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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 ...
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]
The economic and environmental benefits of precision agriculture have also been confirmed in China, but China is lagging behind countries such as Europe and the United States because the Chinese agricultural system is characterized by small-scale family-run farms, which makes the adoption rate of precision agriculture lower than other countries.
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]
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.
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 ...
Recognizing simple digit images is the most classic application of LeNet as it was created because of that. Yann LeCun et al. created LeNet-1 in 1989. The paper Backpropagation Applied to Handwritten Zip Code Recognition [ 4 ] demonstrates how such constraints can be integrated into a backpropagation network through the architecture of the network.