligence in Agriculture market position in forecast period.

Market Scope

Agriculture is seeing prompt implementation of AI and Machine Learning (ML) both in terms of agricultural products and in-field agriculture techniques. Intellectual computing in specific, is all set to become the most disruptive technology in agriculture service sector as it can understand, learn, and respond to different circumstances to rise efficacy. Providing some of these solutions as a service such as chatbot or other conversational platform to all the farmers will help them keep pace with technological innovations as well as apply the same in their day-to-day farming to obtain the benefits of this service. Now, Microsoft is working with 175 farmers in India to deliver counselling services for sowing, land and fertilizer. This initiative has previously resulted in 30% higher yield per hectare on an average compared to last year.

Industry Dynamics

Drivers

Growth driven by IOT

Large volumes of data get produced every day together with structured and unstructured format. These re-count to data on historic weather pattern, soil reports, new research, rainfall, pest invasion, images from drones and cameras. Intellectual IOT solutions can sense all this data and deliver strong perceptions to increase yield. Proximity Sensing and Remote Sensing are two technologies which are mainly used for intelligent data fusion. This supports in soil characterization based on the soil below the surface in a specific place. Hardware solutions like Rowbot are already coupling data collecting software with robotics to formulate the best fertilizer for growing corns as well to other activities to maximize output.

Image-based insight generation

Exactitude farming is one of the maximum discussed areas in farming today. Drone-based images can support in in-depth field analysis, crop observing and scanning of fields. Computer vision technology, IOT and drone data can be collective to assure rapid actions by farmers. Feeds from drone image data can create alerts in real time to increase the speed of precision farming. Companies such as Aerialtronics have employed IBM Watson IoT Platform and the Visual Recognition APIs in commercial drones for image analysis in real time. More or less areas where computer vision technology can be put to utilization in Disease detection, Crop readiness identification, Field management, etc.

Health monitoring of crops

Remote sensing techniques together with hyper spectral imaging and 3d laser scanning are crucial to create crop metrics thru thousands of acres. It has the likely to lead in a revolutionary change regarding of how farmlands are observed by farmers both from time and effort outlook. This technology will also be utilized to monitor crops along their complete lifecycle containing report generation in case of anomalies.

Automation techniques in irrigation and enabling farmers

With regard to human intensive processes in farming, irrigation is one of the process. Machines trained on historic weather pattern, soil quality and kind of crops to be grown, can automate irrigation and amplify overall yield. With close to 65-75% of the world’s fresh water being utilized in irrigation, automation can assist to farmers for better management of their water problems.

Challenges

Lack of familiarity with high tech machine learning solutions

However, AI offers huge opportunities for application in agriculture, there still exists a lack of awareness with high tech machine learning solutions in farms across most of the region in a globe. Introduction of farming to external factors like weather conditions, soil situations and existence of pests is relatively high. Similarly, AI systems also require a lot of data to train machines and to make accurate predictions.

Market Trends

Agricultural Drones to Amplify the Growth of Market

As global population anticipated to reach over 9.8 billion by 2050, agricultural consumption is anticipated to rise by a massive 75%, where drones have now been mainstreamed for smart farming supporting farmers in a range of tasks from analysis and planning to the real planting of crops, and the ensuing observing of fields to determine health and growth. Also, drones prepared with hyperspectral, multispectral, or thermal sensors are capable to detect areas that need changes in irrigation. Once crops have started growing, these sensors are capable to estimate their vegetation index, and indicator of health through AI, by determining the crop’s heat signature.

Geographic Overview

Europe is estimated to account for the largest market growth due to their farmers manage almost half of the land area for agriculture and it makes dominant industry in Europe. Trend in observing and reporting utensils for indoor and outdoor farms, and delivering a visualization of the farmer’s intact production using computer vision and AI are increasing the AI market in agriculture. The European Soil Data Centre (ESDAC) is the thematic center for soil associated data in Europe, where its goal is to be the single reference point for and to host all appropriate soil data and statistics at European level. AI firms are handling ‘Internet of the Soil’, which is a software and hardware solution for observing 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 retrieved by any internet connected device. Berlin-based InFarm has urbanized a vertical indoor farming system using IoT, Big Data, and cloud analytics, which can be employed in supermarkets, restaurants, local distribution warehouses, permitting businesses to grow their own fresh crop on site to deliver to customers. It is already inaugural indoor farms in 1,000 locations in Germany, and expanding in other European markets, which rises the AI in agriculture market.

North America is evaluated as second largest market for AI in agriculture in the worldwide. The growth of the market is attributed to the high selection of trend setting innovations and item in agriculture part. Asia Pacific is estimated to meet high growth rate in the forecast period due to the rising demand from emerging nations, for instance, India and China. Also, rising adoption of the mechanical technology and IoT devices in agriculture is additionally evaluated to drive the Artificial Intelligence in Agriculture market.

The report covers the market leaders, followers and new entrants in the industry with the market dynamics by region. It will also help to understand the position of each player in the market by region, by segment with their expansion plans, R&D expenditure and organic & in-organic growth strategies. Long term association, strategic alliances, supply chain agreement and M&A activities are covered in the report in detail from 2014 to 2019. Expected alliances and agreement in forecast period will give future course of action in the market to the readers. More than ten companies are profiled, benchmarked in the report on different parameters that will help reader to gain insight about the market in minimum time.

The objective of the report is to present a comprehensive analysis of the Global Artificial Intelligence in Agriculture Market including all the stakeholders of the industry. The past and current status of the industry with forecasted market size and trends are presented in the report with the analysis of complicated data in simple language. The report covers all the aspects of the industry with a dedicated study of key players that includes market leaders, followers and new entrants by Vehicle. PORTER, SVOR, PESTEL analysis with the potential impact of micro-economic factors by Vehicle on the market have been presented in the report.

External as well as internal factors that are supposed to affect the business positively or negatively have been analyzed, which will give a clear futuristic view of the industry to the decision-makers. The report also helps in understanding Global Artificial Intelligence in Agriculture Market dynamics, structure by analyzing the market segments and project the Global Artificial Intelligence in Agriculture Market size. Clear representation of competitive analysis of key players by Application, price, financial position, Product portfolio, growth strategies, and regional presence in the Global Artificial Intelligence in Agriculture Market make the report investor’s guide.
Scope of Global Artificial Intelligence in Agriculture Market:

Global Artificial Intelligence in Agriculture Market by Technology:

• Machine Learning
• Computer Vision
• Predictive Analytics
Global Artificial Intelligence in Agriculture Market by Offering:

• Hardware
• Software
• AI-as-a-Service
• Service
Global Artificial Intelligence in Agriculture Market by Application:

• Precision Farming
• Crop Monitoring
• Drone Analytics
• Agriculture Robots
• Others
Global Artificial Intelligence in Agriculture Market by Geography:

• North America
• Asia Pacific
• Europe
• Latin America
• Middle East & Africa
Key Players Operated in Market Includes:

• IBM
• John Deere
• Microsoft
• Agribotix
• The Climate Corporation
• ec2ce
• Descartes Labs
• Sky Squirrel Technologies
• Mavrx
• aWhere
• Gamaya
• Precision
• Granular
• Prospera technologies
• Cainthus
• Spensa Technologies
• Resson
• FarmBot
• Connecterra
• Vision Robotics
• Harvest Croo
• Autonomous Tractor Corporation
• Trace Genomics
• Vine Rangers
• CropX
• Intel
• SAP

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