Digital and intelligent technologies are now being integrated into all aspects of spring farming, reshaping agricultural production methods and enhancing the quality and efficiency of agricultural development, presenting a new vision for agricultural modernization. Across the country, efforts are being made to seize the farming season, actively exploring new pathways for digital and intelligent technologies to empower spring planting, thereby strengthening the foundation for ensuring food security and promoting rural revitalization.
These technologies can help address the issue of "who will farm the land." Smart agriculture expands the application scenarios for technological products such as drones and robots. Through data sensing, intelligent decision-making, and automated execution, tasks like smart agricultural machinery seeding, drone pesticide spraying and fertilizing, precise pest and disease identification, and integrated water-fertilizer irrigation are achieved. This shifts spring farming tasks, which previously relied on human observation, calculation, and labor, to AI and machines, reducing the agricultural sector's reliance on manual labor across the entire chain, effectively increasing the attractiveness of agriculture and drawing more people to engage in agricultural production and management.
Digital and intelligent technologies can enhance agricultural production efficiency. Utilizing technologies like satellite remote sensing and IoT sensors enables real-time monitoring of data such as soil moisture, crop growth, and weather changes. This facilitates on-demand sowing, precise irrigation, and scientific fertilization, promoting cost reduction, efficiency gains, and a reduction in agricultural pollution. For instance, intelligent irrigation in Shouguang, Shandong, has improved water and fertilizer utilization efficiency by 40%. Research indicates that smart technologies can reduce agricultural carbon emissions by 15% to 20%.
Furthermore, these technologies can scientifically predict and mitigate agricultural operational risks. Large models built on big data analysis can forecast meteorological disaster risks and market fluctuations. This guides farmers in adjusting their spring planting structure based on market conditions, helps guard against extreme weather risks, and drives a shift in spring farming decisions from experience-based judgment to data-driven approaches, aiding in solving the problems of agriculture being overly dependent on weather conditions and achieving increased yield without increased income.
However, there are still some obstacles to the effective empowerment of spring farming by digital and intelligent technologies. Firstly, there is a shortage of industrialized application scenarios for digital agriculture. Some agricultural AI models and equipment are primarily designed for large-scale field farming, exhibiting poor adaptability to scenarios like hilly and mountainous areas or specialized cultivation, and lack strong alignment with industrialized agricultural development. Secondly, the barrier to technology adoption is relatively high. High-end intelligent equipment, such as IoT sensors, has not yet been included in local agricultural machinery subsidy catalogs, hindering the large-scale promotion of smart technologies in agriculture. The difficulty for farmers to bear the initial investment and replacement costs somewhat limits the application of AI technologies in agricultural settings. Additionally, a regional "digital divide" impedes technology implementation. Some underdeveloped central and western regions and remote mountainous areas face limitations due to network infrastructure, characterized by poor coverage and unstable signals, making effective implementation of smart spring farming challenging.
Addressing these bottlenecks in smart spring farming and advancing the agricultural digital and intelligent transformation requires strategic measures. Firstly, there should be an acceleration in building industrialized application scenarios for digital agriculture. This involves establishing a technological system, application scenarios, and service system for smart agriculture, and supporting the design and development of agricultural apps and application scenarios for mobile terminals like smartphones. Encouraging AI companies to assist farmers in upgrading their production and management facilities and providing personalized solutions is also crucial. Secondly, measures to reduce the cost of technology adoption are needed. This includes appropriately increasing subsidies for AI companies involved in agricultural operations and providing support for the research, development, and use of agricultural intelligent equipment, core software, and management platforms. Incorporating high-end intelligent agricultural machinery products into subsidy catalogs, specifying subsidized products, models, and amounts, and gradually raising subsidy levels can lower costs for end-users. Finally, accelerating efforts to bridge the "digital divide" in central and western regions is essential. This involves increasing the number of signal base stations, particularly in rural areas of these regions, to improve internet speed and stability. Implementing programs to enhance the digital literacy and skills of the entire population, and making AI technology a key component of training for high-quality farmers, will help cultivate more technically proficient new-era farmers.
As the spring tide surges, it is a timely moment for determined progress. Empowering agricultural production with digital and intelligent technologies is a key lever for ensuring national food security and advancing agricultural modernization. There is a need to accelerate the integrated promotion of technological innovation and industrial innovation, making the intelligent empowerment of agriculture more potent and impactful, thereby painting a magnificent picture of a strong agricultural sector, beautiful countryside, and prosperous farmers.