Solution for Smart Agriculture Teaching and Training Center

1、 Construction background

With the rapid development of information technology, smart agriculture, as an important direction of agricultural modernization, is gradually changing the production mode of traditional agriculture. Smart agriculture integrates advanced technologies such as the Internet of Things, big data, artificial intelligence, and cloud computing, achieving precise, intelligent, and automated management of agricultural production, improving agricultural production efficiency, reducing resource consumption, and ensuring the quality and safety of agricultural products.

In the field of education, it is of great significance to establish smart agriculture training bases in order to cultivate high-quality talents that can meet the needs of agricultural development in the new era. The training base can provide students with practical operation platforms, enabling them to learn and master smart agricultural technologies in real production environments, and improve their practical and innovative abilities; At the same time, the training base can also provide technical training and continuing education services for agricultural practitioners, promoting the widespread application of smart agricultural technology in agricultural production.

2、 Construction Goal

（1） Talent cultivation objectives:

To build a smart agriculture talent training base that integrates teaching, practice, and training, providing practical teaching venues for students majoring in agriculture, enabling them to proficiently master the application and operation of smart agriculture technology, and cultivate high-quality agricultural professionals with innovative spirit and practical ability; Provide technical training services for agricultural practitioners to enhance their level of smart agricultural technology application and business capabilities.

（2） Technical demonstration objectives:

Showcasing advanced technologies and equipment in the field of smart agriculture, such as agricultural IoT monitoring systems, intelligent irrigation and fertilization systems, unmanned aerial vehicle (UAV) crop protection and farmland mapping, agricultural big data analysis and decision-making platforms, etc., to provide technical demonstrations for agricultural production in surrounding areas and promote the promotion and application of smart agriculture technologies.

（3） Research and innovation goals:

Build a smart agriculture research and innovation platform, encourage teachers and students to carry out research projects related to smart agriculture, explore new models and technologies for the development of smart agriculture, solve practical problems in agricultural production, and provide technical support and theoretical basis for the development of smart agriculture.

3、 Construction principles

（1） Progressiveness principle:

Introducing advanced smart agriculture technologies and equipment from both domestic and international sources, ensuring that the technical level of the training base is at the forefront of the industry, and enabling students and trainees to access the latest agricultural technology achievements.

（2） Principle of practicality:

Based on the actual needs of agricultural production and talent cultivation goals, select practical, easy to operate and maintain technologies and equipment to ensure that the training base can truly serve agricultural production and talent cultivation.

（3） Principle of openness:

The training base should not only be open to students and teachers of the school, but also to surrounding agricultural colleges, agricultural enterprises, and agricultural practitioners to achieve resource sharing and improve the utilization rate of the training base.

（4） Principles of Sustainable Development:

In the construction process, full consideration is given to the rational utilization of resources and environmental protection, and energy-saving and environmentally friendly technologies and equipment are adopted to achieve the sustainable development of the training base.

4、 Overall Architecture

（1） Overall technical architecture

（2） Overall Technical Architecture Description

1. Infrastructure

The basic layer mainly includes three parts: agricultural IoT infrastructure, communication network, and cloud computing. Among them, the agricultural Internet of Things infrastructure includes monitoring devices such as sensors and videos, as well as resources such as computing, storage, networking, operating systems, and basic application software; The construction of communication networks relies on existing infrastructure, including wireless networks, broadband networks, etc. Cloud computing includes cloud storage and cloud computing environment.

2. Business data and interface specifications

The business data interface specification layer mainly includes the Internet agricultural data acquisition interface specification, the Internet of Things agricultural data acquisition interface specification, and the government and enterprise agricultural data acquisition interface specification, which respectively solve the problems of automatic acquisition of agricultural website information on the Internet, agricultural data collection based on the Internet of Things, and agricultural data integration of agricultural government departments and enterprises.

3. Application platform layer

The platform layer provides unified functional support for various applications that meet the needs of smart agriculture business, including application support and security support capabilities. Based on the actual needs of smart agriculture, the application layerBuild 9 application service systems on 1 major platform. This includes: agricultural IoT system, agricultural pest and disease intelligent monitoring and management system, expert and knowledge base system, agricultural and livestock product quality and safety traceability system, agricultural management system, visual monitoring system, agricultural intelligent irrigation system, agricultural product trading system, and agricultural big data intelligent decision analysis system.

