# Establishment of Plot-Yield Prediction Models in Soybean Breeding Programs Using UAV-Based Hyperspectral Remote Sensing

^{1}

^{2}

^{3}

^{*}

^{†}

## Abstract

**:**

_{M}

^{2}) and root mean square error of the model (RMSE

_{M}), verification R

_{V}

^{2}and RMSE

_{V}, and their sum R

_{S}

^{2}and RMSE

_{S}. Integrated with the coincidence rate between the model predicted and the practical yield-selection results, the models, M

_{A1-2}, M

_{A}

_{4-2}and M

_{A}

_{6-2}, with coincidence rates of 56.8%, 58.5% and 52.4%, respectively, were chosen for yield-prediction in yield-test nurseries. The established model construction elements and methods can be used as local models for pre-harvest yield-selection and post-harvest integrated yield-selection in advanced breeding nurseries as well as yield potential prediction in plant-derived-line nurseries. Furthermore, multiple models can be used jointly for plot-yield prediction in soybean breeding programs.

## 1. Introduction

^{2}) value of 0.66 [28]. Sensitive vegetation indices in the form of NDVI and RVI based on canopy spectral reflectance were suggested to predict the grain yield of soybean by Ma et al [45] and Qi [54], where NDVI was found to have the highest correlation with soybean yield.

## 2. Materials and Methods

_{V}) and real breeding selection results. Finally, three best models were selected for yield prediction in 1st- and 2nd-year yield-test as well as for plant-derived-line evaluation which may be used in comprehensive yield selection integrated with the harvested yield records. Please see the flowchart for the UAV data-processing process (Supplementary material Figure S1).

#### 2.1. Plant Materials and Field Experiments

^{−1}. The plot seed yield was measured by harvesting plots with the seed moisture adjusted to 13%, recorded as t ha

^{−1}.

#### 2.2. Assembly of the Unmanned Aerial Vehicle (UAV)-Based Hyperspectral Remote-Sensing System

#### 2.3. Processing of the UAV Hyperspectral Reflectance and Determination of the Reflectance-Sampling Unit-Size in Plots

#### 2.4. Optimization of the Vegetation Indices along with Corresponding Hyperspectral Bands

^{2}, Equation(1)) was plotted according to the value of R

^{2}completed with “plsregress” function in MATLAB R2010b (MathWorks, Inc., Natick, MA, USA). From the contour map, the sensitive wavebands along with the corresponding indices were identified according to their largest R

^{2}values. The R

^{2}are calculated as follows:

_{i}and y

_{i}are the measured yield value and the predicted yield value, respectively, $\overline{x}$, is the average value of x

_{i}$i$ varies from 0 to n, where n is the number of tested breeding lines.

#### 2.5. Establishment and Verification of the Yield Prediction Models

_{i}and y

_{i}are the measured yield value and the predicted yield value, respectively, $i$ varies from 0 to n while n is the number of tested breeding lines.

_{M}

^{2}and the root mean square error of the model as RMSE

_{M}; the coefficient of determination calculated from the other half of the lines is used for validation and designated as R

_{V}

^{2}and the root mean square error of the model as RMSE

_{V}; both sets of determination coefficient and root mean square error are used to assess the yield prediction model. For a comprehensive evaluation to balance the modelling and verification, these two parts were summed up as R

_{S}

^{2}and RMSE

_{S}. Yield predictions with higher R

^{2}and lower RMSE are deemed to be better ones.

_{M}

^{2}, RMSE

_{M}, R

_{V}

^{2}, RMSE

_{V}) were completed with MATLAB R2010b software (MathWorks, Inc., Natick, MA, USA). The calibration and validation of the established yield model were calculated by using Microsoft Excel 2007 (Microsoft Corporation, Redmond., Washington, USA).

