# Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time

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## Abstract

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## 1. Introduction

#### 1.1. The Need for Fruit Load Forecast

#### 1.2. Literature Base

#### 1.3. Types of Models

## 2. Measurement of Fruit Number

#### 2.1. Benchmarking

#### 2.1.1. Reference Estimates

#### 2.1.2. Evaluation Metrics

^{2}), or Mean Absolute Error (MAE), with a few studies employing variants of these parameters such as Mean Absolute Percentage Error (MAPE), Lin’s Concordance Correlation Coefficient (CCC), Simple Average Ensemble (SAE), Reference Change Values (RCV), and Matthews Correlation Coefficient (MCC) [1], as defined in Table 2. Useful terms for the characterization of population variability is the coefficient of error (CE), defined as the standard error of mean divided by the mean and coefficient of variation (CV), defined as the standard deviation divided by the mean.

#### 2.2. Tree Evaluation and Historical Knowledge

#### 2.3. Manual Fruit Count

#### 2.3.1. Sample Size and Variance

^{2}:

#### 2.3.2. Sampling Approaches

^{−0.5}approximately, where n is the sample size [15]. An efficient sample design will produce low between-sample variance (different realizations of the sampling design should provide similar estimates) and will have a steeper rate of decrease in CE than n

^{−0.5}.

#### 2.3.3. Examples

_{obs}= SD/mean). The observed variability of a sample is lower bound by the true or biological variability of the population Equation (5), which means that it cannot by itself indicate the accuracy of an estimate. A decision to supplement a sample can be made using the sample size calculation (Equations (3) and (4)) for random samples and the SE of the estimator. As an example, consider site 1 from Table 3, with an RS (without replacement) estimator. An estimator of the variance of the sample mean is given by substituting SD

_{obs}for σ in Equation (1). As a starting point, a single realization of n = 13 trees was taken. The resulting sample provided an estimate of the mean as 69.5 fruits per tree (i.e., an error of −36.7%), a sample SD of 90.5 fruits, and an estimated sampling variance Equation (1) of 612 (for an estimated coefficient of error, CE = 35.6%). Substituting these values in Equation (5) and then rearranging to solve for SD

_{bio}, the biological variability was estimated to be:

_{obs}of 104.8. The estimated CE was 18.1%, which compares well with the true CE of 16.5%, reported in the previous subsection) but the estimated SD

_{bio}was 102.8 (an overestimate by 25%). The more precise estimator based on 26 trees provided an even higher SD than the smaller 13 tree sample! Increasing the sample size will reduce the sampling error (a property of an unbiased estimator), but in this case, the observed SD will always be large because the biological SD is high.

^{3}) placed into the canopy of each sample tree, (iv) the measurement of fruit size and (v) the measurement of fruit retention rate. Falivene and Hardy [29] recommended a count of 20 frames per hectare, noting that for more accurate and better representation, more units could be sampled. Lacey [30] recommends counts of fruit in a frame of 60 trees per citrus block, with sampling of at least two sides of the tree. The estimated time for this procedure was 60 min per block.

#### 2.4. Machine Vision Methods

#### 2.4.1. Image Processing

#### 2.4.2. Hardware and Imaging Platform

^{2}> 0.7 compared to 0.53, against field-based counts) for the estimation of pear flower cluster number per tree [45]. Apolo-Apolo et al. [46] report that a 3D reconstructed image from an aerial view accounted for only 27% of fruit on the 19 apple trees assessed, with an R

^{2}of 0.80, MAE of 129 and RMSE of 131 fruit per tree achieved for a linear regression of machine vision estimated fruit counts against hand harvest counts. Trees had an average of 255 fruit/tree. This accuracy was acknowledged as inadequate for the task of yield prediction.

#### 2.4.3. Implementation on Ground Vehicles

^{2}= 0.78, RMSEP = 27.8 fruit/tree, and slope = 0.87 for fruit counts, relative to manual counts of 18 trees, while the multi-sensor imaging system achieved an R

^{2}= 0.88, RMSEP = 19.8 and slope = 0.97.

^{2}between estimated and harvested apple yield were 2.6 kg/tree and 0.62 for early fruit growth (small, green fruit) and 2.5 kg/tree and 0.75 near harvest (red, large fruit), for trees with an average 18 kg of fruit. In a later attempt in the direct prediction of total tree fruit load, deep learning techniques were employed [12], and good predictions of current season tree fruit loads were achieved, but predictions of fruit load per tree for a subsequent season were poor. Further work should be undertaken to progress this concept.

