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The 2016 2nd International Digital Agriculture Forum with the theme of "Let Digital Serve Every Field" was held in Beijing.
The reporter found that this forum not only gathered experts and scholars in the fields of automation control and Internet of Things technology at home and abroad, but also attracted many agricultural enterprises to learn from the experience.
Facing fierce industry competition, more and more agricultural companies see the value of agricultural big data in actual production and hope to use digital agricultural technology to find more efficient service models to help agricultural producers achieve precise production.
"Agricultural material companies rely on selling fertilizers, pesticides, seeds and other agricultural material products.
It is difficult to meet the demand for large-scale planting and it is difficult to win in the competition.
" At the meeting, many agricultural material companies said that digital agriculture has accumulated and accumulated through big data.
The application can achieve precise control of agricultural production and help agricultural producers increase production and income.
Agricultural materials companies can use the data support provided by digital agriculture to improve the accuracy and efficiency of agrochemical services and create new competitiveness.
Digital agriculture is a new intelligent agricultural model based on agricultural big data.
At the meeting, Professor Li Daoliang of the School of Information and Electrical Engineering of China Agricultural University introduced that the current key applications of agricultural big data in agriculture mainly include four points: First, basic research, using high-yield crops as samples, and screening out through big data collection and analysis.
Varieties with the characteristics of high yield, strong stress resistance, and low resource consumption; the second is agricultural intelligent production, through farmland environmental monitoring and control, to achieve precise seeding, the optimization of agricultural resources allocation, and the prevention and control of diseases and insect pests; the third is the agricultural product market Forecasting and logistics; fourth is to strictly control the quality and safety of agricultural products.
Li Daoliang said that with the rapid development of sensors, smart mobile devices and the Internet, agricultural big data will show explosive growth.
According to Liu Zongbo, chairman of Beijing Kebaihongye Technology Co.
, Ltd.
, digital agriculture has been deployed and used in many places in China, and the benefits of big data to agriculture are unimaginable.
For example, with the help of digital agricultural technology, the income of one acre of cherry orchard can reach 200,000 yuan or more.
?????? The collection of agricultural big data should rely on professional Internet of Things equipment, such as rain gauges, humidity and temperature probes, wind speed and direction sensors, soil moisture sensors, plant growth measuring instruments and other hardware to collect and monitor farmland in real time.
Water flow, soil humidity, air temperature, air humidity, carbon dioxide concentration and other data are sent to the central controller.
With the help of wireless greenhouse automatic control system and drip irrigation system, the farmland temperature increase or sprinkler irrigation can be completed.
Provide precise support for the prevention of pests and diseases through professional data analysis to increase production and income.
Gu Zhu, a former data scientist at the National Laboratory of Aeronautics and Astronautics of the United States of America (NASA), introduced that he currently uses remote sensing technology to collect crops.
Big data has been used to increase production and income of various crops such as corn, grapes and pasture.
Taking grapes as an example, real-time monitoring of soil moisture and crop transpiration coefficients, as well as small-scale crop growth environments through remote sensing, can predict future precipitation and soil moisture changes, and set drip irrigation time and water volume to ensure grape quality.
Big data has also been applied in the forecast of plant diseases and insect pests.
Vitaly Ignatovich, general manager of Caipos Austria, said that through wind sensors, rainfall sensors, etc.
, the hourly weather forecast in a certain area can be accurately calculated with an accuracy of 85%.
Professor Zhao Zhanzhou of the Institute of Plant Protection of the Chinese Academy of Agricultural Sciences emphasized that the poor level of traditional pest prevention and control technology has caused farmers to instinctively abuse pesticides and fertilizers.
Digital agriculture can effectively improve the accuracy and efficiency of plant protection and promote the sound development of ecological agriculture.
It works very well.
However, Li Daoliang said that the large-scale application of digital agriculture in agricultural production still faces five major bottlenecks.
Including lack of big data, lack of long-term accumulation, lack of integration with industries, lack of necessary specifications, and lack of support from leading industries.
