Significant advances in the field of digital image processing have been achieved in recent
years using deep learning, which has significantly exceeded previous methods. Deep …

The normalized difference vegetation index (NDVI) is crucial to many sustainable
agricultural practices such as vegetation monitoring and health evaluation. However, optical …

Over large areas, land cover classification has conventionally been undertaken using
satellite time series. Typically temporal metric percentiles derived from single pixel location …

A significant amount of study has been conducted on food security forecasting, yet, few
systematic reviews of the literature in this context are available. Recently, Machine Learning …

Neural network models play an important role in crop extraction based on remote sensing
data. However, when dealing with high-dimensional remote sensing data, these models are …

Crop type mapping is regarded as an essential part of effective agricultural management.
Automated crop type mapping using remote sensing images is preferred for the consistent …

Thematic crop-type maps provide helpful information for the decision-makers to ensure
society's food security, control market prices, impose new export or import restrictions, and …

Spectral and spatial features in remotely sensed data play an irreplaceable role in
classifying crop types for precision agriculture. Despite the thriving establishment of the …

Credit card fraud is a common occurrence in today's society because the majority of us use
credit cards as a form of payment more frequently. This is the outcome of developments in …

Accurate monitoring of agricultural droughts in data-scarce areas remains a challenge due
to their intricate spatiotemporal patterns. Deep learning represents a promising approach for …