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The agricultural cycle is the annual cycle of activities related to the growth and harvest of a crop (plant). These activities include loosening the soil, seeding, special watering, moving plants when they grow bigger, and harvesting, among others. Without these activities, a crop cannot be grown.
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 ...
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
(pl.) aboiteaux A sluice or conduit built beneath a coastal dike, with a hinged gate or a one-way valve that closes during high tide, preventing salt water from flowing into the sluice and flooding the land behind the dike, but remains open during low tide, allowing fresh water precipitation and irrigation runoff to drain from the land into the sea; or a method of land reclamation which relies ...
Agriculture encompasses crop and livestock production, aquaculture, and forestry for food and non-food products. [1] Agriculture was a key factor in the rise of sedentary human civilization, whereby farming of domesticated species created food surpluses that enabled people to live in the cities. While humans started gathering grains at least ...
Digital agriculture encompasses a wide range of technologies, most of which have multiple applications along the agricultural value chain. These technologies include, but are not limited to: Cloud computing/big data analysis tools [21] Artificial intelligence; Machine learning; Distributed ledger technologies, including blockchain and smart ...
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Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]