Global Artificial Intelligence in Agriculture Market – Industry Analysis and Forecast (2018-2026)

Global Artificial Intelligence in Agriculture Market was valued US$ 575.3 Mn in 2017 and is expected to reach US$ 3090.8 Mn by 2026, at a CAGR of 23.39 % during forecast period. Artificial intelligence (AI) in agriculture market is being driven by the increasing implementation of the robots in agriculture. Increasing consumption and need of better yield of crops are expected to be the major factor which is fueling the demand of robots in agriculture. Increasing consumption inspiring farmers to expand farming operations and give escalation to the requirement of automating farming operations. By way of the farmers are moving more towards automation, drones and robots are becoming essential part of agriculture farms and are improving yield and improving the product quality. Presence of IoT sensors in agriculture application is capable of providing farmers with acute information about rainfall, crop yields, soil nutrition, and pest infestation. This information is valuable to overall farm production and provides the most accurate data to be used for improving complete farming techniques.

This factor are driving the AI market in Agriculture in recent periods with good effects. Increasing adoption of cattle face recognition technology is also one of the major drivers behind the growth of AI market in agriculture. With the application of advanced metrics, including cattle facial recognition programs and image classification incorporated with body condition score and feeding patterns, dairy farms are currently being able to independently monitor all behavioral aspects in a group of cattle. So that, with the implementation of AI in agriculture, cattle facial recognition can enable diagnoses of lame cows before they show plain symptoms, so, saving farmers from the loss of low milk production. This factor is helping farmers to optimize records and decreases the cost of feed to a great extent. Several agriculture start-ups effectively placed themselves on the global canvas in a short period due to their scientific inventions. Prospera, an Israeli start-up, established in 2014, transformed agriculture by emerging a cloud-based solution. It collects data from the field like water, soil, aerial images, and integrates with the device to take required decisions and use the available resources in an optimum method. Major obstacles in the growth of AI market in agriculture are high cost of cutting-edge AI technology and lack of awareness of AI in agriculture between end users. Recently, more than 61% of small and medium enterprises (SMEs) surveyed believed that they are not on the radar of seriously executing AI in their business process because of budget restraints and lack of skilled workers.

Crop and soil management, robots and predictive analytics are major applications of the AI in agriculture in field monitoring, Predictive analytics is projected to be the fastest growing application during the forecast period. This technology supports farmers in improving their crop yield and product quality by analyzing and associating large amount of structured and unstructured data from multiple sources to abstract actionable visions to enhance yield. AI makes practical assessments of, market intelligence, demand and supply, crop competitiveness and regional crop planning very conveniently. Several new products are getting launched with new technology integration in crop monitoring space are arriving with research groups and regional unions, to expand regional crop monitoring capabilities.

This is a major factor for the growth of the AI market in agriculture in recent times. For example, Joint Research Centre (JRC) in European Union (EU), launched an EU-wide crop and weather monitoring e-service in September 2017. With growing research and development of advanced robotics technology in the agriculture sector, farming practices are being more sophisticated and modernized. Focus of agriculture stakeholders is now shifting from traditional farming practices to filtering the output using advanced farming methods and minimizing the extent of carbon footprint in entire agricultural process.

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