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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.

The ability to accurately and systematically monitor avocado crop phenology offers significant benefits for the optimization of farm management activities, improvement of crop productivity, yield estimation, and evaluation crops’ resilience to extreme weather conditions and future climate change. In this study, Sentinel-2-derived enhanced vegetation indices (EVIs) from 2017 to 2021 were used to retrieve canopy reflectance information that coincided with crop phenological stages, such as flowering (F), vegetative growth (V), fruit maturity (M), and harvest (H), in commercial avocado orchards in Bundaberg, Queensland and Renmark, South Australia. Tukey’s honestly significant difference (Tukey-HSD) test after one-way analysis of variance (ANOVA) with EVI metrics (EVImean and EVIslope) showed statistically significant differences between the four phenological stages. From a Pearson correlation analysis, a distinctive seasonal trend of EVIs was observed (R = 0.68 to 0.95 for Bundaberg and R = 0.8 to 0.96 for Renmark) in all 5 years, with the peak EVIs being observed at the M stage and the trough being observed at the F stage. However, a Tukey-HSD test showed significant variability in mean EVI values between seasons for both the Bundaberg and Renmark farms. The variability of the mean EVIs between the two farms was also evident with a p-value < 0.001. This novel study highlights the applicability of remote sensing for the monitoring of avocado phenological stages retrospectively and near-real time. This information not only supports the ‘benchmarking’ of seasonal orchard performance to identify potential impacts of seasonal weather variation and pest and disease incursions, but when seasonal growth profiles are aligned with the corresponding annual production, it can also be used to develop phenology-based yield prediction models.

Understanding the components of water consumption of a crop or pasture through evapotranspiration is important for improving water management. In this study the FAO dual crop coefficient methodology has been used in conjunction with a portable evapotranspiration chamber in-situ, to quantify the basal crop (Kcb) and soil evaporation (Ke) coefficients of a pasture as a function of leaf area index and a widely used spectro-optical reflectance index, NDVI. To facilitate the measurement of soil evaporation component, small segments of the green biomass of a target pasture canopy, Tall Fescue (Festuca arundinacea var. Dovey) were allowed to rapidly senesce by applying a commercial herbicide while preserving the soil moisture and canopy structure. The ratio between the transpiration and evapotranspiration components (Kcb/Kc) increased from 0.03 to 0.46 for zero to highest available vegetation cover (LAI from 0 to 4.22). A significant linear relationship was observed between Kcb and NDVI (R2 = 0.88) suggesting the possibility of using the latter, for example through remote sensing technologies, to determine the former.

In current best commercial practice, pre-harvest fruit load predictions of mango orchards are provided based on a manual count of fruit number on up to 5% of trees within each block. However, the variability in fruit number per tree (coefficient of variation, CV, from 27 to 93% across ten orchards) was demonstrated to be such that the best case commercial sampling practice was inadequate for reliable estimation (to an error of 54–82 fruit/tree, and percentage error, PE, of 10% at a probability of 0.95). These results highlight the need for alternative methods for estimation of orchard fruit load. Pre-harvest fruit load was estimated for a case study orchard of 469 trees using (i) count of a sample of trees, (ii) in-field machine vision and (iii) correlation to a tree spectral index estimated using high resolution satellite imagery. A count of 5% of trees (23) in the trial orchard resulted in a PE of 31% (error of 37 fruit/tree), with a count of 157 trees required to achieve a PE of 10% (error of 12 fruit/tree). Sampling effort to achieve a PE of 10% was decreased by only 10% by sampling from aspatial k-means tree classifications based on machine vision derived fruit counts of all trees. Clustering based on tree attributes of canopy volume and trunk circumference was not helpful in decreasing sampling effort as these attributes were poorly correlated to fruit load (R2 = 0.21 and 0.17, respectively). In-field multi-view machine vision-based estimation of fruit load per tree achieved a R2 = 0.97 and a RMSE = 14.8 fruit/tree against harvest fruit count per tree for a set of 18 trees (average = 88; SD = 82 fruit/tree), using a faster region convolutional neural network trained the previous season. The relationship between WorldView-3 (WV3) satellite spectral reflectance characteristics of sampled trees and fruit number was characterised by a R2 = 0.66 and a RMSE = 56.1 fruit/tree. For this orchard, for which the actual fruit harvest was 56,720 fruit, the estimate based on a manual count of 5% of trees was 47,955 fruit, while estimates based on 20 iterations of stratified sampling (of 5% of trees in each cycle) had variation (SD) of 9597. The machine vision method resulted in an estimate of 53,520 (SD = 1960) fruit and the remote sensing method, 51,944 (SD = 26,300) fruit for the orchard.

