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.

Accurate pre-harvest yield forecasting of mango is essential to the industry as it supports better decision making around harvesting logistics and forward selling, thus optimizing productivity and reducing food waste. Current methods for yield forecasting such as manually counting 2–3% of the orchard can be accurate but are very time inefficient and labour intensive. More recent evaluations of technological solutions such as remote (satellite) and proximal (on ground) sensing have provided very encouraging results, but they still require infield in-season sampling for calibration, the technology comes at a significant cost, and commercial availability is limited, especially for vehicle-mounted sensors. This study presents the first evaluation of a ”time series”—based remote sensing method for yield forecasting of mango, a method that does not require infield fruit counts and utilizes freely available satellite imagery. Historic yield data from 2015 to 2022 were sourced from 51 individual orchard blocks from two farms (AH and MK) in the Northern Territory of Australia. Time series measures of the canopy reflectance properties of the blocks were obtained from Landsat 7 and 8 satellite data for the 2015–2022 growing seasons. From the imagery, the following vegetation indices (VIs) were derived: EVI, GNDVI, NDVI, and LSWI, whilst corresponding weather variables (rainfall (Prec), temperature (Tmin/Tmax), evapotranspiration (ETo), solar radiation (Rad), and vapor pressure deficit (vpd)) were also sourced from SILO data. To determine the relationships among weather and remotely sensed measures of canopy throughout the growing season and the yield achieved (at the block level and the farm level), six machine learning (ML) algorithms, namely random forest (RF), support vector regression (SVR), eXtreme gradient boosting (XGBOOST), RIDGE, LASSO and partial least square regression (PLSR), were trialed. The EVI/GNDVI and Prec/Tmin were found to be the best RS and weather predictors, respectively. The block-level combined RS/weather-based RF model for 2021 produced the best result (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). Further testing of the RS/weather-based RF models over six additional orchards (other than AH and MK) produced errors ranging between 24% and 39% from 2016 to 2020. Although accuracies of prediction did vary at both the block level and the farm level, this preliminary study demonstrates the potential of a ”time series” RS method for predicting mango yields. The benefits to the mango industry are that it utilizes freely available imagery, requires no infield calibration, and provides predictions several months before the commercial harvest. Therefore, this outcome not only presents a more adoptable option for the industry, but also better supports automation and scalability in terms of block-, farm-, regional, and national level forecasting.

Extracted time series satellite remote sensing (RS) data in an excel table with column names describing the content of the observations in rows. It also include figures and results tables from the analysis conducted in the research. The data excludes the actual yield data obtained from mango growers in the study locations of which sharing is not permitted. The satellite RS data was used in relation with the actual yield data to develop the time series yield models.