Artificial Intelligence in Agriculture Market Survey Reviews, Analysis 2020-2026

Posted by BIS RSRCH on January 14th, 2022

Agriculture technologies are offered for a host of agriculture applications and cover the entire farming cycle, including data management, soil management, yield mapping, as well as monitoring, spraying, harvesting, and planting, among others. The adoption of agriculture technologies across the globe has resulted in the rapid accumulation of a large amount of agricultural data on-farm operations and fields. Management of this big data for increased accuracy, productivity, and reduced manual data feeding has become a pressing requirement. Hence, the advent of artificial intelligence in the agriculture sector is welcomed by the growers and other agriculture customers, as AI technology has been critically used to optimize agricultural operations based on this data and has found innovative use cases and applications. Predictive analytics is one of the most deployed use cases of AI in agriculture, where it aids in efficient farm production with reduced losses.

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The governing trends of artificial intelligence in agriculture market vary across different regions. Artificial intelligence in agriculture market holds a prominent share in various countries of North America, Europe, Asia-Pacific (APAC), and Rest-of-the-World (RoW), including the U.K. and China.

The competitive landscape for artificial intelligence in agriculture market demonstrates an inclination toward companies adopting strategies such as product launch and development and partnerships, collaborations, and joint ventures. The major established players in the market are focusing on product launches and developments to introduce new technologies or developing further on the existing product portfolio. AgEagle Aerial Systems Inc., Gamaya SA, Granular Inc., IBM Corporation, Microsoft Corporation, PrecisionHawk Inc., The Climate Corporation, VineView, Root AI Inc., Prospera Technologies, Robert Bosch GmbH, Deere and Company, Harvest Crop Robotics LLC, BASF SE, and SAP SE are some of the prominent players in the artificial intelligence in agriculture market. The market is highly fragmented with the presence of many small- to medium-sized companies that compete with each other and the large enterprises.

In order to generate public awareness about their existing and new products and technologies and compete with the competitors’ product portfolio, key players operating in this market have ramped up their product launch activities over the recent years. This has been one of the most widely adopted strategies by the players in this market. The partnerships and collaborations strategy have been significantly employed for the expansion into artificial intelligence in agriculture market. With the increasing growth in the global market, companies operating in this industry are compelled to come up with collaborative strategies in order to sustain themselves in the intensely competitive market. Moreover, extensive R&D activities and appropriate regulatory environments are also a prerequisite for the sustained growth of this market. Various government and private research institutes and favorable trade policies are putting in substantial efforts to identify the benefits of these agricultural drone and robot solutions for augmenting global food production. The increase in the adoption of sophisticated smart farming techniques is necessary to bridge the demand and supply gap along with attaining sustainability in production.

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The global agriculture industry has undergone significant and dynamic changes in the past decades, specifically with the introduction of technologies introduced to improve crop yield. With the increase in world population leading to the rise in the global demand for food, growers are prompted to adopt the most advanced and productive methods in farming operations to increase crop yield. One of the new-age methods applied for an increase in production is the new efficient mode of farming known as precision agriculture. This way of farming has resulted in the creation of a large number of data points leading to the growth of data-driven farming. Consequently, the growers and consumers of agricultural products are left with complex big data to analyze. The analysis for this data has prompted the need for cognitive computing with human-like intelligence in farm equipment and solutions based on such computing to yield healthier crops, as well as to monitor crops and soil conditions, control weeds, aid with farm workload, optimize data for growers and enhance the farm operation efficiency. The set of cognitive computing technologies introduced in agriculture for the said purpose is known as artificial intelligence technology.

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