A Machine Learning and Big Data Approach
Keywords:
Agriculture Data, Sensor for Environment, Camera and Images, Environment Sensors, Weather Data, Machinery Data, Buildings Data, Data from Bears, Water Reservoir Data, Image Data, Cattle Location, Data from Satellites, Data from Plane Image, Data from Other Source based on Reporting System.Abstract
Agriculture produces the food, fiber, and feed essential for humanity even though it encompasses merely 4% of the Earth’s land surface. Nevertheless, through the consumption of water and energy, greenhouse gas emissions, land-use changes, and direct and indirect effects on biodiversity, agriculture plays a major role in the depletion of natural resources and environmental deterioration. In the long run, climate and ecological changes may also have adverse effects on agricultural production, thus threatening food security and consequently human well-being. In this context, guaranteeing and securing wise and judicious natural resource consumption is a key issue and challenge for realizing sustainable development.
These opposing trends have spurred research into eco-efficient agriculture, that is, maximizing agricultural output (crops, animal protein, etc.) using the least possible amount of natural resources. They have also motivated the demand for tools capable of monitoring the state of agricultural ecosystems and enabling forecasting and decision support. The aim could be to assess and promote a more suitable input–output equilibrium and adopt agricultural practices compatible with society’s ecological sustainability objectives. Such tools rely on data ecosystems for continuous monitoring, Big Data architectures for data collection, and smart data pipelines for enabling quick real-time decision support or for batch production using advanced Machine Learning capabilities.
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