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  2. Information and communications technology in agriculture

    en.wikipedia.org/wiki/Information_and...

    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 ...

  3. Precision agriculture - Wikipedia

    en.wikipedia.org/wiki/Precision_agriculture

    [4] [5] The goal of precision agriculture research is to define a decision support system for whole farm management with the goal of optimizing returns on inputs while preserving resources. [6] [7] Among these many approaches is a phytogeomorphological approach which ties multi-year crop growth stability/characteristics to topological terrain ...

  4. Digital agriculture - Wikipedia

    en.wikipedia.org/wiki/Digital_agriculture

    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 ...

  5. Deep learning - Wikipedia

    en.wikipedia.org/wiki/Deep_learning

    Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.

  6. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition. [88]

  7. Topological deep learning - Wikipedia

    en.wikipedia.org/wiki/Topological_Deep_Learning

    Motivated by the modular nature of deep neural networks, initial work in TDL drew inspiration from topological data analysis, and aimed to make the resulting descriptors amenable to integration into deep-learning models. This led to work defining new layers for deep neural networks.

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  9. List of statistical software - Wikipedia

    en.wikipedia.org/wiki/List_of_statistical_software

    OpenNN – A software library written in the programming language C++ which implements neural networks, a main area of deep learning research; Orange, a data mining, machine learning, and bioinformatics software; Pandas – High-performance computing (HPC) data structures and data analysis tools for Python in Python and Cython (statsmodels ...