“During the initial development of AI algorithms, our data was fragmented, but now we have constructed a comprehensive dataset,“ Wang said.

　　This dataset encompasses the entire rice growing process, including timing, crop growth images, and relevant climatic and soil moisture data.

　　Moreover, as the dataset expands, both the precision of AI decision-making and its analytical capabilities are improving, enabling farmers to make more accurate assessments regarding key agricultural practices such as fertilization and weeding.

　　However, the technology is not without its limitations. Experts noted that while AI collects data through cameras and sensors installed in the fields, its environmental perception capabilities remain underdeveloped, particularly with regard to satellite imagery, which has not performed ideally in practical applications.

　　Additionally, the integration of AI technology with traditional agricultural practices requires time to establish trust and collaborative processes between AI and farmers.

　　“AI's strength lies in processing 10,000 data points per mu from our monitoring network, but translating that into field actions requires deeper farmer-algorithm synergy,“ Wang said.

　　He said that last year, the adoption rate for AI decisions has reached 73 percent, with particular struggles in pest management timing.

　　“This year, we're targeting above 80 percent alignment between AI suggestions and farmer actions,“ he said.

　　HYBRID HORIZONS EMERGE

　　Rather than framing it as man-versus-machine, organizers emphasize convergence.

　　“Artificial intelligence is a tool that assists rather than replaces human labor. We aim to enhance support for urban producers and decision-makers through upgraded computing power,“ said Wang.