In 2020, mango (Mangifera indica) exports contributed over 40 million tons, worth around US$20 billion, to the global economy. Only 10% of this contribution was made from African countries including Ghana, largely due to lower investment in the sector and general paucity of research into the mango value chain, especially production, quality and volume. Considering the global economic importance of mango coupled with the gap in the use of the remote sensing technology in the sector, this study tested the hypothesis that phenological stages of mango can be retrieved from Sentinel-2 (S2) derived time series vegetation indices (VIs) data. The study was conducted on four mango farms in the Yilo Krobo Municipal Area of Ghana. Seasonal (temporal) growth curves using four VIs (NDVI, GNDVI, EVI and SAVI) for the period from 2017 to 2020 were derived for each of the selected orchards and then aligned with five known phenology stages: Flowering/Fruitset (F/FS), Fruit Development (FRD), Maturity/Harvesting (M/H), Flushing (FLU) and Dormancy (D). The significance of the variation “within” and “between” farms obtained from the VI metrics of the S2 data were tested using single-factor and two-factor analysis of variance (ANOVA). Furthermore, to identify which specific variable pairs (phenology stages) were significantly different, a Tukey honest significant difference (HSD) post-hoc test was conducted, following the results of the ANOVA. Whilst it was possible to differentiate the phenological stages using all the four VIs, EVI was found to be the best related with p < 0.05 for most of the studied farms. A distinct annual trend was identified with a peak in June/July and troughs in December/January. The derivation of remote sensing based ‘time series’ growth profiles for commercial mango orchards supports the ‘benchmarking’ of annual and seasonal orchard performance and therefore offers a near ‘real time’ technology for identifying significant variations resulting from pest and disease incursions and the potential impacts of seasonal weather variations.

Remote sensing based spectral indices, such as the normalized difference vegetation index (NDVI), are often used to estimate the fraction of absorbed photosynthetically active radiation (fAPAR) in plant canopies. Owing to similar changes in both the NDVI and fAPAR as functions of varying solar illumination angle when using entirely passive sensors, the fAPAR–NDVI relationships are often stable, appearing insensitive to solar illumination angle. Active optical sensors (AOS) on the other hand, which have their own illuminating light source and are increasingly being used to measure NDVI (NDVIAOS), do not respond to solar illumination geometry. Yet, the passive sensor-derived fAPAR component of the fAPAR–NDVIAOS relationship remains affected by solar illumination angle. In this paper, a simple two-stream canopy model has been used to predict the fAPAR–NDVIAOS relationships of a pasture canopy (tall fescue; Festuca arundinacea) for a nadir-viewing active optical NDVI sensor under conditions of varying...

Quantifying sugarcane production is critical for a wide range of applications, including crop management and decision making processes such as harvesting, storage, and forward selling. This study explored a novel model for predicting sugarcane yield in Bundaberg region from time series Landsat data. From the freely available Landsat archive, 98 cloud free (

Lightning injuries are injuries caused by lightning strikes. Lightning delivers a massive electrical pulse over a fraction of a millisecond. It can kill a person by instantaneously short-circuiting the heart. Lightning injuries have been the second most common cause of storm-related death in the United States1,2,3. Far more injuries and deaths occur in tropical and subtropical countries. Here we present the case report of a 45 year old Bangladeshi lady who was struck by lightning. Patient presented with immediate loss of consciousness and some superficial skin burn about 1 hour following the event. Over the next few days she was found to have intracerebral haemorrhage and infarct, tympanic membrane rupture and bilateral cataract. Surprisingly patient did not suffer from any cardiac or renal injury. All lightning strike victims should receive emergency medical support on site of injury and be treated in intensive care units (ICU) equipped with multiple organ support facility.Bangladesh Crit Care J September 2017; 5(2): 132-134

