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 discolouration, reduction in growth, biomass and yield traits, and percentage of virus‐infected plants, using enzyme‐linked immunosorbent assay (ELISA) and tissue blot immunoassay (TBIA). Four glasshouse experiments were conducted with eight wheat, barley and oat varieties inoculated with BYDV‐PAV at the 2‐leaf stage, using different numbers of viruliferous aphids and different inoculation periods and temperatures. Inoculation with 5–10 viruliferous aphids per plant for 4 days led to a high percentage of infection in susceptible varieties, indicating that this is an effective BYDV screening method when selecting for resistance in cereal crops. For barley and oat, visual evaluation of symptoms is considered adequate for assessing BYDV resistance. However, for wheat it is necessary to evaluate BYDV resistance by ELISA/TBIA tests and plant biomass (at early stage) and grain number and yield (at late stage) measurements.

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 does not require processing in multiple platforms to complete the classification steps. A time series of Normalised Difference Vegetation Index (NDVI) imagery were generated from Landsat 8 Surface Reflectance Tier 1 (L8SR) and processed using Fourier transformation. This was used to produce the harmonised-NDVI (H-NDVI) from the original NDVI, and then phase and amplitude values were generated from the H-NDVI to visualise active cotton in the targeted fields. Random Forest (RF) models were built to classify cotton at early, mid and late growth stages to assess the ability of the model to classify cotton as the season progresses, with phase, amplitude and other individual bands as predictors. Results obtained from leave-one-season-out cross validation (LOSOCV) indicated that Overall Accuracy (OA), Kappa, Producer’s Accuracies (PA) and User’s Accuracy (UA), increased significantly when adding amplitude and phase as predictor variables to the model, than prediction using H-NDVI or raw bands only. Commission and omission errors were reduced significantly as the season progressed and more in-season imagery was available. The methodology proposed in this study can map cotton crops accurately based on the reconstruction of the unique cotton reflectance trajectory through time. This study confirms the importance of phenological metrics in improving in-season cotton fields mapping across eastern Australia. This model can be used in conjunction with other datasets to forecast yield based on the mapped crop type for improved decision making related to supply chain logistics and seasonal outlooks for production.

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 learning use with hyperspectral datacubes over agricultural fields. Hyperspectral imagery was collected over a mature cotton crop, which had high spatial (~5.2 cm) and spectral (5 nm) resolution over the spectral range 475–925 nm that allowed discrimination of individual crop rows and field features as well as a continuous spectral range for calculating derivative spectra. The nominal reflectance and its derivatives clearly highlighted the different treatment blocks and were strongly related to N concentration in leaf and petiole samples, both in traditional vegetation indices (e.g., Vogelman 1, R2 = 0.8) and novel combinations of spectra (R2 = 0.85). The key hyperspectral bands identified were at the red-edge inflection point (695–715 nm). Satellite multispectral was compared against the UAV hyperspectral remote sensing’s performance by testing the ability of Sentinel MSI to predict N concentration using the bands in VIS-NIR spectral region. The Sentinel 2A Green band (B4; mid-point 559.8 nm) explained the same amount of variation in N as the hyperspectral data and more than the Sentinel Red Edge Point 1 (B5; mid-point 704.9 nm) with the lower 10 m resolution Green band reporting an R2 = 0.85, compared with the R2 = 0.78 of downscaled Sentinel Red Edge Point 1 at 5 m. The remaining Sentinel bands explained much lower variation (maximum was NIR at R2 = 0.48). Investigation of the red edge peak region in the first derivative showed strong promise with RIDAmid (R2 = 0.81) being the best index. The machine learning approach narrowed the range of bands required to investigate plant condition over this trial site, greatly improved processing time and reduced processing complexity. While Sentinel performed well in this comparison and would be useful in a broadacre crop production context, the impact of pixel boundaries relative to a region of interest and coarse spatial and temporal resolution impacts its utility in a research capacity.

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 of rich biodiversity, have experienced dramatic changes during the past five decades, starting to fail in providing adequate environmental functioning and support of social communities as they used to for thousands of years. The aim of this study is to observe, analyse and report the extent of changes in these marshes from 1972 to 2020. Data from various remote sensing sources were acquired through Google Earth Engine (GEE) including climate variables, land cover, surface reflectance, and surface water occurrence collections. Results show a clear wetlands dynamism over time and a significant loss in marshlands extent, even though no significant long-term change was observed in lumped rainfall from 1982, and even during periods where no meteorological drought had been recorded. Human interventions have disturbed the ecosystems, which is evident when studying water occurrence changes. These show that the diversion of rivers and the building of a new drainage system caused the migration and spatiotemporal changes of marshlands. Nonetheless, restoration plans (after 2003) and strong wet conditions (period 2018 - 2020) have helped to recover the ecosystems, these have not led the marshlands to regain their former extent. Further studies should pay more attention to the drainage network within the study area as well as the neighboring regions and their impact on the streamflow that feeds the study area.

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