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Big Data in Smart Farming

Big Data in Smart Farming

    Big Data in Smart Farming

Big data is expected to cause changes to both the scope and the organization of farming.

31 Jul, 2020

Big Data and Smart Farming are new evolving and developingconcepts. There has been a significant trend to consider the application of Big Data techniques and methods to agriculture as a major opportunity for application of the technology stack.

Big data applications in farming are not strictly about primary production, but play a major role in improving the efficiency of the entire supply chain and alleviating food security concerns.

Big data is the focus of indepth, advanced, game-changing business analytics, at a scale and speed that the old approach of copying and cleansing all of it into a data warehouse is no longer appropriate.  Applications of Big Data in agriculture include benchmarking, sensor deployment and analytics. Big Data provides predictive insights to future outcomes of farming ,drive real-time operational decisions, and reinvent business processes for faster, innovative action and game-changing business models .

 

FARM PROCESSES

Primary Business Processes are those involved in the creation of the product, its marketing and delivery to the buyer. Supporting Business Processes facilitate the development, deployment and maintenance of resources required in primary processes. The business processes of farming significantly differ between different types of production, e.g. livestock farming, arable farming and greenhouse cultivation. A common feature is that agricultural production is depending on natural conditions, such as climate, soil, pests, diseases and weather.

 

FARM MANAGEMENT

Management or control processes ensure that the business process objectives are achieved, even if disturbances occur. The basic idea of control is the introduction of a controller that measures system behaviour and corrects if measurements are not compliant with system objectives.

As a consequence, the basic management functions are • Sensing and monitoring: measurement of the actual performance of the farm processes. This can be done manually by a human observer or automated by using sensing technologies such as sensors or satellites. In addition, external data can be acquired to complement direct observations.

• Analysis and decision making compare measurements with the norms that specify the desired performance, signals deviations, and decides on the appropriate intervention to remove the signalled disturbances.

• Intervention: plans and implements the chosen intervention to correct the farm processes' performance.

Big data is expected to cause changes to both the scope and the organization of farming. While there are doubts about whether farmers' knowledge is about to be replaced by algorithms, Big Data applications are likely to change the way farms are operated and managed. Key areas of change are real-time forecasting, tracking of physical items, and reinventing business processes. Wider uptake of Big Data is likely to change both farm structures and the wider food chain in unexplored ways as what happened with the wider adoption of the tractor and the introduction of pesticides in the 1950s.

Global issues such as food security and safety, sustainability, and as a result efficiency improvement are tried to be addressed by Big Data applications. These issues make that the scope of Big Data applications extends far beyond farming alone, but covers the entire supply chain. Sensors and robots producing also non-traditional data such as images and videos provide many machine-generated data. Social media is an important source for human-sourced data. These big amounts of data provide access to explicit information and decisionmaking capabilities at a level that was not possible before.

 

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