#### 2.5. Correlative Methods

#### 2.5.1. What Determines Tree Fruit Load?

_{3}and paclobutrazol [65,66]. the timing of pruning is also important, as juvenile stems (< 4 weeks of age) are not able to convert to the reproductive mode. Further, only a proportion of the panicles that form on a mango tree set fruit, and of those, only a proportion will hold fruit through to harvest, with these proportions varying between trees and seasons as pollination and growth conditions vary. If these factors are not optimal, a yield gap will exist between the actual and potential yield.

#### 2.5.2. Prediction of Yield in Cropping

^{2}= 0.91 and RMSE = 0.54 t/ha) of a set of fields independent to that used in training using OSAVI, CI and ‘stress index’ from Sentinel 2 imagery by Zhao et al. [67]. Soybean and corn yield was predicted (R

^{2}= 0.92 and 0.88, respectively) using neural network and Multiple Linear Regression (MLR) models with a combination of Landsat and SPOT image data from early to mid-season crop growth stages [69].

^{−1}, for yields around 70 t ha

^{−1}, i.e., 29% RRMSE.

^{2}~ 0.75) for prediction two months before crop maturity [71]. It was suggested that time series satellite data allows for the tracking of crop growth, capturing the variability of yield through the growing season, with the contribution to yield prediction saturating at the time of maximum vegetative growth. In contrast, climate data provided added value across the whole season.

#### 2.5.3. Prediction Based on Correlation to within-Season Attributes

#### 2.5.4. Correlation to Canopy Structural Attributes

^{3}, but many trees fail to achieve this maximum, i.e., a yield gap is demonstrated (Figure 7). Thus, while the potential for mango fruit number is set by the number of vegetative terminals, the correlation between fruit load and tree structure parameters can be poor, e.g., Anderson et al. [16] reported an R

^{2}of 0.21 and 0.17 for the correlation of fruit load to canopy volume measured using LiDAR and trunk circumference, respectively. For trees with a greater range in canopy volume, Sarron et al. [9] reported an R

^{2}of 0.63 to 0.76 (p-values < 0.05) between mango fruit load and canopy volume, as measured using a UAV photogrammetry method.

^{2}for the fruit load prediction was 0.53, which the author noted was unsatisfactory. The identified constraints included the smallholder practice of interplanting tree crops and varieties and mixtures of tree ages in a given orchard and the reliability of yield data from growers.

^{2}= 0.80) was demonstrated for citrus canopy size and yield in a single season [73], another study demonstrated that the relationship was not consistent between seasons, orchards, time of imagery acquisition (twice per season) and the combined features of canopy area (pixel based) and spectral band, for a canopy area estimated from 0.2 m spatial resolution satellite imagery [74]. Sarron et al. [9] devised a methodology for yield modeling based on mango tree structural parameters and a fruit ‘load index’. Mango tree structure parameters of height, crown area and volume were assessed by images collected by a UAV. These parameters were used together with cultivar information and a human-assessed ‘fruit load index’ per orchard as inputs to a second-degree polynomial predictive model. The fruit load index involved a visual assessment of the orchard to one of four classes (null, low, medium, high) based on the area of visible fruits to the overall crown area. This assessment was made of 50 trees located on a transect through the orchard, although this was recognized as a potential source of sampling error. Cultivar-specific models were developed, with an R

^{2}of 0.77 to 0.87 and RRMSE from 20–29% reported on validation sets. The load index was weighted higher than tree structure variables in all models.

#### 2.5.5. Correlation to Spectral Indices

^{2}= 0.84 and 0.77 for images captured at 09:30 and 11:30 h in 2004) [75]. Olive and peach fruit weight (g) were also correlated to canopy temperature (R

^{2}= 0.91 and 0.92 in 2004 and 2005, respectively, for olive, and R

^{2}= 0.82 and 0.81 in 2004 and 2005 seasons, respectively).

^{2}cv = 0.83 and an RMSEcv = 299 kg/ha for the ten regions across the ten seasons, while the C-Fix + respiration model achieved an R

^{2}cv = 0.89 and an RMSEcv = 224 kg/ha (cross validation based on use of yearly datasets), on an average yield of 1600 kg/ha, and thus an MAE of 14%.

