Smart agriculture refers to the use of information technology in farming to intelligently control the whole industrial chain of agricultural production, operation, management, and service, so as to achieve high quality, high efficiency, safety, and controllability of agricultural production. China attaches great importance to the development of smart agriculture, and the development of smart agriculture is the only way to realize modern agriculture.
What is the new technology in farming applications?
In the process of integration of primary, secondary, and tertiary industries, smart agriculture can break information bottlenecks and promote information flow and sharing.
The technical framework of smart technology in farming agriculture broadly includes three aspects: information perception, intelligent decision-making, and decision implementation.
Information perception includes obtaining various information related to agricultural production and operation through the agricultural Internet of Things, such as crops, soil, and other environments, as well as information about people engaged in agricultural production and even society.
Intelligent decision-making is the brain, which analyzes and processes various types of information and provides solutions for management or control, such as expert systems.
Decision implementation then proceeds based on the results of the analysis, including business strategies, planting schemes, environmental regulation, or the manipulation of farm machinery (e.g., drones, and seeders).
While information perception and decision implementation often rely on hardware devices, intelligent decision-making is mainly about data and knowledge processing and computation. The background of today’s era of big data, cloud computing, and the Internet of Things has put forward higher requirements for intelligent decision-making.
Big data is the basis of smart agriculture, and information perception is the source of agricultural data.
In a narrow sense, agricultural data refers to biophysical information about crops themselves and the environment outside of people. However, agricultural production activities are inseparable from human activities, and producers’ own experiences and the markets constituted by consumers will all have an impact on production.
Therefore, in a broad sense, agricultural data also includes social information such as people engaged in agricultural production, social environment, and market dynamics.
Biophysical information perception includes spatial information perception and ground information perception.
The former mainly includes remote sensing technology in farming, satellite positioning technology in farming, and geographic information technology in farming (“3S” technology).
Based on remote sensing technology in farming, spatial information such as planting area, crop growth, flooding, pest and disease situation, and soil and crop nutrition can be obtained.
Satellite positioning technology in farming is based on which the precise location of equipment can be obtained and can be used for mobile positioning of agricultural machinery.
Geographic information technology in farming, on the other hand, gives an intuitive way to manage data.
The latter is mainly a sensing technology in farming involved in agricultural IoT, obtaining data such as soil and air temperature and humidity, light intensity, CO2 concentration, wind speed, soil salinity, etc.
In addition to this, there are sensor technologies for quick measurement of plant nutrients, soil fertility, and quality of agricultural products through visible/near-infrared spectroscopy and near-infrared spectroscopy.
Ground sensing also includes setting up cameras or infrared monitors in the field, etc., which can be remotely observed by users and used to improve planting transparency and enhance consumer confidence.
Low power consumption, low cost, and stable performance sensors are the key to obtaining reliable data in the long term and are precisely the bottleneck of the current promotion and application of agricultural IoT.
Social information perception includes agricultural market demand, agricultural prices, agricultural policies, and grower experience.
At present, under the development trend of international integration of the agricultural market, agricultural enterprises, associations, cooperatives, and other economic cooperation organizations have more urgent needs for market and technical information such as what to plant before production with high efficiency, what kind of agricultural materials in production with high quality and good price, and who to sell to after production, etc. This information is of great significance to the development of modern agriculture in China, especially large-scale agriculture, order agriculture, and agriculture with local characteristics.
Agricultural product demand information is the soul of the agricultural market. Capturing market information conveniently and accurately, grasping market trends, and adjusting production and sales directions in a timely manner are the main goals of perceiving social information.
An automatic collection based on crawler technology in farming is an important way to perceive market prices through the network and can be used for weekly price forecasting of agricultural products, etc.
And the grain yield forecasting model with input-occupancy-output technology in farming as the core considers a variety of agricultural inputs that embody social information.
