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Machine learning may also provide predictions to farmers at the point of need, such as the contents of plant-available nitrogen in soil, to guide fertilization planning. [59] As more agriculture becomes ever more digital, machine learning will underpin efficient and precise farming with less manual labour.
The environmental data are most often climate data (e.g. temperature, precipitation), but can include other variables such as soil type, water depth, and land cover. SDMs are used in several research areas in conservation biology , ecology and evolution .
Predictive learning is a machine learning (ML) technique where an artificial intelligence model is fed new data to develop an understanding of its environment, capabilities, and limitations. This technique finds application in many areas, including neuroscience , business , robotics , and computer vision .
In agriculture, a soil test commonly refers to the analysis of a soil sample to determine nutrient content, composition, and other characteristics such as the acidity or pH level. A soil test can determine fertility , or the expected growth potential of the soil which indicates nutrient deficiencies, potential toxicities from excessive ...
There is one novel machine learning framework for fire prediction, which represents a significant contribution to computational sustainability in the field of environmental monitoring. The model, centered on the identification of specific ignitions likely to lead to large fires, provides a more straightforward and interpretable alternative to ...
The Water Erosion Prediction Project (WEPP) model is a physically based erosion simulation model built on the fundamentals of hydrology, plant science, hydraulics, and erosion mechanics. [ 1 ] [ 2 ] The model was developed by an interagency team of scientists to replace the Universal Soil Loss Equation (USLE) and has been widely used in the ...
Recent technological advancements, such as drones, and satellite imagery, have enabled the collection of extensive data on soil health, weather patterns, crop growth, and pest activity. This data is analyzed to improve agricultural efficiency, identify patterns and trends, detect issues early, and minimize potential losses.
A hydrologic model is a simplification of a real-world system (e.g., surface water, soil water, wetland, groundwater, estuary) that aids in understanding, predicting, and managing water resources. Both the flow and quality of water are commonly studied using hydrologic models.