A field observation on a National Key R&D Program project — involving the development and application of a big data platform for grain production, focusing on smart corn production — was held on Sept 19. It was staged at the Beidahuang Group's Hongxing Farm. The event was organized by the Institute of Agricultural Resources and Regional Planning (IARRP) at the Chinese Academy of Agricultural Sciences (CAAS) and the Information Technology Research Center of the Beijing Academy of Agriculture and Forestry Sciences (BAAFS). It was co-organized by the Bei'an branch of the Beidahuang Group and Hongxing Farm. Over 70 participants attended, including leaders and experts from the department of crop management and the Big Data Development Center of the Ministry of Agriculture and Rural Affairs, the Heilongjiang Academy of Land Reclamation Sciences and the Heilongjiang Academy of Agricultural Sciences. Others included participants from the Northeast Institute of Geography and Agroecology of the Chinese Academy of Sciences, the Northeast Agricultural University, the Bei'an Branch of Beidahuang Group, Hongxing Farm and representatives of the project's participating units. 

The observation centered on the demonstration and application of intelligent decision-making technologies for the entire corn production process using big data. The event showed the application of intelligent analysis algorithms in various scenarios and in determining optimal harvest times. These included algorithms for agricultural machinery operation scheduling, drone-based seedling rate surveys, variable precision fertilization, seedling growth monitoring, pest and disease detection and early warnings, yield estimations and planting planning. Project leader Yang Guijun, a researcher at the Information Technology Research Center of the BAAFS, provided a comprehensive overview of the progress that has been made in developing and applying the big data platform for enhanced grain production.

Meanwhile, significant breakthroughs in corn intelligent decision-making technologies were highlighted by Sun Liang, the project leader and researcher at the IARRP. In pre-production planting planning, deep learning was applied for the first time to optimize planting layouts for large-scale farms — developing a farm-scale intelligent planting planning algorithm based on PPO reinforcement learning. The approach is said to achieve multi-objective coordination, balancing economic benefits and agricultural sustainability, while ensuring national food security. In mid-production dynamic monitoring, an adaptive corn growth process dynamic monitoring and forecasting algorithm was developed. That's suitable for multi-scale applications in major grain-producing areas and farms, with a resolution range of 10 meters to 1 kilometer — enabling daily dynamic monitoring and precise forecasting 20 days ahead of key growth stages. The algorithm provides a solid technical foundation for the dynamic real-time updating of fertilization prescriptions and yields and optimal harvest timing predictions. In post-production efficiency evaluations, a comprehensive efficiency evaluation algorithm was constructed, encompassing both production and ecological dimensions. It accurately quantified the core advantages of the socialized service model throughout the entire corn production process in Beidahuang. This in turn has enhanced production efficiency and improved the farmland ecosystem — providing theoretical and practical support for optimizing farm resource allocations and for promoting sustainable agricultural development.

By deeply integrating the big data platform with field production management, the entire corn production process has reportedly achieved visualized, precise and intelligent control. That's further boosted the application level of intelligent decision-making technology in corn production management. The successful hosting of the field observation is said to have marked an important milestone in the development of corn production decision-making algorithms in the field of intelligent corn production. It's expected that these achievements will provide strong technical support for the digital transformation of the entire Heilongjiang region — contributing to the continuing strengthening of national food security capabilities.