Data transmission is based on communication networks that connect scattered devices or subsystems with independent functions and communicate data according to prescribed network protocols to realize the sharing of hardware and software resources of distributed systems and the integrated management and control of systems.
At the technical level, many domestic research institutes and companies can realize different transmission methods. Which data transmission method is adopted for actual application needs to be determined based on the farming environment, program requirements, cost constraints, and other factors.
Agricultural big data covers a large amount of data generated from agricultural production itself and the whole chain of pre-production, post-production, processing, and sales of agricultural products.
The cost of acquiring various agricultural data is still relatively high, there is a lack of mature products, and different users have different needs. How to integrate biophysical and social information and provide corresponding information services for different stakeholders such as farmers, government and businessmen is still a challenge to be met in the future.
Intelligent decision-making technology in farming is the core of smart agriculture, which is the process of generating value from data, covering all aspects of agricultural production from pre-production planning, in-production planting management, and environmental control to post-production storage, processing, transportation, and sales.
Pre-production planning technology in farming includes demand analysis and planting plan recommendations.
Mid-production planting management includes intelligent decision support in environmental regulation (for facility agriculture), fertilization, dosing, irrigation, etc.
Post-production inventory control of agricultural products, transportation vehicle deployment, distribution processing, distribution center location, etc., all require intelligent computing methods to provide decision support.
Research on decision support technologies for crop production management has focused on crop growth simulation and various types of expert systems.
The crop management expert system is a specific application of the expert system in the field of agriculture, which generally contains a knowledge base composed of experience, information, data, and results of authoritative agricultural experts, and can use their knowledge to simulate the thinking methods of agricultural experts to make judgments and reasoning in order to reach the conclusion of agricultural production problems.
As agriculture enters the era of big data, agricultural intelligent decision-making shifts to a big data-driven approach and is reflected in all aspects of agricultural production.
The algorithms and presentation of agricultural intelligent decision technology in farming support systems need to be iterated accordingly. With the relatively easy access to data and the common use of smartphones, agricultural expert systems, which used to be used offline only on computers and were criticized as “electronic dictionaries”, have ushered in a new era of development.
Intelligent agricultural machinery and management software is the presentation form after information perception and intelligent decision-making.
With the aging and decreasing number of agricultural workers in China, the application of intelligent agricultural equipment is an inevitable trend. Agricultural intelligent equipment includes equipment serving various operations such as fertilization, dosing, irrigation, pruning, harvesting, seeding, and environmental regulation.
Foreign intelligent agricultural equipment is more advanced and widely used, mainly intelligent navigation, automatic driving, variable fertilization, variable spraying, and other functions. With the country’s attention and investment in the research and development of agricultural intelligent equipment, several domestic institutions have carried out relevant research, including automatic navigation, variable fertilization, precision spraying, seeding, rice planting, spraying, weeding, and harvesting.
In addition, China’s agricultural aviation operations are increasing year by year, and the field of operation is gradually expanding. In addition to applying medicine to food crops, horticultural crops, and cash crops, plant growth pollination and other operations are also carried out.
Agricultural operation equipment precision control technology in farming is developed in developed countries under the conditions of large-scale mechanization, while China’s crop production geographical environmental conditions vary greatly, so the choice of technology in farming and equipment can not be applied uniformly. The development of precision farm mechanization adapted to China’s national conditions must be continued and gradually improved according to local conditions.
With the development of intelligent technology in farming, virtual agricultural systems similar to digital twins in the industry are expected to develop gradually. Due to the characteristics of China’s small farmer economy, asymmetric production and marketing information are major bottlenecks in agricultural development.
Unlike traditional record-based agricultural information systems, virtual agricultural management systems based on intelligent production scheduling and fine calculation of agricultural inputs can be combined with new business models such as agricultural trusteeship and shared farmland currently under development, which not only meets urban residents’ aspirations for an idyllic life but also partially solves the problem of lack of technology and sales channels in rural areas, while creating new jobs in rural areas and driving a new generation of young people to engage in agriculture.
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