On September 28, an on-site observation meeting for the application demonstration of a smart farming system in the main wheat-maize rotation production area of the Huang-Huai-Hai region was held at the Sinochem MAP Hengtai Farm in Hengtai County, Shandong Province. The event was part of a National Key R&D Program project titled "Key Technologies and Integrated Demonstration for Precision and Smart Production Management of Grain Crops," led by the Institute of Agricultural Resources and Regional Planning under the Chinese Academy of Agricultural Sciences.

Representatives from the Chinese Academy of Agricultural Sciences, Nanjing Agricultural University, Henan Agricultural University, Beijing Academy of Agriculture and Forestry Sciences, China Agricultural University, Sinochem Modern Agriculture Co., Ltd., and Hengtai County attended the meeting. Participants went deep into the fields to observe firsthand a series of new technologies and new equipment developed by the project to meet the practical needs of precise and smart production in wheat-maize rotation in the Huang-Huai-Hai region, focusing on key management links such as seed, fertilizer, water, and pesticide management for wheat and maize. These included crop phenotype monitoring, precision sowing, nutrient diagnosis and variable-rate topdressing, precision irrigation, and pest and disease monitoring and variable-rate spraying.

They also inspected on site a regional smart farming technology system that integrates real-time monitoring, intelligent diagnosis, quantitative regulation, and variable-rate operations, with deep integration of agronomy, agricultural machinery, and information technology, as well as its demonstration results. They directly experienced how digital and intelligent technology helps wheat-maize production management shift from empirical judgment to data-driven decision-making, and its important supporting role in improving the precision and intelligence of crop production management.

In terms of crop phenotype monitoring, the project integrated the technical advantages of high-precision ground-based in-situ sensing, high-resolution low-altitude UAV sensing, and large-scale satellite remote sensing monitoring. It overcame core technologies such as digital farmland and key environmental parameter monitoring, fixed in-situ monitoring of crop growth, and UAV remote sensing monitoring of disasters and yield. Through cross-platform, multi-scale data fusion, it opened up a fully automated technical chain from data perception and collection to intelligent analysis of phenotype information, and built a high-throughput, full-process field crop phenotype monitoring system based on "underground-ground-air" information fusion. This enables continuous monitoring and intelligent analysis of information such as soil moisture, crop growth, disasters, and yield, providing high-precision and highly timely data support for crop planting and precise management of water, fertilizer, and pesticides.

In terms of intelligent decision-making for crop production, the project focused on the precise management needs of nutrients, water, and pests and diseases for wheat and maize. It developed technologies for crop growth diagnosis, variable-rate topdressing, canopy water content monitoring, and intelligent identification of pests and diseases. It built a three-level topdressing prescription model at the pixel, plot, and irrigation group levels, and formed gridded irrigation and variable-rate spraying prescriptions. Among these, the accuracy of the crop variable-rate topdressing decision model reached over 86%, water monitoring accuracy reached 88%, the four-level identification accuracy of maize brown spot reached 87%, and the three-level assessment accuracy of maize Asiatic corn borer reached 89%, achieving a leap from "identifying field differences" to "precise production prescriptions."

In terms of precision operation systems, the project developed intelligent equipment and supporting control technologies adapted to wheat-maize production, including precision sowing, variable-rate fertilization, and plant protection UAVs. The precision sowing system can meet the needs of wheat drilling and maize hill sowing, with sowing precision of at least 95%. The speed-adaptive MPC variable-rate fertilization lag compensation technology reduced system response time to less than 0.5 seconds and fertilization distance lag to less than 0.7 meters. The variable-rate fertilization monitoring and regulation technology achieved fertilizer discharge flow control precision of at least 95% and fertilizer flow detection precision of at least 87.5%, significantly improving the implementation of prescriptions and the precise operation capacity of agricultural machinery.

Project chief scientist Researcher Wu Wenbin said that maize is China's grain crop with the largest planting area and the highest yield, and plays a pivotal role in safeguarding national food security and supporting the stable development of the livestock industry. For a long time, however, China's maize planting and production management has generally been extensive, with uneven sowing quality, delayed acquisition of key agricultural information such as soil moisture, seedling condition, and pests and diseases, and water, fertilizer, and pesticide application mostly relying on traditional experience. This has not only created a large gap between China's maize yields and those of advanced countries, but also brought prominent problems such as waste of agricultural resources and increased ecological pressure on farmland.

In response to this industrial reality, the project team, together with the Sinochem MAP team, selected MAP Hengtai Farm, which has sound irrigation conditions and a solid mechanization foundation, as the core base to build a demonstration zone for the digital transformation and upgrading of maize production methods. The project has always adhered to the principle of being "implementable, replicable, and benefit-generating," and built a smart farming technology system based on multi-source perception, with intelligent diagnosis as the core, precise machinery execution as the guarantee, and a data platform as the hub, achieving data-driven and precise control throughout the entire maize planting process.

The person in charge of Sinochem MAP Hengtai Farm introduced that this digital technology solution, through optimization of core links such as precision sowing, variable-rate fertilization, and intelligent irrigation, increased maize yield per unit area in a 1,000-mu demonstration plot by 8% to 12% compared with surrounding conventional planting controls, while the high-quality product rate remained stable at over 88%. At the same time, it achieved savings in agricultural resources, with chemical fertilizer, pesticide, and irrigation water use reduced by 10% to 15%, 8% to 10%, and 12% to 18%, respectively, compared with conventional planting, significantly improving resource use efficiency. It has now developed the capacity for technical training, extension, and radiation services for surrounding ordinary farmers and new types of agricultural business entities.

Wu Wenbin said that next, the project team will continue to promote the maturation of the smart farming technology system, carry out cross-regional adaptability verification, further expand the scale of demonstration and application, accelerate the translation of scientific and technological achievements into practical results, and provide a replicable and scalable practical model for improving the large-scale yield per unit area of China's grain and oil crops and for the green and low-carbon transformation of agriculture. Author: Li Liying, all-media reporter of Farmers' Daily.