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.

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.

Accurate, non-destructive forecasting of carrot yield is difcult due to its subterranean growing habit. Furthermore, the timing of forecasting usually occurs when the crop is mature, limiting the opportunity to implement alternative management decisions to improve yield (during the growing season). This study aims to improve the accuracy of carrot yield forecasting by exploring time series and multivariate approaches. Using Sentinel-2 satellite imagery in three Australian vegetable regions, we established a time series of carrot phenological stages (PhS) from 'days after sowing' (DAS) to enhance prediction timing. Numerous vegetation indices (VIs) were analyzed to derive temporal growth patterns. Correlations with yield at diferent PhS were established. Although the average root yield (t ha⁻¹) did not signifcantly difer across the regions, the temporal VI signatures, indicating diferent regional crop growth trends, did vary as well as the PhS at when the maximum correlation with yield occurred (PhS_R2max) with two of the regions producing a delayed PhS_R2max (i.e. 90–130 DAS). The best multivariate model was identifed at 70 DAS, extending the forecasting window before harvest between 20 to 60 days. The performance of this model was validated with new crops producing an average error of 16.9 t ha⁻¹ (27% of total yield). These results demonstrate the potential of the model at such early stage under varying growing conditions ofering growers and stakeholders the chance to optimize farming practices, make informed decisions on selling, harvesting, and labor planning, and adopt precision agriculture methods.