In Bangladesh, remittance plays an important role in terms of its contribution to the gross national income and foreign exchange earnings, as well as its significant positive impact in reducing poverty both in the rural and urban areas (Raihan et al. 2009, 2014; Raihan and Sugiyarto 2012; Raihan and Uddin 2011; Khondker and Raihan 2009). Bangladesh is a major exporter of labor, and remittance is the second highest foreign exchange earning sector in this country. Not only international remittance, domestic remittance too has its own contribution to national income. In 2014, personal remittances received (which includes both domestic and international remittance) as a percentage of GDP was 8.7% (World Bank 2016). Though remittance is an asset for the country and it can further be transmitted to the development of the socioeconomic infrastructures of the country, members from the remittance recipient households can have a disparate preference towards the labor market participation decision compared to the non-recipient households. Hence, remittance could be one of the major drivers behind the labor market dynamics in Bangladesh. Against this backdrop, this paper examines the influence of remittances (both international and internal) over the labor market participation behavior of the recipients in agricultural and nonagricultural sectors of Bangladesh. In this study, we have used the Household Income and Expenditure Survey (HIES) 2010 data for our analysis and relevant econometric exercises, as HIES 2010 is the latest available nationwide household-level data that has a separate section on household migration and remittance information.

The fraction of absorbed photosynthetically active radiation (fAPAR) for plant canopies is often inferred from top-of-canopy, spectral reflectance, vegetation indices like the normalized difference vegetation index (NDVI). Such measures are derived using passive optical sensors and solar illumination of the canopy. However both the passive sensor-derived NDVI and the accompanying fAPAR measurements are affected by the solar elevation angle ( θ s ). In many cases the effect of θ s on both NDVI and fAPAR measurements is similar and the effect of θ s is often cancelled out. The new class of active optical sensors (AOS) that contain their own radiant light sources to produce equivalent measurements of NDVI are not influenced by θ s even though the accompanying values of fAPAR, as derived using a passive sensor are. This means the fAPAR–NDVI AOS relationship will invariably be sensitive to θ s . By way of example, this paper investigates the correlations between the NDVI AOS and fAPAR under conditions of varying solar illumination angle for a tall fescue ( Festuca arundinacea) pasture. The NDVI AOS was observed to retain a strong linear correlation with fAPAR ( R 2 ≥ 0.85) but fAPAR was highly sensitive to θ s . Subsequently, simple models can be utilized to predict the fAPAR–NDVI AOS relationship for any solar elevation angle between 30 and 80°.

The amount of photosynthetically active radiation (PAR, 0.4–0.7 μm) absorbed by plants for photosynthesis relative to incident radiation is defined as the fraction of absorbed photosynthetically active radiation (fAPAR). This is an important variable in both plant biomass production and plant growth modeling. This study investigates the application of a newly developed, linear irradiance sensor (LightScout Quantum Bar Sensor, LightScout, Spectrum Technologies, Inc. USA), to quantify fAPAR for a demonstrator crop, Triticale (X Triticosecale Wittmack). A protocol was devised for sensor placement to determine reflected PAR components of fAPAR and to determine the optimal time of day and sensor orientation for data collection. Coincident, top of canopy, normalized difference vegetation index (NDVI) measurements were also acquired with a CropCircle™ ACS-210 sensor and measurements correlated with derived fAPAR values. The optimum height of the linear irradiance sensor above soil or plant canopy was found to be 0.4 m while measuring reflected PAR. Measurement of fAPAR was found to be stable when conducted within 1 h of local solar noon in order to avoid significant bidirectional effects resulting from diurnal changes of leaf orientation relative to the vertically-placed sensor. In the row crop studied, averaging fAPAR readings derived from the linear irradiance sensor orientated across and along the plant row provided an R2 = 0.81 correlation with above-canopy NDVI. Across row sensor orientation also gave a similar correlation of R2 = 0.76 allowing the user to reduce sampling time.