^{2}= 0.45, 0.28 and 0.29; n = 90 trees). Better results were obtained by using models developed for individual blocks, using the VI with the highest correlation to yield in each case (R

^{2}= 0.21 to 0.89). Yield predictions of orchard blocks using the correlations developed on 18 trees were within 0.1 to 3.4 t ha

^{−1}of actual harvest (packhouse data), with average totals around 8 t/ha. In the mango study, Rahman et al. [78] utilized ANN models based on WV-3 image-derived tree crown area and all spectral bands. For a model across all orchards and seasons, the highest correlation (R

^{2}= 0.30) to fruit count was achieved with the NDVI red-edge band. As for the avocado work, superior results were obtained with use of local (orchard) models compared to use of a generic model. For individual orchard models, the best result of 18 VIs gave R

^{2}of between 0.56 to 0.63. Yield estimations within 1 to 7% of harvested yield for each orchard were reported.

^{2}= 0.84 (average of 2016 and 2017) was obtained using data from within the period mid to late July. However, in validation (using 2016 to predict 2017 and vice versa) the best result was obtained by combining the captures from late July and early August (R

^{2}= 0.35 and 0.43, PE = 14.8 and 13.3% for 2016 and 2017 seasons, respectively). The modified WOrld FOod STudies (WOFOST) model, which used the satellite-estimated leaf area index from the maximum vegetative period (per year) as an additional input, achieved an improved validation result (R

^{2}= 0.62 and 0.59, PE = 10.9 and 11.1% for 2016 and 2017, respectively).

#### 2.5.6. Prediction Based on Multiple Season Attributes

#### 2.5.7. Prediction Based on Yield Across Multiple Seasons, Climatic Variables and Canopy Characteristics

_{max}). Warmer winter conditions and summer VPD

_{max}beyond 40 hPa resulted in decreased yield. A random forest model based on these inputs explained 82% of yield variation in a sixfold cross validation, with an RMSE of 88.3 kg/ha, for average orchard yields around 3000 kg/ha. When light interception was not used as an input, the model still explained 78% of yield variation.

## 3. Measurement of Fruit Size

#### 3.1. Current (Manual) Methods

#### 3.2. Machine Vision Methods

#### 3.3. Prediction of Size at Harvest

## 4. Measurement of Fruit External Quality

## 5. Commercial Systems for Fruit Number, Size and External Quality

## 6. Measurement of Fruit Maturity

#### 6.1. Thermal Time from Flowering

#### 6.1.1. Heat Units/Growing Degree Days

#### 6.1.2. Identifying Flowering Events

^{2}= 0.87 and an RMSEP = 10.9% [118]. The use of FRCNN and SURF algorithms allowed the detection of mango panicles instead of pixel counts with a result of R

^{2}= 0.79 and an RMSEP = 35.3 panicles per tree against a field count of panicles of 48 trees [40]. Similarly, in recent work, a pixel-based classification of a 3D point cloud was recommended over an object-based estimation for the level of apple flowering. However, this approach depends on synchronous flowering, as pixel area per flower cluster will vary with the developmental stage of flowering. This was recognized by Vanbrabant et al. [45] who noted that ‘preconditions of the transferability of the developed method are the flower phenology stage’.

^{2}= 0.80 and RMSEP = 35.6 panicles per tree for total panicle count per tree, with a precision of 69, 78, and 61% and a F1 score of 75, 79 and 70 for the three maturation stages, respectively. A time series of the frequency of the three stages was used to identify the times of peak flowering. Flower detection and count in apple canopy images has also been achieved using a CNN, with an average precision of 68% [114] and 59% [115] reported, and with YOLOv4, with mean average precision of 97.3% [116].

#### 6.2. Fruit Attributes

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Conflicts of Interest

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**Figure 1.**Annual scientific paper publication rate as indexed by Scopus records (doa 18/4/2021) for the keywords ‘tree AND fruit AND yield AND estimation’.

**Figure 2.**Images of canopies associated with mango fruit loads of (

**a**) 60, (

**b**) 40 and (

**c**) 8 tonnes per ha, with tree density of 400 trees/ha.

**Figure 3.**Comparison of two sampling strategies, RS and SUR, in estimation of total fruit load (N

_{(fr)}) in a mango orchard in which the fruit number on every tree is known (site 1 in Table 3). The orchard has 469 trees and high variability in tree fruit load, with an average of 88 and SD = 89 fruit per tree (CV = 81%). The boxplots present estimates of fruit loading based on 250 realizations of 26 random samples using RS, compared to SUR sampling of rows with period 2 and trees-in-rows with period 9, for a mean sample size of 26 trees.

