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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 ...
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]
Agricultural technology can be products, services or applications derived from agriculture that improve various input and output processes. [1] [2] Advances in agricultural science, agronomy, and agricultural engineering have led to applied developments in agricultural technology. [3] [4]
Caffe is being used in academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Yahoo! has also integrated Caffe with Apache Spark to create CaffeOnSpark, a distributed deep learning framework.
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.
The term "sustainable agriculture" was defined in 1977 by the USDA as an integrated system of plant and animal production practices having a site-specific application that will, over the long term: [13] satisfy human food and fiber needs; enhance environmental quality and the natural resource base upon which the agriculture economy depends
Machine learning can be used to spot early-warning signs of disasters and environmental issues, possibly including natural pandemics, [343] [344] earthquakes, [345] [346] [347] landslides, [348] heavy rainfall, [349] long-term water supply vulnerability, [350] tipping-points of ecosystem collapse, [351] cyanobacterial bloom outbreaks, [352] and ...
The 2010s marked a significant shift in the development of AI, driven by the advent of deep learning and neural networks. [31] Open-source deep learning frameworks such as TensorFlow (developed by Google Brain) and PyTorch (developed by Facebook's AI Research Lab) revolutionized the AI landscape by making complex deep learning models more ...