At present, the agricultural industry is in a stage of transition from small-scale farmers to large-scale, mechanized, and intensive operations, which provides a huge space for the application of agricultural big data.
Agricultural big data focuses on agricultural fields covering sub-industries such as planting, forestry, and animal husbandry, involving feed production, fertilizer production, agricultural machinery production, agricultural product processing and other related upstream and downstream industries, and integrates statistical data, import and export data, Links such as price data and even the macroeconomic background data of meteorological data are cross-industry, cross-professional, and cross-business data analysis, mining and visualization.
“These data will become the brains of agricultural decision-making,” Zhonghuan Yida IoT engineer and system expert believes that in the future, in addition to making recommendations for planting, agricultural big data will also provide growers with what to plant and how to plant to maximize profits. To avoid the homogenization risk of similar products getting together in the market, “including market forecasting, farmers’ credit reporting, agricultural finance, etc., all need to be based on data analysis.”
Agricultural big data is a collection of data that has a wide range of sources, diverse types, complex structures, and potential value after integrating its own characteristics such as agricultural regionality, seasonality, diversity, and periodicity, and is difficult to process and analyze by ordinary methods. The information flow within agriculture has been extended and deepened. As the speed and capacity of computers continue to increase, the ability to collect and use information about all aspects of productive agriculture will explode, forever changing the health of humans and crops.
Agricultural big data
At present, the agricultural industry is in a stage of transition from small-scale farmers to large-scale, mechanized, and intensive operations, which provides a huge space for the application of agricultural big data.
Agricultural big data focuses on agricultural fields covering sub-industries such as planting, forestry, and animal husbandry, involving feed production, fertilizer production, agricultural machinery production, agricultural product processing and other related upstream and downstream industries, and integrates statistical data, import and export data, Links such as price data and even the macroeconomic background data of meteorological data are cross-industry, cross-professional, and cross-business data analysis, mining and visualization.
“These data will become the brains of agricultural decision-making,” Zhonghuan Yida IoT engineer and system expert believes that in the future, in addition to making recommendations for planting, agricultural big data will also provide growers with what to plant and how to plant to maximize profits. To avoid the homogenization risk of similar products getting together in the market, “including market forecasting, farmers’ credit reporting, agricultural finance, etc., all need to be based on data analysis.”
Agricultural big data is a collection of data that has a wide range of sources, diverse types, complex structures, and potential value after integrating its own characteristics such as agricultural regionality, seasonality, diversity, and periodicity, and is difficult to process and analyze by ordinary methods. The information flow within agriculture has been extended and deepened. As the speed and capacity of computers continue to increase, the ability to collect and use information about all aspects of productive agriculture will explode, forever changing the health of humans and crops.