Agricultural big data will play a role in these six areas

[China Agricultural Machinery Industry News] Agricultural big data is like a reservoir being built, that is, the water in this reservoir is not only a living water, but also must have a source; if the source is not enough, there must be a source and application. In recent times, agricultural big data has become a hot red.

Agricultural big data will play a role in these six areas

This will benefit from the official issuance of the Agricultural and Rural Big Data Pilot Program. It is understood that the "Program" aims to use the big data concept and technology to innovate the ideas and methods of agricultural monitoring and statistics work, give full play to the role of local agricultural departments and enterprises, scientific research units, and trade associations, and promote the production, management and management of big data in agriculture. The application of services and services forms a batch of results that can be replicated and promoted.

However, for big data, many people still don't understand it.

What is agricultural big data?

Relevant data show that agricultural big data is a combination of agricultural characteristics, seasonality, diversity, periodicity and other characteristics, resulting in a wide range of sources, diverse types, complex structures, potential value, and difficult to apply the usual methods of processing and analysis. Data collection. It retains the basic features of big data itself, such as size, variety, value density, speed, veracity, and complexity. And the flow of information within agriculture has been extended and deepened.

There is a metaphor in the outside world. Agricultural big data is like a reservoir being built. That is, the water in this reservoir is not only a living water, but also a source. If the source is not enough, there must be a source and application.

Why do you need agricultural big data?

Most of the traditional agriculture is “seeing the sky to eat.” If there is no mitigation of climate disasters and effective prevention during the growth of plants, it is very likely that “the particles are not collected”.

With the agricultural big data, not only can the future trend of the environment be predicted by the algorithm model, the future climate trend can be obtained, and the current growth of the crop, the plot information, etc. can be obtained by analyzing the real-time environmental analysis; in addition, the environment can be analyzed. The overall direction of the data, get planting advice and management guidance.

In other words, the use of agricultural big data for planting can be greatly improved from the agricultural production to the agricultural market, agricultural management and other chains.

What areas can big data be applied to?

At present, many institutions and enterprises have conducted preliminary explorations. In the future, agricultural big data will play a role in these six areas:

(1) Ecological environment data, including meteorological, hydrological, soil and pest and disease, animal epidemic data. These data are the main basis for the adjustment of agricultural water use and agricultural product input in daily operations of agriculture. Accurately mastering these data will help to plant, raise, and reduce resource waste and cost.

(2) Agricultural technology and agricultural materials circulation data. Mastering agricultural technology can guarantee agricultural products and high yield, and based on the analysis of agricultural materials circulation data, it provides a basis for agricultural operators to choose agricultural products. The circulation data of seeds and seedlings can also determine the scale of production of a certain type of agricultural products, which is the basis for adjusting the scale.

(3) Agricultural product prices and agricultural product circulation data. The adjustment of production scale and the adjustment of raw product categories must be informed beforehand about the price of agricultural products and the production and sales of major producing areas. In addition, through the B2B, B2C e-commerce platform to promote the supply and demand information of agricultural products docking, can expand the sales market and increase the price of agricultural products.

(4) Land transfer data. Through the land transfer between the supply and demand sides of the information, the transfer rate will be more favorable, and one side will be reduced and one side will find the land.

(5) Traceable data on the quality of agricultural products. Through the above-mentioned integration of agricultural materials use data and production circulation data, traceable data from farm to table can be constructed to eliminate consumers' doubts about the quality of agricultural products and increase the purchase rate of agricultural products.

(6) Agricultural operator credit data. The above data can be incorporated into the credit information system of banks, rural credit cooperatives and insurance institutions, as a credit basis for issuing loans and setting up agricultural insurance, thereby promoting the integration of finance and agriculture.

(Original title: What is the big picture of agricultural big data?)