Pre-harvest yield estimation of mango fruit is important for the optimization of inputs and other resources on the farm. Current industry practice of visual counting the fruit on a small number of trees for yield forecasting can be highly inaccurate due to the spatial variability, especially if the trees selected do not represent the entire crop. Therefore, this study evaluated the potential of high resolution WorldView-3 (WV3) satellite imagery to estimate yield of mango by integrating both geometric (tree crown area) and optical (spectral vegetation indices) data using artificial neural network (ANN) model. WV3 images were acquired in 2016⁻2017 and 2017⁻2018 growing seasons at the early fruit stage from three orchards in Acacia Hills region, Northern Territory, Australia. Stratified sampling technique (SST) was applied to select 18 trees from each orchard and subsequently ground truthed for yield (kg·tree−1) and fruit number per tree. For each sampled tree, spectral reflectance data and tree crown area (TCA) was extracted from WV3 imagery. The TCA was identified as the most important predictor of both fruit yield (kg·tree−1) and fruit number, followed by NDVI red-edge band when all trees from three orchards in two growing seasons were combined. The results of all sampled trees from three orchards in two growing seasons using ANN model produced a strong correlation (R2 = 0.70 and 0.68 for total fruit yield (kg·tree−1) and fruit number respectively), which suggest that the model can be obtained to predict yield on a regional level. On orchard level also the ANN model produced a high correlation when both growing seasons were combined. However, the model developed in one season could not be applied in another season due to the influence of seasonal variation and canopy condition. Using the relationship derived from the measured yield parameters against combined VIs and TCA data, the total fruit yield (t·ha−1) and fruit number were estimated for each orchard, produced 7% under estimation to less than 1% over estimation. The accuracy of the findings showed the potential of WV3 imagery to better predict the yield parameters than the current practice across the mango industry as well as to quantify lost yield as a result of delayed harvest.

Monitoring pasture growth rate is an important component of managing grazing livestock production systems. In this study, we demonstrate that a pasture growth rate (PGR) model, initially designed for NOAA AVHRR normalised difference vegetation index (NDVI) and since adapted to MODIS NDVI, can provide PGR at spatial resolution of ~2 m with an accuracy of ~2 kg DM/ha.day when incorporating in-situ sensor data. A PGR model based on light-use efficiency (LUE) was combined with in-situ measurements from proximal weather (temperature), plant (fraction of absorbed photosynthetically active radiation, fAPAR) and soil (relative moisture) sensors to calculate the growth rate of a tall fescue pasture. Based on an initial estimate of LUEmax for the candidate pasture, followed by a process of iterating LUEmax to reduce prediction errors, the model was capable of estimating PGR with a root mean square error of 1.68 kg/ha.day (R2 = 0.96, P-value ≈ 0). The iterative process proved to be a convenient means of estimating LUE of this pasture (1.59 g DM/MJ APAR) under local conditions. The application of the LUE-PGR approach to developing an in-situ pasture growth rate monitoring system is discussed.

This paper evaluates the potential of very high resolution multispectral (Worldview-3) satellite imagery for mapping yield parameters in avocado and macadamia orchards. An evaluation of 18 structural and pigment based vegetation indices (VIs) derived from Worldview-3 imagery identified a positive relationship to nut/ fruit weight (kg/tree) R2>0.69 for macadamia and R2>0.68 for avocado; and nut/ fruit number (per tree) R2>0.6 for macadamia and R2>0.61 for avocado. Using the algorithms derived between the optimal VI and the measured parameter, yield and nut/ fruit number maps were derived for each block. In the absence of a commercial yield monitor, the resulting yield maps offer significant benefit to growers for improving orchard management, harvest scheduling, and forward selling decisions.

Evapotranspiration (ET) is the total amount of water released by a crop or pasture canopy in the form of Transpiration (T) and Evaporation (E). ET accounts for up to 96% of the water loss depending on the types of vegetation cover and climatic conditions (Wilcox et al., 2003). Transpiration is related to the productivity of crops and pasture; whereas, evaporation is the loss of water directly from soil surface. Estimating separately the components of ET in the field is challenging yet it is highly significant in terms of improving crop water use and irrigation efficiency, since a anywhere between 30 and 80% of the water flux can be associated with the all important evaporation component (Wilcox et al., 2003). Sap flow monitoring, using micro-lysimeters or isotopic analysis are the usual options for separately measuring transpiration and evaporation in plants but these are incompatible with in-situ field deployment. In this study a portable and convenient method for separately determining the evapotranspiration components has been developed. When coupled with widely-used active optical sensors, the device can be used to develop, in-situ, relationships between spectro-optical indices such as NDVI and evapotranspiration coefficients (Kc, Kcb and Ke) that characterize actual ET water loss relative to evaporative demand.