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.

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.

Globally, the production and trade of fruits and nuts from horticultural tree crops (HTCs) is increasing due to greater demand from a growing world population. Among those HTCs, is the mango, venerated as the “king of fruits” due to its nutritional, health and the economic benefits it provides to both developed and developing nations. Its production and trade have consistently risen since the early 1960s, when official reporting begun. Global production increased from 10.9 million tons in 1961 to over 57 million tons in 2021, representing a 422% increase. According to the Food and Agricultural Organization of the United Nation (FAO), mango contributed USD 0.6 million to approximately USD 3.7 billion from 1961 to 2021 in export value to the global economy.

With increasing food demand, there is the need for increased production, optimizing efficiencies and minimizing environmental impact through the more judicious use of crop inputs. Part of this solution is the development of technologies and analytics that can more accurately and efficiently measure the spatial and temporal variability in tree health, timings of key phenological stages that dictate key management practices and production (yield and quality). The outcomes of these applications also assist with improved decision making around harvest planning and logistics, minimizing potential food wastage along the value chain, market access and forward selling. The current commercial practice for measuring these key parameters in tree crops, including mango, is predominantly by in-field within season assessment which is costly, time and labour intensive, can be inaccurate due to a nonrepresentative area of the orchard being evaluated and human subjectivity.

A review of prior literature identified some recent technological advancements such as the use of weather parameters ‘Growing Degree Days’ for determining tree growth phase and fruit maturation; proximal sensing/ machine vision and the targeted manual fruit counts of individual trees for calibrating remotely sensed imagery. However, these methods are costly, time and labour intensive. Additionally, the approaches can lack spatial granularity, scalability, commercial readiness and provide accuracies fairly similar to current commercial practices. From the publications reviewed across many horticulture and agricultural crops, remote sensing (RS) and associated cutting edge analytics (e.g. Machine learning (ML)) presents as the most likely technology to improve current management and forecasting practices in mango. However, a large knowledge gap still remains.

This study sought to address this knowledge gap by undertaking, four key objectives:

1. Assess the potential of Sentinel-2 satellite data derived vegetation indices (VIs) in distinguishing phenological stages of mango (Chapter 2)

2. Assess the potential of Sentinel-1 satellite data in distinguishing mango phenology and investigating its relationship with weather variables (Chapter 3)

3. Explore the relationship between very high-resolution satellite imagery data and fruit count for predicting mango yield at multiple scales (Chapter 4), and finally

4. Integrate time series remote sensing and weather variables for mango yield prediction using a machine learning approach (Chapter 5).

The study was conducted in commercial mango orchards in both Ghana and Australia covering the period 2015 to 2022 using RS data obtained from platforms such as Sentinel-1 (S1), Sentinel-2 (S2), Landsat-8 and WorldView-2 (WV2) and WorldView-3 (WV3) to derive VIs. A number of Statistical and ML approaches including Linear regression (LR), Random Forest (RF), Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBOOST), Ridge, Least Absolute Shrinkage and Selection Operators (LASSO) and Partial Least Square (PLSR) regressions were employed at various stages throughout the study

Four publications were produced during the study, with the key findings of each publication presented as follows:

• The S2-derived Enhanced Vegetation Index (EVI) was identified as the index that best distinguished five phenological stages of mango (Flowering/Fruitset (F/FS), Fruit Development (FRD), Maturity/Harvesting (M/H), Flushing (FLU) and Dormancy (D)) of four mango farms in Ghana.

• S1-derived radar VI was identified to be responsive for distinguishing three phenological stages (Start of Season (SoS), Peak of Season (PoS) and End of Season (EoS)) from a mango farm in Ghana. These stages align well with three of the key phenological stages (F/FS, M/H and D) retrieved in the optical data (S2) experiment above. It was also established that although weather is known to influence growth and yield of HTCs, for the weather conditions of the study area, its influence on phenology was marginal.

• The evaluation of 24 WV3-derived VIs from individual tree canopies and associated fruit counts collected across many locations, seasons and cultivars (n = 1958), identified no consistent generic relationship between the predictor (24 VIs) and response (fruit count) variables at the individual orchard level. The subsequent modelling of all composite data through ML algorithms, identified the RF-based yield prediction accuracy was better at the farm level than the individual tree level with percentage root mean square error (PRMSE) of 10.1% and 26.5% respectively, for the combined model (i.e. a model trained on all cultivars, locations and seasons data). The potential of developing an ML-based yield variability map at the individual tree level to support precision agriculture was demonstrated.

• An RF-based time series model is capable of predicting block and farm level mango yield around 3 - 5 months ahead of the commencement of the commercial harvest season. The block level combined RS/weather-based RF model for 2021 produced the best result (mean absolute error (MAE) = 2.9 t/ha), marginally better than the RS only RF model (MAE = 3.4 t/ha). The farm level model error (FLEM) was generally lower than the block level model error, for both the combined RS/weather-based RF model (farm = 3.7%, block (NMAE) = 33.6% for 2021) and the RS-based model (farm = 4.3%, block = 38.4% for 2021). The errors thus, ranged from 3.7% to 82.7% and 28.7% to 70.7% at the farm and block levels respectively, for the RS/weather-based RF model across the 8-year time series. Factors such as irregular bearing and data associated limitations were possible causes of errors in the study. The study demonstrated the ability to improve yield prediction accuracy from a finer (e.g. block level) to coarser (e.g. farm level) scales as positive (overprediction) and negative (underprediction) errors tend to cancel out.

Overall, this study demonstrated the potential of integrating RS and weather variables for accurate mango yield prediction. Nevertheless, challenges including a lack of extensive farm level standardized data and the high cost of high-resolution imagery exist. Additionally, whilst these methodologies did demonstrate some benefit over existing practice, further validation of these methodologies is required over more growing locations, cultivars and seasons. This also includes the extrapolation of models at multiple scales, particularly regional and national levels (which were beyond the scope of this study). Furthermore, future research could explore the potential of this method to produce robust estimates in other perennial tree crops.