Unfortunately, because your browser version is too low to get the best browsing experience, it is recommended to download and install Google Chrome!
With the advent of the age of science and technology, with the Internet, multimedia, and cloud computing as the main representatives, China's information development process has been promoted, and cloud computing has also largely promoted the progress of computing capabilities. Imagine that if the farm management staff can grasp the weather change data, market supply and demand data, crop growth data, etc. at any time, the farm management staff and agro-technical experts can observe the real scene and related data on the farm without leaving home, and accurately judge whether the crop The fertilization, watering or pesticide application can not only avoid the decline in production due to natural factors, but also avoid economic losses to the farm due to the imbalance of supply and demand in the market.
In the era of agricultural big data , not only can we establish a comprehensive data platform to regulate agricultural production , but we can also record and analyze the dynamic changes in the process of agricultural cultivation and farming, and the circulation of agricultural products. To make agricultural development efficient and orderly.
Second, the role of agricultural big data platform :
After years of development, it has developed a multi-level and multi-field agricultural information system, constructed many different levels of data resources for different fields, and formed a huge wealth of information resources. However, for reasons such as interest, these data lacked unified standards and specifications before each other, lacked information sharing, and disconnected information resources from business, which inevitably led to low data utilization and scattered information redundancy. The emergence of the intelligent management service platform of Aoke Meiyitian's helper farm will be able to better standardize data standards and play a huge role in improving the level of farm management.
Application of agricultural big data platform:
1. Agricultural resource management:
Based on GIS and remote sensing technology, a digital map of the farm is established to make scientific decision-making and refined management of the production and planting land on the farm.
Based on the geographic basic information provided by the Global Positioning System (GPS), a digital map of the farm is established based on the geographic information system (GIS). Use remote sensing (RS) technology to perceive the field information (soil quality, crops) in the electronic map, comprehensively grasp the scope of agricultural planting land, understand the real-time comprehensive information of soil conditions, atmospheric environment and other areas in the area and analyze the planting area through the difference analysis of information Divided into different management areas, targeted planning, real-time query, analysis, decision-making functions of agricultural resources in the plantation industry.
2. Crop production management:
Integrate traditional statistical data and agricultural resource management information to conduct targeted planting management of crops in different plots of the farm.
Quantitatively obtain information on environmental factors (such as soil fertility, water content, seedling conditions, diseases and insect pests) that affect crop growth in different areas with large differences in planting influencing factors, analyze the reasons that affect the block yield differences, and adopt technically feasible and economical " Accurate agriculture " that implements effective farming measures, treats them differently, and implements them as needed.
3. Crop monitoring and estimation:
Use remote sensing (RS) technology to monitor crop growth, take effective measures in a timely manner as needed, and accurately estimate crop yield and harvest information based on comprehensive analysis of various data.
4. Early warning of diseases and insect pests:
Use GIS, remote sensing, hyperspectral analysis and other technologies to analyze, predict and control plant diseases and insect pests.
5. Agricultural product quality and safety management:
Integrate the origin environment, production archives, and inspection data to form agricultural product quality and safety traceability data.
6. Origin environmental data:
Use remote sensing (RS), sensors and other technical means to comprehensively grasp the environmental data of agricultural production areas and form historical records.
7. Production archive data:
Agricultural product production records, recording various agricultural operations information during the growth process of agricultural products.
8. Agricultural product testing data:
Record enterprise qualification, test report, product quality certification and other information.
As China's basic industry, agriculture is facing severe challenges such as increasing demand for agricultural products, scarce resources, frequent disasters caused by climate change, fragile ecological security, and continued decline in biodiversity. It is consolidating agriculture with the agricultural Internet of Things and cloud computing technology as its core. The foundation of informatization, improving agricultural informatization services supported by big data, opening up a new situation in smart agriculture, and realizing the leap-forward development of agricultural modernization and informatization.
Therefore, the application of big data technology to agricultural production can greatly promote the development and progress of agricultural information service technology, and can also greatly promote the overall development of modern agriculture in China.
July 31, 2017
Chengdu Xinxin Electronic Technology Co., Ltd. Editorial Department
Editor: Little Devil in Gourd