4. Data Management Center

At the data management level, various services for data table operations are encapsulated, concealing the complexity of database operations and improving the portability of different database transformations; The data management layer implements operations with the database, including transaction control, security, control of database connections, and load balancing. Based on different business needs, establish relevant basic databases and specialized business databases to form unstructured, semi-structured, and structured agricultural data management centers. Unified management, sharing and exchange of multi-source heterogeneous agricultural data, meeting the information exchange and service requirements of various agricultural businesses, providing data support for upper level business applications.

5. User layer

By encapsulating business components and objects, the interface appears simpler in terms of representation; Interface representation is the graphical interface provided by the software for the client sideWeb interfaces, with consistent processing logic, are implemented by calling the business logic of business components and objects. The user layer refers to the main service objects: managers, producers, teachers, and students.

6. Guarantee support system

The construction of each layer of the overall architecture requires extensive and comprehensive support and guarantee, including policy support system, standard specification system, operation management system, and security guarantee system. These architectures run through all aspects of the platform construction, ensuring the secure and efficient operation of the platform.

7. System deployment and security

The overall design of the scheme includes a description of the system operating environment, security access restrictions, identity authentication, access control, communication integrity, and confidentiality. Ensure system security and stable operation from multiple perspectives such as application security, software security, hardware security, network security, and database security.

5、 Construction content

（1） Smart Breeding Training Area

1. Construction of smart planting areas

(1) Construction of intelligent greenhouse

Construct intelligent greenhouses (such as glass greenhouses and plastic greenhouses) equipped with advanced environmental monitoring and control systems to achieve automatic monitoring and precise regulation of environmental parameters such as temperature, humidity, lighting, and carbon dioxide concentration inside the greenhouse. Adopting advanced planting techniques such as soilless cultivation and three-dimensional cultivation, we plant economic crops such as vegetables, flowers, and fruits, showcasing an intelligent and efficient greenhouse planting mode.

Install IoT sensors to monitor environmental parameters such as temperature, humidity, light, carbon dioxide concentration, soil moisture, and soil fertility in the greenhouse in real time, and automatically adjust and control environmental parameters based on crop growth needs through an intelligent control system.

Build an intelligent irrigation and fertilization system that achieves precise irrigation and fertilization based on soil moisture and crop growth stages, improving water resources and fertilizer utilization efficiency.

(2) Precision planting construction in Datian

Plan the field planting area, install soil moisture monitoring system, meteorological monitoring station, intelligent irrigation system, unmanned aerial vehicle plant protection system, etc., to achieve precise management of field planting. Real time collection of soil, meteorological and other data through sensors, utilizing big data analysis and decision support systems to guide the production processes of crops such as sowing, fertilization, irrigation, pest control, etc., improving agricultural production efficiency and quality.

(3) Soil moisture warning management

ØSoil moisture information management: The system has the function of converting the soil moisture content measured by sensors into relative moisture content. Soil moisture information management enables timely reporting of soil moisture information and real-time monitoring of soil moisture.

ØSoil moisture warning model: It can set multi-level interval warnings based on the required soil moisture during crop growth, For example: over humid or saturated state (level 5), humid state (level 4), suitable humidity (level 3), arid state (level 2), extremely arid state (level 1). You can view the suitability of the current crop growth environment to guide growth.

ØReal time soil moisture warning function: Based on the soil moisture warning results, the warning function is provided, and the year-on-year and visible trends of soil moisture changes are displayed.

(4) Crop growth monitoring and management

Utilizing high-definition cameras, drones, and other devices to monitor crop growth in real-time, obtaining information on crop height, leaf area, and the occurrence of pests and diseases.

Using image recognition and artificial intelligence technology to analyze and process monitoring data, achieve intelligent diagnosis of crop growth status and early warning of pests and diseases, and provide corresponding management suggestions.

