Crop Yield Prediction in Precision Agriculture

Predicting crop yields is one of the most challenging tasks in agriculture. It plays an essential role decision making at global, regional, and field levels. Soil, meteorological, environmental, parameters are used to predict yield. A wide variety support models extract significant features for prediction. In precision agriculture, monitoring (sensing technologies), management information systems, variable rate technologies, responses inter- intravariability cropping systems all important. The benefits agriculture involve increasing yield quality, while reducing environmental impact. Simulations help understand cumulative effects water nutrient deficiencies, pests, diseases, other conditions during growing season. Farm situ observations (Internet Things databases from sensors) together with existing provide opportunity both using “simpler” statistical methods or that already as extension, also enable potential use artificial intelligence. contrast, big data created tools collection capabilities able handle many indefinitely time space, i.e., they can be analysis meteorology, technology, soils, including characterizing different plant species.