A great deal of data has been produced in agricultural production and scientific research. The integration, future mining and utilization of these data will play an extremely important role in the development of modern agriculture. If farmers can know the weather changes, market supply and demand, crop growth and other data in time, farmers and agricultural experts can observe the field situation and related data at home and accurately judge whether crops need fertilization, irrigation or pesticide application, which can not only avoid the decline in yield caused by natural factors, but also avoid the economic losses brought to farmers by market factors. At present, in many agricultural fields, such as precision agriculture, agricultural product circulation system, agrometeorological prediction, food security, pest prediction and prevention, soil control, animal and plant breeding, agricultural structure adjustment, agricultural product prices, agricultural and sideline products consumption, and small town construction, big data technology can be used for prediction and intervention. 4 Observing the application of big data in agriculture includes but is not limited to:
(1) precision agriculture. Precision agriculture is a new type of agriculture that combines information technology with agricultural production. It consists of six subsystems: global positioning system, farmland information collection system, farmland remote sensing monitoring system, farmland geographic information system, agricultural expert system and intelligent agricultural machinery system. Its core is to establish a perfect farmland geographic information system [3]. Based on the massive information obtained from different systems, big data provides a lot of opportunities for the development of precision agriculture. It can be used for soil fertility management, farmland boundary map management, yield distribution map management, accurate positioning of pest control methods and fertilization decision management.
(2) Agricultural product circulation system. Big data can provide relevant climate, price trends of agricultural products, traffic information of roads entering the city, and terminal consumption after system integration.
Demand and other related data, supplemented by the monitoring and evaluation data system of supermarket stalls, can judge the demand and price changes of agricultural products through the interpretation of these professional data.
(3) Agrometeorological forecast. By establishing weather identification models, and then comparing these models with the current climate conditions, and then using forecast analysis, the weather is predicted. In this case, the weather forecast takes longer and has higher accuracy.
(4) Environmental prediction. Environmental prediction needs to understand the complex interaction among crops, soil, water, animals, climate and weather in this area. Data of these different factors need to be collected for analysis,
Big data technology helps to integrate and improve massive data in different regions.
(5) Improve human health. By analyzing the interaction between people and the surrounding biological communities, including the genome of biological communities, animal nutrition and human nutrition status data, big data can better predict human health and happiness. By sequencing the genome of crops, we can qualitatively change the quality of crops, cultivate crops with higher nutritional value and improve the health level of human beings. At the same time, with the deep integration of big data and agriculture, and the improvement of infrastructure, information management mode and software technology, many problems that cannot be solved by traditional methods and technologies in the past will be solved.
Application of Big Data in Agriculture
A large amount of data will be produced in agricultural production and research, and the integration, future mining, utilization and development of these data will play a very important role in modern agriculture. If farmers can know the weather changes, market supply and demand, crop growth and other data in time, farmers and agricultural experts can observe the field situation and related data at home and accurately judge whether fertilization, irrigation and crop spraying are needed, which can not only avoid the decline in yield caused by natural factors, but also avoid the economic losses brought to farmers by market factors. At present, in precision agriculture, agricultural products circulation system, agricultural weather forecast, food security, pest prediction and control, soil management, animal and plant breeding, agricultural structure adjustment, agricultural products prices, agricultural and sideline products consumption, small town construction and many other agricultural fields can be predicted and intervened by data technology. 4 Application of observation data in agriculture, including but not limited to:
Precision agriculture (1). Precision agriculture is a new combination of agricultural information technology and agricultural production, which consists of six subsystems: global positioning system, farmland information collection system, farmland remote monitoring system, farmland geographic information system, agricultural expert system and intelligent agricultural system, and its core is to establish a perfect farmland geographic information system [3]. Big data, based on massive information obtained through different systems, provides great opportunities for the development of precision agriculture. It can be used for soil fertility management, field boundary map management, yield distribution map management, accurate positioning of pest control methods and fertilization decision management.
(2) Agricultural product circulation system. Big data can provide related climate, price trends of agricultural products with system integration, urban road traffic information, terminal consumption and so on.
Relevant data demand, supplemented by market supermarket monitoring and evaluation data system, can judge the demand and price change of agricultural products through the interpretation of these professional data. ..
(3) Agrometeorological forecast. By establishing weather identification models, and then comparing these models with the current climate conditions, and then using forecast analysis, the weather is predicted. In this case, the longer the forecast time, the more accurate the weather will be.
(4) Environmental prediction. Environmental prediction needs to understand the complex interaction among crops, soil, animals, water, climate and weather in the region. Data on these different factors need to be collected and analyzed,
Big data technology helps to improve the integration of massive data in different regions.
(5) Improve human health. By analyzing the interaction between human beings and the surrounding biomes, including biome genome, animal nutrition and human nutrition status data, big data can better predict human health and well-being. And sequencing the genome of crops can change the quality of crops, cultivate crops with higher nutritional value and improve the health level of human beings. At the same time, with the development of integration, the improvement of infrastructure, information management mode, big data software technology and the deepening of agricultural problems, problems solved by traditional methods and technologies will be solved easily.