(5) Intelligent monitoring and management platform for agricultural pests and diseases

vIntelligent real-time monitoring of pests and diseases

Automatic recognition and analysis of crop pest and disease images, achieving functions such as automatic generation and field management measures, enabling field workers to carry out timely field management operations; Identify, analyze, and process pest and disease images reported by smartphones to provide information feedback. Smartphones receive feedback on pest and disease types, occurrence situations, and corresponding field management measures; Realize the dynamic display function of pests and diseases in various regions.

vIntelligent recognition of pests and diseases

Based on artificial intelligence image classification algorithms, photos of crops can be uploaded, and the AI platform will automatically identify the types of crops and identify the types of pests and diseases in crop leaves.

The currently supported crop types include apples, potatoes, tomatoes, grapes, and corn.

The identifiable pests and diseases include: black spot disease, gray spot disease, leaf spot disease, black rot disease, leaf spot disease, brown spot disease, wheel spot disease, scab disease, early disease, late disease, leaf blight disease, pine rust disease, powdery mildew, and so on.

(6) Intelligent Irrigation Control System

The system mainly consists of sensor modules, control units, and actuators. Sensors monitor soil moisture, temperature, and meteorological data in real-time (such as rainfall, light intensity, wind speed, etc.). These data are sent to the control unit through wireless transmission technology, and the control unit accurately calculates the required irrigation amount and time for crops based on preset irrigation strategies and data analysis results, and sends instructions to the executing agency. The executing mechanism is usually various irrigation equipment, such as water pumps, solenoid valves, etc. They automatically turn on or off according to instructions, accurately controlling the start and stop of irrigation.

2. Construction of Smart Aquaculture Zones

(1) Smart Aquaculture Zone

Build smart aquaculture ponds equipped with water quality monitoring equipment, dissolved oxygen control systems, intelligent feeding machines, etc., to achieve intelligent management of aquaculture. By monitoring water quality parameters in real-time, automatically adjusting the breeding environment, accurately feeding feed, improving the yield and quality of aquaculture, and reducing breeding costs and risks.

(2) Construction of smart livestock breeding facilities

Construct intelligent livestock and poultry breeding facilities equipped with automatic ventilation, temperature control, humidity control, and lighting control systems to provide suitable growth environments for livestock and poultry.

Install automated breeding equipment, such as automatic feeding systems, automatic drinking water systems, manure cleaning systems, etc., to improve breeding efficiency and reduce labor intensity.

(3) Monitoring and early warning of aquaculture environment

Install various sensors in the breeding house to monitor environmental parameters such as ammonia, hydrogen sulfide, temperature and humidity in real time, as well as physiological parameters such as animal activity, feed intake, and water intake.

By analyzing data and establishing models, intelligent early warning of the breeding environment and real-time assessment of the health status of livestock and poultry can be achieved, and problems that arise during the breeding process can be discovered and resolved in a timely manner.

3. Agricultural Production Management System

The main purpose is to achieve informatization of agricultural production and management, record agricultural planting and production processes. The system functions include: basic resource management, input management, crop file management, crop planting management, production and processing management, batch management, packaging management, etc.

4. Quality and safety traceability system for agricultural and livestock products

Establish a quality traceability system for agricultural and livestock products, utilizing technologies such as the Internet of Things and QR codes to record and track information throughout the entire process of planting, breeding, growing, harvesting, slaughtering, processing, and sales of agricultural, livestock, poultry, and aquatic products, achieving traceability of product quality and safeguarding consumer rights. Provide effective mechanisms and means for the supervision and management of agricultural product quality and safety for the agricultural and animal husbandry departments.

（2） Agricultural Big Data and Intelligent Decision Center

1. Data integration and summarization

Multi system data source data extraction collects and integrates various agricultural data from different data sources into a central database, such as meteorological data, soil data, production data, sales data, agricultural product price data, etc.

Integrate various sensor data from the smart planting training area and smart breeding training area, as well as external data such as meteorological data, environmental data, video data, pest and disease data, drone data, integrated water and fertilizer data, traceability data, market data, third-party monitoring data, etc., and achieve real-time data collection and transmission through wired and wireless communication networks.