**Figure 4.**Example of a machine vision system employing LED lighting, GNSS for geolocation and cameras facing in two directions, mounted to a tractor–spray rig.

**Figure 5.**Manual tree fruit counts plotted against multi view machine vision estimates of mango tree fruit load for an orchard of smaller, open canopy trees (

**top panel**) and larger, dense canopy trees (

**bottom panel**). Units for RMSE and bias are counts of fruit per tree.

**Figure 6.**Image of the same mango tree canopy at day and at night. Bounding boxes relate to tracking of fruit between frames.

**Figure 7.**Relationship between tree fruit load and tree canopy volume as assessed using LiDAR for 494 trees of a single mango orchard. Line with slope of 18 fruit/m

^{3}bounds 99% of observations.

**Figure 8.**Fruit load per tree of 18 mango trees from one orchard, over 6 years.

**Top panel**: trees demonstrating synchronicity in biennial bearing, with ‘on’ years in odd years;

**middle panel**: trees demonstrating biennial bearing with ‘on’ years in even years or a non-biennial bearing pattern; Colored lines represent individual trees in top and middle panel;

**bottom panel**: average fruit load of 18 trees and the packhouse count for the orchard (494 trees).

**Figure 9.**Estimated weight of mango fruit on-tree, based on lineal dimensions of fruit assessed using a ToF camera for estimation of camera to fruit distance and an RGB camera for estimation of fruit size in pixels.

**Figure 10.**Fruit mass (g) estimated using lineal dimensions temporally up to harvest for mango, cv. Honey Gold. A linear regression model has been fitted to data between 32 and 11 days before harvest (dotted line): slope = 3.41 g/day, intercept = 604 g, R

^{2}= 0.99.

**Table 1.**Literature base as indexed by Scopus records (doa 8/2/2021) for the keywords ‘fruit AND yield AND estimation’ for the years 2010–2021. # represents number.

Crop | # of Records | % | Country | # of Records | % |
---|---|---|---|---|---|

Apple | 32 | 33 | China | 38 | 21 |

Mango | 19 | 20 | United States | 34 | 19 |

Citrus | 17 | 18 | Spain | 29 | 16 |

Other | 16 | 16 | Australia | 20 | 11 |

Kiwi | 4 | 4 | India | 16 | 9 |

Grape | 3 | 3 | Germany | 14 | 8 |

Olive | 3 | 3 | Brazil | 11 | 6 |

Tomato | 3 | 3 | France | 11 | 6 |

Italy | 9 | 5 | |||

Method | # of records | % | Estimation level | # of records | % |

MV RGB | 65 | 59 | image | 36 | 38 |

Satellite | 10 | 9 | tree | 28 | 30 |

MV Hyperspectral | 8 | 7 | orchard | 19 | 20 |

Proximal indirect | 8 | 7 | region | 11 | 12 |

UAV | 7 | 6 | |||

Historical/climate | 6 | 5 | |||

Manual | 6 | 5 |

Term | Meaning | Equation |
---|---|---|

RMSE | The standard deviation of the prediction residuals | $RMSE=\sqrt{\frac{{{\displaystyle \sum}}_{i=1}^{n}{\left({\widehat{y}}_{i}-{y}_{i}\right)}^{2}}{n}}$ where ${\widehat{y}}_{i}$ is the estimated value, ${y}_{i}$ is the observed value and n is the sample size |

RRMSE | RMSE relative to mean | $RRMSE=\frac{RMSE}{\overline{x}}$ |

MSE | The average squared errors of prediction residuals | $MSE=\frac{1}{n}{\displaystyle {\displaystyle \sum}_{i=1}^{n}}{({y}_{i}-{\widehat{y}}_{i})}^{2}$ |

R^{2} | The proportion of the variance of one variable that can be explained by the variance of the second variable | ${R}^{2}=1-\frac{{{\displaystyle \sum}}_{i=1}^{n}{({y}_{i}-{\mu}_{y})}^{2}}{{{\displaystyle \sum}}_{i=1}^{n}{({y}_{i}-{\widehat{y}}_{i})}^{2}}$ where ${\mu}_{y}$ is the mean of observed values |

