Deep learning has emerged as a potential tool for crop yield prediction, allowing the model
to automatically extract features and learn from the datasets. Meanwhile, smart farming …

Land-use and land-cover change (LULCC) are of importance in natural resource
management, environmental modelling and assessment, and agricultural production …

Empirical vegetation indices derived from spectral reflectance data are widely used in
remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf …

Agriculture provides humanity with food, fibers, fuel, and raw materials that are paramount
for human livelihood. Today, this role must be satisfied within a context of environmental …

There is a rapid increase in the adoption of emerging technologies like the Internet of Things
(IoT), Unmanned Aerial Vehicles (UAV), Internet of Underground Things (IoUT), Data …

This study investigates whether coupling crop modeling and machine learning (ML)
improves corn yield predictions in the US Corn Belt. The main objectives are to explore …

Accurate yield estimation and optimised nitrogen management is essential in agriculture.
Remote sensing (RS) systems are being more widely used in building decision support tools …

The global population is expected to reach 9.8 billion by 2050. There is an exponential
growth of food production to meet the needs of the growing population. However, the limited …

Dense time series of Landsat 8 and Sentinel-2 imagery are creating exciting new
opportunities to monitor, map, and characterize temporal dynamics in land surface …

The increasing availability of satellite data at higher spatial, temporal and spectral
resolutions is enabling new applications in agriculture and economic development …