2. Data analysis on agricultural big data platform

Build an agricultural big data storage and management platform, using distributed storage technology to efficiently store and manage massive agricultural data. Develop big data analysis and mining tools, using algorithms such as data statistical analysis, machine learning, and deep learning to analyze and process agricultural data, and uncover the patterns and values behind the data.

(1) Basic Information Statistics and Analysis

uAdministrative Information Special Database

Including park planning, construction progress, management organization situation, park location, area, industry types, output value, and information on promoting agriculture.

uEnterprise or Base Information Special Database

Including enterprise information, planting information, variety distribution, yield, agricultural activities, production and processing technology, brand, marketing data, etc.

uSocial Service Special Topic Library

Including agricultural expert knowledge base, characteristic planting and processing technology data of the park, innovative achievements of the park, etc; Unified defense and governance, green production and service information, etc.

(2) Analysis of Crop Agricultural Growth Environment

Using sensors, drones, remote sensing and other technologies to obtain data on crop growth environment, such as soil moisture, temperature, light, carbon dioxide concentration, etc., to monitor, analyze and predict crop growth environment, in order to provide suitable growth conditions and planting decisions.

(3) Crop growth monitoring and evaluation

Using sensors, drones, remote sensing and other technologies to collect data on crop growth environment, growth status, etc., predicting crop growth trends, probability of disease and pest occurrence through data analysis, and providing decision support for agricultural production.

(4) Diagnosis and Control Analysis of Diseases and Pests

Using image recognition and big data analysis techniques, identify and analyze images of agricultural pests and diseases, determine the type, degree, and spread range of pests and diseases, and provide timely prevention and control measures and suggestions to reduce the impact of pests and diseases on crop yield.

(5) Agricultural Production Analysis

Refine the management of agricultural production processes, including planting plans, resource allocation, production tracking, and other aspects. By comprehensively monitoring and managing the agricultural production process, agricultural production efficiency can be improved, production costs can be reduced, and the quality of agricultural products can be enhanced.

(6) Business analysis

By combining agricultural market information, price indices, sales data, agricultural product traceability and other related data, analyzing the sales, cost-effectiveness, input-output ratio, market price fluctuations and market feedback of agricultural products, analyzing business strategies, and providing economic profit and loss analysis and risk assessment for agricultural producers, we can understand the competitive advantages and disadvantages of enterprises and formulate more reasonable business plans.

(7) Agricultural Market Forecast

Using big data analysis technology to analyze and predict historical agricultural market data, including price trends, market demand, supply situation, etc., to provide agricultural producers with market trend and forecast information to help them make more reasonable production and sales decisions.

(8) Analysis of Agricultural Resources

By combining data on land resources, water resources, etc., we aim to achieve refined management of agricultural resources, improve resource utilization efficiency, and reduce production costs.

(9) Monitoring and optimization of agricultural inputs

By analyzing data, monitoring and controlling the use of agricultural inputs, we aim to improve the efficiency of agricultural production and the quality and safety of agricultural products

(10) Industry analysis

Production monitoring, material monitoring, supplier monitoring, and park quality monitoring.

(11) Traceability management

Traceability product statistics, traceability QR code statistics, traceability QR code area ranking, QR code distribution map, agricultural product quality and safety monitoring and early warning, providing comprehensive agricultural product quality and safety monitoring and risk warning to ensure the quality and safety of agricultural products.

3. Multidimensional decision-making cockpit

The visual monitoring system provides comprehensive and multi-dimensional agricultural big data visualization display for leaders, supporting macro control of planting area, planting structure, production progress, disaster warning, yield prediction, etc., and providing a basis for scientific decision-making.

4. Agricultural Technology Knowledge Resource Library

Gathering massive and diverse agricultural technology knowledge is a key information hub for the development of modern agriculture. Covering the entire process of planting, from seed selection, sowing, field management to pest and disease control, including fertilization techniques tailored to different crop characteristics, as well as diagnosis and green prevention methods for various diseases; In terms of breeding, it includes livestock and poultry breeding management, disease prevention and control, and even water quality regulation in aquaculture. To provide convenient channels for farmers, agricultural practitioners, and other agricultural related personnel to obtain professional agricultural technology knowledge, promote the dissemination and application of agricultural knowledge, and provide solid knowledge support for the modernization of agriculture and rural revitalization.

