Rice yield depends on factors including variety, weather, field management, nutrient and water availability. We analyzed important drivers of yield variability at the field scale, and developed yield forecast models for crops in the temperate irrigated rice growing region of Australia. We fused a time-series of Sentinel1 and Sentinel-2 satellite remote sensing imagery, spatial weather data and field management information. Rice phenology was predicted using previously reported models. Higher yields were associated with early flowering, higher chlorophyll indices and higher temperatures around flowering. Successive rice cropping in the same field was associated with lower yield (p<0.001). After running a series of leave-one-year-out cross validation experiments, final models were trained using 2018–2022 data, and were applied to predicting the yield of 1580 fields (43,700 hectares) from an independent season with challenging conditions (2023). Models which aggregated remote sensing and weather time-series data to phenological periods provided more accurate predictions than models that aggregated these predictors to calendar periods. The accuracy of forecast models improved as the growing season progressed, reaching RMSE=1.6 t/ha and Lin’s concordance correlation coefficient (LCCC) of 0.67 30 days after flowering at the field level. Explainability was provided using the SHAP method, revealing the likely drivers of yield variability overall, and of individual fields.

Macadamia yield forecast models were trained with a large set of commercial yield data (10 years, 1,156 records). Predictors included remote sensing and weather data, aggregated spatially to macadamia block boundaries, and temporally to quarterly intervals. Errors were typically around 23% at the block level, and 10% at the region level. Much of the yield variability yield was predicted even for orchards excluded from training data. At least 400-500 training data points were needed to minimize error. Best results were obtained with a fusion of weather and remote sensing data, aggregated over 8 quarterly periods from 2 years before harvest.

This study presents a comprehensive evaluation of seasonal, locational, and varietal variations in canopy reflectance responses in 315 commercial citrus blocks from three major growing regions in Australia. The dataset includes three different citrus types (Mandarin, Navel, Valencia) and 26 varieties. The aim is to utilize this combined information to better understand yield variation and develop improved forecasting models. Landsat satellite data spanning from October 2006 to February 2021 (1419 tiles) were used to derive reflectance values, and calculate four vegetation indices (NDVI, GNDVI, LSWI, and GCVI), for each citrus block. These indices were then analyzed alongside corresponding yield data, which consisted of 3660 individual yield records dating back to 2007. Two temporal resolutions were incorporated as predictors: spatio-temporal vegetation index time series (TS) aggregated every two months and annual time series of historical block-yield records. Six statistical and machine learning algorithms were calibrated using a leave-one-year-out cross-validation approach (LOYO CV) and validated for one-year forward prediction over a five-year period (2017–2021). The results highlight significant yield variations across years, alternate bearing patterns, and spatio-temporal changes in reflectance profiles influenced by seasonal conditions, varietal characteristics, and locations. The support vector machine (SVM) algorithm with a radial basis function kernel consistently outperformed other algorithms, indicating a non-linear relationship between citrus yield and predictors. The SVM model achieved an RMSE of 15.5 T ha⁻¹ , R² of 0.88, MAE of 12.1 T ha⁻¹ , and MAPE of 29% in predicting block-yield across farms, varieties, and seasons. These prediction accuracy metrics demonstrate an improvement over current forecasting methods. Notably, the proposed approach utilizes freely available imagery, provides forecasts between two to nine months before harvest, and eliminates the need for infield counting of fruit load for image calibration. This approach provides an improved method for understanding seasonal yield variation and quantifying citrus block-yield, offering valuable insights for growers in harvest logistics, labor allocation, and resource management.

Regional maps of rice fields provided early in each growing season facilitate production estimates, planning around harvest logistics, marketing and targeted agronomic recommendations. This work develops maps of all irrigated rice fields in New South Wales, Australia. Classification models were trained on reference maps from the 2019 and 2020 harvest seasons. Model predictions were tested against a reference rice map from the 2021 harvest season, covering 60,000 km ² . The random forest algorithm was used, with features from aggregated time-series of Sentinel-2 imagery. A sequence of maps were generated at intervals of 15 days, from early to late in the growing season, with accuracy assessed at each time. The maps achieved 95% overall accuracy against point samples at 16 January 2021 ( ≈80 days after sowing). Pixel-based F1-scores against the reference map were above 80% for the 1, 16 and 31 January classified maps.

Daily calibrated multispectral imagery (Planet Fusion) of an olive irrigation deficit trial was used to assess the degree and speed to which vegetation indices indicate water stress. We developed normalization techniques to increase sensitivity to differences across a grove. The normalized difference vegetation index (NDVI) was able to significantly detect differences between the control and deficit treatments for the Arbequina variety. For the Picual variety, the green red vegetation index (GRVI) was the best indicator. Though multispectral imagery is not as quick at indicating irrigation deficits as in-field sensor data, it is complementary in being able to capture the spatial variability of water stress.