MAE | The average of the absolute values of prediction residuals | $MAE=\frac{{{\displaystyle \sum}}_{i=1}^{n}\left|{\widehat{y}}_{i}-{y}_{i}\right|}{n}$ |

MAPE | The average of the absolute values of prediction residuals divided by the actual values | $MAPE=\frac{1}{n}{\displaystyle {\displaystyle \sum}_{i=1}^{n}}\left|\frac{{y}_{i}-{\widehat{y}}_{i}}{{y}_{i}}\right|$ |

CCC | Interpreted similarly to R^{2}, but used to compare two methods predictions or to measure repeatability of repeats of a single method | $CCC=\frac{2p{\sigma}_{y}{\sigma}_{\widehat{y}}}{{\sigma}_{y}^{2}+{\sigma}_{\widehat{y}}^{2}+{({\mu}_{y}-{\mu}_{\widehat{y}})}^{2}}$ where ${\sigma}_{y}$ represents the variability of either the observed value or another variable and ${\sigma}_{\widehat{y}}$ and ${\mu}_{\widehat{y}}$ the variability and the mean of either the estimated value or another variable |

RCV | The minimal significant difference between temporally different measurements | $RCV=\pm q\sqrt{2\left({\sigma}_{y}^{2}+{\sigma}_{\widehat{y}}^{2}\right)}$ where $q$ is the quantile for the desired probability from a standard normal distribution |

MCC | Measures the agreement of binary classifications | $MCC=\frac{TP\times TN-FP\times FN}{\sqrt{\left(TP+FP\right)\left(TP+FN\right)\left(TN+FP\right)\left(TN+FN\right)}}$ where TP is true positives, TN is true negatives, FP is false positives and FN is false negatives of binary classification |

**Table 3.**Calculated sample sizes $n$ (Equation (3)) and adjusted sample size ${n}^{*}$ (Equation (4)) required for SRS with a 90% confidence and a PE of 10% based on an estimated $SD$ from a preliminary sample taken from the population of $N$ trees. Data from Anderson et al. [16]. # represents number.

Site | $\mathit{N}$ (# Trees) | Average (# Fruit/Tree) | $\mathit{S}\mathit{D}$ (# Fruit/Tree) | $\mathit{n}$ (# Trees) Equation (3) | ${\mathit{n}}^{*}$ (# Trees) Equation (4) | ${\mathit{n}}^{*}$$/\mathit{N}\phantom{\rule{0ex}{0ex}}(\%)$ |
---|---|---|---|---|---|---|

1 | 469 | 88 | 82 | 234 | 156 | 33 |

2 | 486 | 259 | 102 | 42 | 38 | 8 |

3 | 1017 | 240 | 160 | 120 | 107 | 11 |

4 | 1100 | 80 | 34 | 49 | 47 | 4 |

5 | 224 | 59 | 36 | 100 | 69 | 31 |

6 | 1205 | 97 | 65 | 121 | 110 | 9 |

7 | 1091 | 201 | 55 | 20 | 20 | 2 |

8 | 1818 | 106 | 51 | 62 | 60 | 3 |

9 | 1176 | 77 | 61 | 169 | 148 | 13 |

10 | 1115 | 85 | 40 | 60 | 57 | 5 |

**Table 4.**Example reported results of machine vision-based estimation of tree fruit load. Papers are ordered chronologically within commodities. Single view refers to collection of one image per tree and one row side only. Dual view (DV) refers to collection of one image per tree side from each interrow and multi view (MV) entails collection of several images in succession per tree side.

Paper | Algorithm/Technique | Imaging Method | Validation Set | Result |
---|---|---|---|---|

Apple | ||||

Gongal et al. [54] | Color and shape thresholding | DV on ground vehicle with row cover | 20 trees | 20% error on fruit in image, 18% error on fruits per tree |

Bargoti and Underwood [58] | CNN | MV on ground vehicle, day imaging | 15 rows | 10.8% error on fruit counts per tree |

Cheng et al. [53] | Color thresholding, neural network utilizing ancillary variables (foliage area, fruit area) | Single view on ground vehicle, day imaging | Multi season cross validations (2009–2011) 180 trees total. | 10.7 and 8.9% MAPE for estimations of fruit per tree after fruit drop and during ripening period, respectively |

