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Machine learning may also provide predictions to farmers at the point of need, such as the contents of plant-available nitrogen in soil, to guide fertilization planning. [59] As more agriculture becomes ever more digital, machine learning will underpin efficient and precise farming with less manual labour.
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]
Some useful resources for learning about e-agriculture in practice are the World Bank's e-sourcebook ICT in agriculture – connecting smallholder farmers to knowledge, networks and institutions (2011), [2] ICT uses for inclusive value chains (2013), [3] ICT uses for inclusive value chains (2013) [4] and Success stories on information and ...
Many AI platforms use Wikipedia data, [272] mainly for training machine learning applications. There is research and development of various artificial intelligence applications for Wikipedia such as for identifying outdated sentences, [ 273 ] detecting covert vandalism [ 274 ] or recommending articles and tasks to new editors.
In agriculture, data mining is the use of data science techniques to analyze large volumes of agricultural data. Recent advancements in technology, such as sensors, drones, and satellite imagery, have enabled the collection of large amounts of data on soil health, weather patterns, crop growth, and pest activity.
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
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Learning/ advisory/ extension services: applications that use mobile technology to deliver or retrieve agricultural/agronomy information and advice. Geospatial applications: Applications enabling data and information related to geography and space to be managed, processed, and visualized.