AI is adding new dimensions to the agricultural field with several applications of agricultural robots, drones, crop & soil monitoring and predictive analysis. On the basis of the data related to water stress, nutrient content, images of crops, climate and soil moisture content, AI aids in the prediction of the disease and its cure. Issues such as global warming, growing population, and food security concerns forced the innovations and technological advancements in agriculture. According to the UN Food and Agricultural Organisation (FAO), global population is going to increase its reach up to 9.2 billion by 2050, which means in the next 33 years there will be 2 billion more people on this earth with limited resources. So only increasing plantation doesn’t seem an option to tackle this situation, something more is needed, like the adoption of AI technologies in agriculture.
Farmers in many parts of the world were largely dependent on rainfalls for harvest and subsequent activities. Global warming and climate changes resulted in fragile soil water equation and uncertainty in rainfalls. But AI technology provides an accurate update on climate change for that particular location with suggestions that will guide farmers to take actions in such conditions. AI in agriculture is expanding in areas such as drones, driverless tractors, automated irrigation systems, crop health monitoring, and facial recognition (to detect physical appearances).
Factors Influencing Market Growth:
Growing population, the uncertainty of rain, lack of farm workers, and the need for better yield are some of the major factors driving the growth of AI in agriculture market globally. Growing population has resulted in increased food consumption, food security concerns and need for more agricultural production. Supportive worldwide governments’ policies and initiatives to deploy AI is agriculture is expected to increase awareness among farmers about benefits of utilization of AI techniques in farming.
Most of the farmers are not aware of AI applications in agriculture or they resist adopting AI due to various reasons such as lack of knowledge about the deployment of AI in agriculture and poor IT infrastructure facilities. This is expected to hamper the growth of AI in the agricultural market. There is no standardization and guidelines about AI in agriculture which is expected to lower the growth of the market in near future. The high cost of ownership, lack of historical data and limited resources in developing and underdeveloped countries are some of the other factors expected to hinder the growth of AI in agriculture market globally.
In terms of geography, the market is divided into North America, Europe, Asia-Pacific, and LAMEA. The growth of AI in agriculture market in North America region is influenced by factors such as the presence of startups in this market, advanced infrastructure facilities, and increased awareness about the benefits of AI in agriculture. Along with startups, most of the major players operating in this market are present in U.S. that includes organizations like IBM, John Deere, Microsoft, Agribotix, The Climate Corporation and others. Asia Pacific comprises of numerous developing nations, where supportive government policies from developing and underdeveloped countries is expected to boost the farming industry in future. India has a large area under farming and government initiatives under Digital India campaign, which is promoting the adoption of AI techniques in agriculture to enhance the agricultural production with better quality yield. Growing number of firms from this market in countries like China and India, huge investments on developing infrastructure, and increased adoption of IoT devices in agriculture are some the factors driving the growth of the AI in agriculture market in the Asia Pacific region.
The prominent players from AI in Agriculture market are IBM, Deere & Company (John Deere), Microsoft, Agribotix, The Climate Corporation, ec2ce, Sky Squirrel Technologies, aWhere, Precision Hawk, Granular, and Autonomous Tractor Corporation. Along with these key players, some of the start-ups are engaging in developing AI techniques for agriculture field that includes TellusLabs, Trace Genomics, Harvest CROO Robotics, Abundant Robotics, PEAT and others.
Acquisition and partnerships & tie ups are the major strategies used by the top companies from this market. For instance, In April 2018, TellusLabs announced a strategic partnership with CME group for distribution of products across the globe. TellusLabs and CME group will work together to enhance access to TellusLabs CHI and NDVI metrics. In September 2017, Deere & Company (John Deere) acquired Blue River Technology. Blue River Technology developed technology that uses AI and machine learning to eliminate weeds and produce a better yield. Space Times Lab has tied up with Raizen Energia to develop a technology that will able to predict up to a year in advance, what will be the production capacity of the sugarcane harvest in all its units.
• Machine Learning
• Computer Vision
• Predictive Analytics
• Precision Farming,
o Yield Monitoring
o Field Mapping
o Crop Scouting
o Weather Tracking and Forecasting
o Irrigation Management
• Livestock Monitoring
• Drone Analytics
• Agriculture Robots
o Storage Device
o AI Platform
o AI Solution
o Deployment and Integration
o Support and Maintenance
• North America
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