The article cited AMA's Global AI in Agriculture Market Study explored substantial growth with CAGR of 22.34%. According the report, Adoption of Advanced Robotic Technology is one of the primary growth factors for the market. Rising Demand of Cattle Face Recognition Technology is also expected to contribute significantly to the AI in Agriculture market. Overall, Precision Farming  applications of AI in Agriculture, and the growing awareness of them, is what makes this segment of the industry important to its overall growth.

AMA’s Analyst on the Global AI in Agriculture market identified that the demand is rising in many different parts of the world as "Huge Opportunity In Untapped Market In Emerging Countries". Furthermore, some recent industry insights like "In December 2022, Accenture forged a collaborative partnership with Planet Labs PBC (NYSE: PL), a prominent provider of daily Earth data and insights. This collaboration aims to enhance decision-making processes for organizations operating in agriculture industries by leveraging the comprehensive information offered by Planet Labs." is constantly making the industry dynamic. One of the challenges that industry facing is "High Investment"

The Technology, such as Machine Learning, is boosting the AI in Agriculture market. Additionally, the rising demand from SMEs and various industry verticals, macro-economic growth are the prime factors driving the growth of the market.

The Offerings, such as Hardware, is boosting the AI in Agriculture market. Additionally, the rising demand from SMEs and various industry verticals, macro-economic growth are the prime factors driving the growth of the market.

The report provides an in-depth analysis and forecast about the industry covering the following key features:
o Industry outlook including current and future market trends, drivers, restraints, and emerging technologies
o Analyses the Global AI in Agriculture market according to Type, Application, and regions
o Analyzes the top 10 players in terms of market reach, business strategy, and business focus
o Provides stakeholders’ insights and key drivers & trends of the market

Market Size Estimation
In market engineering method, both top-down and bottom-up approaches have been used, along with various data triangulation process, to predict and validate the market size of the AI in Agriculture market and other related sub-markets covered in the study.

o Key & emerging players in the AI in Agriculture market have been observed through secondary research.
o The industry’s supply chain and overall market size, in terms of value, have been derived through primary and secondary research processes.
o All percentage shares, splits, and breakdowns have been determined using secondary sources and verified through primary sources.

Data Triangulation
The overall AI in Agriculture market size is calculated using market estimation process, the AI in Agriculture market was further split into various segments and sub-segments. To complete the overall market engineering and arriving at the exact statistics for all segments and sub-segments, the market breakdown and data triangulation procedures have been utilized, wherever applicable. The data have been triangulated by studying various influencing factors and trends identified from both demand and supply sides of various applications involved in the study. Along with this, the Global AI in Agriculture market size has been validated using both top-down and bottom-up approaches.