The reporter found that this forum not only gathered experts and scholars in the fields of automation control and Internet of Things technology at home and abroad, but also attracted many agricultural enterprises to learn from the experience.
Facing fierce industry competition, more and more agricultural companies see the value of agricultural big data in actual production and hope to use digital agricultural technology to find more efficient service models to help agricultural producers achieve precise production.
"Agricultural material companies rely on selling fertilizers, pesticides, seeds and other agricultural material products.
It is difficult to meet the demand for large-scale planting and it is difficult to win in the competition.
" At the meeting, many agricultural material companies said that digital agriculture has accumulated and accumulated through big data.
The application can achieve precise control of agricultural production and help agricultural producers increase production and income.
Agricultural materials companies can use the data support provided by digital agriculture to improve the accuracy and efficiency of agrochemical services and create new competitiveness.
Digital agriculture is a new intelligent agricultural model based on agricultural big data.
At the meeting, Professor Li Daoliang of the School of Information and Electrical Engineering of China Agricultural University introduced that the current key applications of agricultural big data in agriculture mainly include four points: First, basic research, using high-yield crops as samples, and screening out through big data collection and analysis.
Varieties with the characteristics of high yield, strong stress resistance, and low resource consumption; the second is agricultural intelligent production, through farmland environmental monitoring and control, to achieve precise seeding, the optimization of agricultural resources allocation, and the prevention and control of diseases and insect pests; the third is the agricultural product market Forecasting and logistics; fourth is to strictly control the quality and safety of agricultural products.
Li Daoliang said that with the rapid development of sensors, smart mobile devices and the Internet, agricultural big data will show explosive growth.
According to Liu Zongbo, chairman of Beijing Kebaihongye Technology Co.
, Ltd.
, digital agriculture has been deployed and used in many places in China, and the benefits of big data to agriculture are unimaginable.
For example, with the help of digital agricultural technology, the income of one acre of cherry orchard can reach 200,000 yuan or more.
?????? The collection of agricultural big data should rely on professional Internet of Things equipment, such as rain gauges, humidity and temperature probes, wind speed and direction sensors, soil moisture sensors, plant growth measuring instruments and other hardware to collect and monitor farmland in real time.
Water flow, soil humidity, air temperature, air humidity, carbon dioxide concentration and other data are sent to the central controller.
With the help of wireless greenhouse automatic control system and drip irrigation system, the farmland temperature increase or sprinkler irrigation can be completed.
Provide precise support for the prevention of pests and diseases through professional data analysis to increase production and income.
Gu Zhu, a former data scientist at the National Laboratory of Aeronautics and Astronautics of the United States of America (NASA), introduced that he currently uses remote sensing technology to collect crops.
Big data has been used to increase production and income of various crops such as corn, grapes and pasture.
Taking grapes as an example, real-time monitoring of soil moisture and crop transpiration coefficients, as well as small-scale crop growth environments through remote sensing, can predict future precipitation and soil moisture changes, and set drip irrigation time and water volume to ensure grape quality.
Big data has also been applied in the forecast of plant diseases and insect pests.
Vitaly Ignatovich, general manager of Caipos Austria, said that through wind sensors, rainfall sensors, etc.
, the hourly weather forecast in a certain area can be accurately calculated with an accuracy of 85%.
Professor Zhao Zhanzhou of the Institute of Plant Protection of the Chinese Academy of Agricultural Sciences emphasized that the poor level of traditional pest prevention and control technology has caused farmers to instinctively abuse pesticides and fertilizers.
Digital agriculture can effectively improve the accuracy and efficiency of plant protection and promote the sound development of ecological agriculture.
It works very well.
However, Li Daoliang said that the large-scale application of digital agriculture in agricultural production still faces five major bottlenecks.
Including lack of big data, lack of long-term accumulation, lack of integration with industries, lack of necessary specifications, and lack of support from leading industries.
