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.

Early tree crop yield forecasts are valuable to industry and to growers, as they inform improved harvest logistics, forward selling, insurance and marketing strategies. Previous work has demonstrated the utility of weather and particularly remote sensing data to forecast tree crop yield at the orchard block scale. In this work, such data were aggregated spatially to block boundaries, and temporally at quarterly intervals. Yield prediction models were trained with a large set of grower-supplied yield data (more than 10 years, 20 orchards, 200 blocks across the Australian growing regions, for a total of 1156 yield records). Yields were forecast three months before harvest begins, and were compared to actual yields. Errors were typically around 10% and 23% at the regional and block levels respectively. Errors in 2020 were higher in non-irrigated regions due to an extreme drought in east Australia. Models were able to describe much of the variability of yields even for orchards not included in the training data, but block-level prediction errors increased by 4.1% in this case. Bootstrap sampling was used to investigate data requirements. At least 400-500 training data points was needed to minimize prediction errors. Weather data alone did not produce satisfactory accuracy, fusing weather and remote sensing data produced the best results. Including predictor data from all 8 quarterly periods from the 2 years before harvest proved a good strategy. These results demonstrate the potential of tree crop forecasting using public spatio-temporal datasets, give guidance on data requirements and identify areas for further work.

The rise in global production of horticultural tree crops over the past few decades is driving technology-based innovation and research to promote productivity and efficiency. Although mango production is on the rise, application of the remote sensing technology is generally limited and the available study on retrieving mango phenology stages specifically, was focused on the application of optical data. We therefore sought to answer the questions; (1) can key phenology stages of mango be retrieved from radar (Sentinel-1) particularly due to the cloud related limitations of optical satellite remote sensing in the tropics? and (2) does weather have any effect on phenology? The study was conducted on a mango farm in the Yilo Krobo Municipal Area of Ghana. Time series analysis for radar vegetation index (RVI) values for 2018 – 2021 was used to retrieve three key phenology stages of mango namely; Start of Season (SoS), Peak of Season (PoS) and End of Season (EoS). Characteristic annual peaks (in April/May for the major season and October/November for the minor season) and troughs (in June/July for the major season and December/January for the minor season) in the phenology trend of mango were identified. Rainfall and temperature explained less than 2% and 14% of the variability respectively in mango phenology. The application of radar remote sensing provides a cutting edge technology in the assessment of mango phenology, particularly in the tropics where cloud cover is a big challenge. This study offers an opportunity for production efficiency in the mango value chain as understanding of the crop's phenology allows growers to manage farm and post-harvest operations.

Rice field management around maturity and harvest are some of the most difficult decisions growers face. Field drainage and harvest timing affect quality, yield, and post-harvest drying costs. These decisions are informed by grain moisture content (MC). Over three years, three sites and three varieties, we studied the field dry-down rate and time to optimal harvest MC. We showed that field-specific parameters significantly affected these characteristics, including rice variety, Nitrogen applied (NA), mid-season N uptake (NU) and dry matter (DM). Increased N and DM is associated with increased MC and thus delays time to harvest. We developed models based on linear regression and nonlinear machine learning (ML) algorithms, including parameters describing these field-specific conditions. Cross validation across the three years provided a realistic expectation of model prediction errors. A linear model with the addition of nonlinear predictors achieved competitive performance compared with more complex and less interpretable ML models. When MC was modeled as a function of days since heading, similar or better accuracy was achieved to using accumulated weather parameters. Moisture content was predicted with mean absolute error of 2.1 %. The predicted time from heading to harvest MC was improved by the inclusion of field-specific parameters (N and variety) from mean absolute error of 6.8 days to 5.7 days. The final linear regression model explained 80 % of the moisture variability in the dataset, and provided estimates of dry-down rates, moisture as a function of time, and time to reach harvest moisture. This study shows the importance of including field-specific parameters when estimating of rice harvest timing, and provides methods to model these effects.