On the basis of the system's built-in knowledge base, the system can interface with mainstream big model AI systems such as deepseek and ERNIE Bot. Provide customers with smarter and richer agricultural technology knowledge.

（3） Agricultural Product Processing and E-commerce Training Area

1. Demonstration of agricultural product processing

Constructing small-scale agricultural product processing production lines, showcasing processing techniques such as cleaning, sorting, packaging, preservation, and drying of agricultural products, and cultivating students' practical operational abilities in agricultural product processing technology.

2. Construction of agricultural product e-commerce platform

Build an agricultural product e-commerce platform, carry out online sales of agricultural products, enable students to understand the operation mode and process of agricultural product e-commerce, and master e-commerce skills such as online marketing, customer service, logistics and distribution.

3. Quality testing and certification of agricultural products

Equip agricultural product quality testing equipment to detect pesticide residues, heavy metal content, microbial indicators, etc. in agricultural products to ensure their quality and safety. At the same time, conduct training on agricultural product quality certification to enable students to understand the standards and procedures for agricultural product certification.

（4） Drone Application Training Area

1. Construction of drone flight site

Plan and construct specialized drone flight sites to meet the needs of drone takeoff and landing, flight training, and operations. Set up safety protection facilities around the venue to ensure flight safety.

2. Equipment allocation for unmanned aerial vehicles

Purchase different types of unmanned aerial vehicles such as multi rotor and fixed wing, equipped with high-definition cameras, multispectral cameras, thermal imaging cameras and other mission payloads, to meet the needs of different application scenarios such as farmland surveying, crop growth monitoring, pest control, pesticide spraying, etc.

3. Training on drone control and data processing

Conduct training on drone control technology to enable students and trainees to master basic operational skills such as takeoff, landing, hovering, and route planning of drones.

Provide training on drone data processing, including image stitching, data analysis, report generation, etc., to enable students to proficiently use the data obtained by drones for agricultural production analysis and decision-making.

（5） Development and Achievement Display of Smart Agriculture Technology

1. Demonstration of Smart Agriculture Technology Development

Constructing a smart agriculture science and technology museum, through physical displays, model demonstrations, multimedia interactions, and other forms, to popularize the concept, technology, development history, and application achievements of smart agriculture to the public, and to enhance public awareness and attention to smart agriculture.

2. Display of smart agriculture achievements

Display the achievements of the training base in the research and development, application promotion, and talent cultivation of smart agriculture technology in the exhibition area, including scientific research project results, award certificates, student works, etc., to enhance the influence and demonstration role of the training base.

6、 Our advantages

(1) The software system is based on Wenlang RunchengMature objectInternet universal platformFlexible and configurable functions, stable and reliable system operation, and excellent performance.

(2)Integrated video surveillance functionSeamless integration of video and environmental monitoring, achieving true visual monitoring management.

(3)highly integratedThe front-end monitoring equipment adopts the integrated greenhouse intelligent monitoring terminal independently developed by Wenlang Runcheng, with high integration, no need for special installation, plug and play, and easy maintenance in the later stage.

(4)High hardware reliability, Front end equipment with a protection level of IP66 or above, dustproof and waterproof, can be installed outdoors, and is suitable for harsh environments such as rainy and humid weather.

(5)Complete software system functionsSpecially customized system interface for agricultural customers, user-friendly and easy to operate. Integrated video surveillance function enables convenient remote visual management.

(6)Good compatibilityBy configuring, customers can connect their existing collection and control devices from different manufacturers and brands. Flexible configuration functions can meet the personalized needs of different customers.

(7)Support access from various terminals such as computers, mobile phones, pads, etcProvide smartphone client software that can monitor greenhouse environments anytime and anywhere. integrationWeChat, mobile SMSThrough various display and interaction methods, information transmission is timely and convenient.

(8)Good system scalabilitySupport secondary development and can be deeply integrated with other existing information systems of customers.

◆ Demonstration Center:Wenlang Runcheng Smart Agriculture Cloud Platform Demonstration

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