Dhahi Al-Shammari
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View article: Are pulses really more variable than cereals? A comprehensive analysis of within-field yield variability across Australia
Are pulses really more variable than cereals? A comprehensive analysis of within-field yield variability across Australia Open
Purpose In Australia, pulse crops are underutilised relative to cereals, with a consensus that this is largely attributed to pulses exhibiting greater yield variability than cereals. However, the variability indicators used have typically …
View article: Incorporation of mechanistic model outputs as features for data-driven models for yield prediction: a case study on wheat and chickpea
Incorporation of mechanistic model outputs as features for data-driven models for yield prediction: a case study on wheat and chickpea Open
Introduction Context Data-driven models (DDMs) are increasingly used for crop yield prediction due to their ability to capture complex patterns and relationships. DDMs rely heavily on data inputs to provide predictions. Despite their effec…
View article: Combining Sentinel 1, Sentinel 2 and MODIS data for major winter crop type classification over the Murray Darling Basin in Australia
Combining Sentinel 1, Sentinel 2 and MODIS data for major winter crop type classification over the Murray Darling Basin in Australia Open
Crop type classification is an essential task in agriculture that has been studied widely since the emergence of remote sensing technologies. Accurate crop mapping can help assist decision-making related to storage, marketing, and other pr…
View article: E-Agriculture Planning Tool for Supporting Smallholder Cocoa Intensification Using Remotely Sensed Data
E-Agriculture Planning Tool for Supporting Smallholder Cocoa Intensification Using Remotely Sensed Data Open
Remote sensing approaches are often used to monitor land cover change. However, the small physical size (about 1–2 hectare area) of smallholder orchards and the cultivation of cocoa (Theobroma cocoa L.) under shade trees make the use of ma…
View article: Machine Learning Optimised Hyperspectral Remote Sensing Retrieves Cotton Nitrogen Status
Machine Learning Optimised Hyperspectral Remote Sensing Retrieves Cotton Nitrogen Status Open
Hyperspectral imaging spectrometers mounted on unmanned aerial vehicle (UAV) can capture high spatial and spectral resolution to provide cotton crop nitrogen status for precision agriculture. The aim of this research was to explore machine…
View article: Changes in Mesopotamian Wetlands: Investigations Using Diverse Remote Sensing Datasets
Changes in Mesopotamian Wetlands: Investigations Using Diverse Remote Sensing Datasets Open
Early civilizations have inhabited stable-water-resourced areas that supported living needs and activities, including agriculture. The Mesopotamian marshes, recognised as the most ancient human-inhabited area (~6000 years ago) and refuge o…
View article: Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery
Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery Open
A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and…
View article: A screening method to detect <scp>BYDV</scp> ‐ <scp>PAV</scp> resistance in cereals under glasshouse conditions
A screening method to detect <span>BYDV</span> ‐ <span>PAV</span> resistance in cereals under glasshouse conditions Open
A reliable method was developed to screen cereal crops for BYDV ‐ PAV resistance in glasshouse experiments. This also entailed the evaluation of traits associated with Barley yellow dwarf virus ( BYDV ) infection such as leaf discolouratio…