Core Viewpoint - The smart agricultural machinery industry is experiencing rapid growth driven by policy support and technological advancements, with significant potential for future development in China [1][13][15]. Industry Overview - Smart agricultural machinery integrates advanced technologies such as intelligent perception, automatic control, positioning navigation, unmanned driving, and artificial intelligence, enabling precise and efficient agricultural operations [2]. - The main categories of smart agricultural machinery include autonomous tractors, intelligent seeders, intelligent harvesters, drones, smart irrigation systems, agricultural robots, and sensors [2]. Market Scale - The scale of smart agricultural machinery in China has expanded from 1.2 billion yuan in 2020 to 11.8 billion yuan in 2024, with a compound annual growth rate of 77% [13]. - The penetration rate of smart agricultural machinery in China was 7.2% in 2024, significantly lower than the global market penetration rate of 31.6%, indicating substantial room for growth [15]. Submarket Insights - **Smart Tractors**: In 2024, sales of smart tractors in China reached 14,200 units, a year-on-year increase of 67%. Most smart tractors are high-power models with over 100 horsepower [17]. - **Intelligent Harvesters**: The sales of intelligent harvesters in China surged to 317,000 units in 2024, with a penetration rate nearing 25% [18]. - **Plant Protection Drones**: The number of plant protection drones in China reached 251,000 units in 2024, covering an operational area of 2.67 billion acres, reflecting a nearly 25% year-on-year growth [21]. Development Background - The Chinese government has implemented numerous policies to support the development of the smart agricultural machinery industry, including the promotion of digital upgrades and the establishment of a national agricultural machinery operation command platform [8][9][10]. Challenges and Recommendations - The industry faces challenges such as insufficient technological innovation, low acceptance of smart machinery among farmers, and inadequate policy support. Recommendations include increasing research funding, enhancing farmer training, and improving policy clarity and execution [23][24][25].