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  2. Precision agriculture - Wikipedia

    en.wikipedia.org/wiki/Precision_agriculture

    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.

  3. Species distribution modelling - Wikipedia

    en.wikipedia.org/wiki/Species_Distribution_Modelling

    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 .

  4. Soil test - Wikipedia

    en.wikipedia.org/wiki/Soil_test

    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 ...

  5. Hydrological model - Wikipedia

    en.wikipedia.org/wiki/Hydrological_model

    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.

  6. Water retention curve - Wikipedia

    en.wikipedia.org/wiki/Water_retention_curve

    Water retention curve is the relationship between the water content, θ, and the soil water potential, ψ. The soil moisture curve is characteristic for different types of soil, and is also called the soil moisture characteristic. It is used to predict the soil water storage, water supply to the plants (field capacity) and soil aggregate stability.

  7. Data mining in agriculture - Wikipedia

    en.wikipedia.org/wiki/Data_mining_in_agriculture

    In agriculture, data mining is the use of data science techniques to analyze large volumes of agricultural data. Recent advancements in technology, such as sensors, drones, and satellite imagery, have enabled the collection of large amounts of data on soil health, weather patterns, crop growth, and pest activity.

  8. WEPP - Wikipedia

    en.wikipedia.org/wiki/WEPP

    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 ...

  9. Predictive learning - Wikipedia

    en.wikipedia.org/wiki/Predictive_learning

    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 .