Linker [57] | Speeded Up Robust Features | DV (2014) and MV (2016 set) ground vehicle, night imaging | 10 grabs of 20 random trees (2014 and 2016 separately) | 2014: 4.8% error and 2016: 5.4% error (fruit/tree) |

Kuznetsova et al. [59] | YOLOv3 | Single view on stationary platform | 552 images | 93% precision; 1% error on fruit count per image |

Citrus | ||||

Gan et al. [60] | FRCNN | Fusion of thermal and RGB, day imaging | 1658 images | 95.5% precision on fruit/image |

Apolo-Apolo et al. [46] | FRCNN | DV on UAV | 20 trees, 3 seasons | 11.5, 4.3 and 5.8% error against harvested kg for 2016, 2017, and 2018, respectively |

Grape | ||||

Nuske et al. [52] | Shape, texture, color, occlusion correction | MV at 6 fps on ground vehicle; stereo camera for position | 1212 vines of 5 varieties on 4 trellis types, for 4 seasons. | 4% error in yield prediction with use of occlusion correction |

Font et al. [55] | Color thresholding and area or volume modeling | Single view on ground vehicle | 25 bunches | 16–17% error on bunch weight. |

Kiwifruit | ||||

Wijethunga et al. [44] | Color thresholding | Single view on ground vehicle, night imaging | 78 gold 42 green images | 7.1 and 24.9% error for gold and green varieties, respectively, for fruit in image counts |

Mango | ||||

Stein et al. [37] | FRCNN | DV and MV on ground vehicle, day imaging | 16 trees | 1.4% error on fruit per tree (MV) |

Qureshi et al. [56] | Texture and shape segmenting | DV ground vehicle, multiple light conditions | Multiple validations (10–74 trees) | Multiple validations from 0.5 to 40% error on fruit per tree estimate (mean = 18.6 and sd = 15.6%) |

Anderson et al. [16] | FRCNN | DV and MV on ground vehicle, night imaging | 1 orchard | 9 and 6% error on packhouse count (DV and MV, respectively) |

Koirala et al. [3] | MangoYOLOv3 | DV on ground vehicle; night imaging | 5 orchards | 3% error on packhouse count (5 orchards combined) |

**Table 5.**Examples of allometric relations between fruit lineal dimensions and weight, and growth models for prediction of size at harvest, for several commodities. Abbreviations are: W, fruit weight (g) at a given time, e.g., W

_{60 DAB}; where DAB is days after full bloom; HW, weight of fruit at harvest (g), L, fruit length (mm), Wi, fruit width (mm), T, fruit thickness (mm).

Publication | Commodity | Allometry between Weight and Lineal Dimensions | Prediction of Harvest Weight |
---|---|---|---|

Marini et al. [90] | Apple (Gala) | Quadratic W = 17.03 – (1.48 × d) + (0.046 × d ^{2})R ^{2} = 0.972 | Linear HW = −169.09 + (8.19 × W _{60 DAB})R ^{2} = 0.79 |

Lakso et al. [91] | Apple (Empire) | Linear 1.47 to 1.95 g/day | |

Ortega-Farias et al. [92] | Apple (Granny Smith) | Logistic growth curve | |

Verreynne [93] | Citrus (Navel Orange) | Linear 26.4 mm/day | |

Ellis et al. [94] | Grape | Bayesian predicted double sigmoid growth curve | |

Hall et al. [95] | Kiwifruit | Linear 0.42 mL/day from 50 DAB | |

Spreer and Müller [96] | Mango (Chok Anan) | Linear W = 0.000539 × L × Wi × T R ^{2} = 0.97 | |

Wang et al. [97] | Mango (HoneyGold) | Linear W = 0.42 × L × Wi ^{2} R^{2}= 0.96 |

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**MDPI and ACS Style**

Anderson, N.T.; Walsh, K.B.; Wulfsohn, D. Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time. *Agronomy* **2021**, *11*, 1409.
https://doi.org/10.3390/agronomy11071409

**AMA Style**

Anderson NT, Walsh KB, Wulfsohn D. Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time. *Agronomy*. 2021; 11(7):1409.
https://doi.org/10.3390/agronomy11071409

**Chicago/Turabian Style**

Anderson, Nicholas Todd, Kerry Brian Walsh, and Dvoralai Wulfsohn. 2021. "Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time" *Agronomy* 11, no. 7: 1409.
https://doi.org/10.3390/agronomy11071409