Search results
Results from the WOW.Com Content Network
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.
Digital soil mapping (DSM) in soil science, also referred to as predictive soil mapping [1] or pedometric mapping, is the computer-assisted production of digital maps of soil types and soil properties. Soil mapping, in general, involves the creation and population of spatial soil information by the use of field and laboratory observational ...
The extent to which such modelled data reflect real-world species distributions will depend on a number of factors, including the nature, complexity, and accuracy of the models used and the quality of the available environmental data layers; the availability of sufficient and reliable species distribution data as model input; and the influence ...
Most data files are adapted from UCI Machine Learning Repository data, some are collected from the literature. treated for missing values, numerical attributes only, different percentages of anomalies, labels 1000+ files ARFF: Anomaly detection: 2016 (possibly updated with new datasets and/or results) [332] Campos et al.
Local regression or local polynomial regression, [1] also known as moving regression, [2] is a generalization of the moving average and polynomial regression. [3] Its most common methods, initially developed for scatterplot smoothing, are LOESS (locally estimated scatterplot smoothing) and LOWESS (locally weighted scatterplot smoothing), both pronounced / ˈ l oʊ ɛ s / LOH-ess.
Machine learning algorithms can analyze data from sensors and drones to optimize resource allocation in agriculture. By providing insights into soil health, moisture levels, and crop growth, these algorithms help farmers make informed decisions to improve productivity and sustainability.
In agriculture, data mining utilizes data science techniques to analyze large volumes of agricultural data. 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 ...
Predictions based on soil surveys serve as a basis for judgment about land use and management for areas ranging from small tracts to regions of several million acres. These predictions, however, must be evaluated along with economic, social, and environmental considerations before they can be used to make valid recommendations for land use and ...