Agricultural modernization involves the integration of industrial elements, such as chemical fertilizers, pesticides, and agricultural machinery, in replacing traditional agricultural factors and resources such as land and labor.
This process is known as agricultural industrialization, which has gradually become more diverse and widespread, particularly since the turn of the century.
Digital technology, such as sensors and machine learning technologies, has accelerated the trend of infiltration, integration, and substitution, giving agricultural industrialization distinctive features of the times.
The importance of agriculture in human survival and development cannot be overstated. However, with population growth and the effects of climate change, modern agriculture is facing various challenges. It is essential to improve the efficiency and quality of agricultural production, ensure food security, and reduce environmental pollution.
The development of artificial intelligence technology is a new change that the agricultural industry is experiencing, and the promotion of intelligent agricultural applications has become an inevitable trend for future agricultural development.
The industrial characteristics of modern agriculture are ubiquitous in every link and scene of the agricultural production chain, from the field to the table. The production process of chemical fertilizers, pesticides, mechanical inputs, post-production product processing, storage, transportation, and sales are all industrial elements present in modern agriculture.
This gradual and long-term process has given rise to agricultural industrialization, which has diversified over time.
With the development of sensors and machine learning technologies, agricultural production has become increasingly intelligent.
Intelligent agriculture collects significant amounts of data and analyzes various factors such as crop growth conditions, soil moisture, and weather changes to provide scientific decision support and intelligent management solutions to maximize the efficiency and quality of agricultural production.
The main technologies used in intelligent agriculture include the Internet of Things (IoT), machine learning, cloud computing, and natural language processing.
IoT realizes real-time monitoring and data collection, machine learning predicts the growth status of crops and pests and diseases based on historical data, cloud computing provides more efficient and convenient management means for agricultural production, and natural language processing helps farmers make intelligent decisions and management.
The digital economy impacts all aspects of agriculture, rural areas, and farmers, involving multiple dimensions of production, life, and ecology. The promotion of rural revitalization requires not only enhancing the penetration rate of digital technology in agriculture but also making up for the digital economy's shortcomings in agricultural and rural development.
In the current complex and changing external environment, the development of the digital economy should make a difference in four aspects: ensuring national food security, increasing farmers' income, enhancing agricultural competitiveness, and achieving sustainable agricultural development.
In conclusion, the integration of industrial elements, including digital technology, in modern agriculture has given rise to agricultural industrialization. This process is characterized by gradualness, diversification, and long-term effects.
The promotion of intelligent agricultural applications through the use of IoT, machine learning, cloud computing, and natural language processing has become an inevitable trend for future agricultural development.
The development of the digital economy is critical to ensure national food security, increasing farmers' income, enhancing agricultural competitiveness, and achieving sustainable agricultural development.
