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In agriculture, data mining utilizes data science techniques to analyze large volumes of agricultural data. Recent technological advancements, such as drones, and satellite imagery, have enabled the collection of extensive data on soil health, weather patterns, crop growth, and pest activity. This data is analyzed to improve agricultural ...
Precision agriculture (PA) is a management strategy that gathers, processes and analyzes temporal, spatial and individual plant and animal data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of ...
The E-agriculture in Action series of publications, by FAO-ITU, that provides guidance on emerging technologies and how it could be used to address some of the challenges in agriculture through documenting case studies. E-agriculture in Action: Big Data for Agriculture [22] E-agriculture in Action: Blockchain for Agriculture [23]
Digital agriculture, sometimes known as smart farming or e-agriculture, [1] are tools that digitally collect, store, analyze, and share electronic data and/or information in agriculture. The Food and Agriculture Organization of the United Nations has described the digitalization process of agriculture as the digital agricultural revolution . [ 2 ]
The early research was on reliable methods for collecting yield statistics of principal food crops. Further research in sampling and statistics was initiated and this became a Statistical Branch in 1945. The branch soon acquired international recognition as a centre for research and training in the field of Agricultural Statistics.
Data mining in agriculture – Application of data mining techniques to agriculture; Food technology – Academic discipline regarding the preparation of foods; Information and communications technology in agriculture – Agricultural and rural development; Optical sorting – Automated sorting of solid products using cameras and/or lasers.
Data farming is the process of using designed computational experiments to “grow” data, which can then be analyzed using statistical and visualization techniques to obtain insight into complex systems. These methods can be applied to any computational model. Data farming differs from Data mining, as the following metaphors indicate:
A systematic review focuses on a specific research question to identify, appraise, select, and synthesize all high-quality research evidence and arguments relevant to that question. A meta-analysis is typically a systematic review using statistical methods to effectively combine the data used on all selected studies to produce a more reliable ...