“Our team consists of around 10 members, and we plan to cultivate a trial area of 100 mu this year,“ said Gao Ying, a contestant from the Qingqiao land cooperative in Chongzhou, about 50 kilometers away from the provincial capital Chengdu.

　　She noted that the advantage of AI farming over traditional methods is its ability to quickly impart essential knowledge and production skills related to agriculture. Additionally, when prompted, it offers constructive feedback.

　　According to the event schedule, the rice transplanting period will occur from May 15 to 30, during which the AI system will initiate intelligent monitoring. Mid-term data analysis and evaluation will take place on July 15, followed by yield and quality assessments in September.

　　LESSONS FROM THE FIRST HARVEST

　　Gao is no stranger to the challenge.

　　Last year's inaugural competition delivered a reality check. The CAAS-developed AI system, guiding novice farmer Gao through 100 mu, finished seventh among nine teams -- outperforming two human counterparts but falling short of veteran growers.

　　Specifically, regarding yield rankings, the AI group achieved an average yield of 516 kilograms per mu, securing eighth place. In terms of quality rankings, the group placed sixth in both the whole milled rice rate and the chalky grain rate, and seventh in chalkiness.

　　The organizers noted that the AI group utilized the Qianxiangyou 956 variety, reaching 88 percent of the established yield standard, an achievement experts called “commendable.“

　　However, its 7th-place finish highlighted challenges in replicating human adaptability.

　　“AI needs field schooling,“ explained Wang Nan, the executive chief scientist of the urban agricultural development strategy and planning team at the CAAS Institute of Urban Agriculture, whose team developed the decision-making system.