#### 2.6. Superior Plot-Yield Prediction Models Selected for Breeding Programs

_{V}summed over the three sets of breeding lines. In the second method, in breeders’ actual yield selection, the breeding line with average yield in a single year less than 3.00 t ha

^{−1}is treated as low-yielding line to be eliminated (Eli), that with average yield in a single year more than 3.75 t ha

^{−1}is treated as high-yielding line to be promoted (Pro) and that with average yield in a single year between 3.00 and 3.75 t ha

^{−1}is treated as intermediate line to be reserved for further observation (Res). According to the selection classification, the prediction values of lines in each of the three sets of tests (1stYYT 2015, 2ndYYT 2015 and 2ndYYT 2016) were grouped into the respective categories and compared with the actual selection results. The coincidence rate between the predicted classification and actual breeding classification was calculated for each of the three yield-tests as well as the total value of the three tests.

## 3. Results

#### 3.1. Field Experiment Precision and Variation among the Tested Breeding Lines

^{−1}, that of 2ndYYT 2015 ranged from 1.65 to 4.91 t ha

^{−1}, that of the 2ndYYT 2016 ranged between 1.72 and 4.41 t ha

^{−1}and the average plot yield of the plant-to-lines of NJRIKY ranged from 1.08 to 3.39 t ha

^{−1}, with their genotypic coefficient of variation values of 34.85%, 29.35%, 26.90% and 33.15%, and their error coefficient of variation of 19.18%, 15.89%, 12.81% and 33.31%, respectively. These results indicated that large yield variation existed in the three sets of breeding lines with small experimental errors while there was larger yield variation and experimental error but less mean yield for the NJRIKY plant-derived lines population. Therefore, the data of the three sets of breeding lines were used for the establishment and validation of yield-prediction models from which the established models can fit a relatively wide situation, while those of the NJRIKY was to be used for calibration of the established prediction models to imitate the plant-derived line prediction and selection.

#### 3.2. Analysis for Sensitive Wavebands and Optimal Vegetation Indices for Breeding Line Yield-Prediction

^{2}up to 0.68 and 0.50 respectively, and therefore, the best growth stage to collect the UAV hyperspectral reflectance data for yield-prediction using vegetation indices was at R5, while the spectral sensitive bands for soybean yield-prediction were in 454~850 nm. The other growth stages, R2, R6 and R4, were in turn not as good as R5. The 10 vegetation indices were ranked for each of the growth stages in the two yield-tests according to their determination coefficients, NDVI and RVI were all ranked the top two (Table 3). Since NDVI and RVI based on filtered optimized bands are the two most sensitive indices, they were selected for the establishment of yield-prediction models in this research.

#### 3.3. Optimized Reflectance-Sampling Unit-Size for Organizing the UAV Hyperspectral Reflectance Data

^{2}, 1.2~5.2 m

^{2}and 1.0~2.7 m

^{2}for 2ndYYT 2015, 1stYYT 2015 and NJRIKY, respectively (Figure 5). Thus, when the proportion of the sampling unit-size in that of the total plot was between about 20% to 80%, the canopy reflectance data obtained could be used for plot-yield prediction. In the establishment of prediction model below, the upper-side of the optimal sampling unit-area was preferred since all the hyperspectral data can be obtained from one flight and no additional expense was needed.

#### 3.4. Identification of Major Factors for the Establishment of Plot-Yield Prediction Models

^{2}between the RVI and NDVI were not significant and the R

^{2}of linear function of all material sets were somewhat larger and more stable. Among the models in Table S4, the linear regression y = 3E-05x + 0.6526 (x = RVI (618, 674)) with R

^{2}of 0.61 and y = −2E-05x + 0.2055 (x = NDVI (618, 674)) with R

^{2}of 0.61 both for A1 + B1 (1stYYT 2015); the two linear regressions composed of NDVI or RVI both with R

^{2}of 0.49 for A2 + B2 (2ndYYT 2015). The similar situation was observed for other material groups, such as A1, B1, A2, B2, etc., which indicates both NDVI and RVI were relevant in the construction of plot-yield prediction models. Based on the aforementioned, a linear function with two vegetation indices (namely NDVI and RVI) at R5 stage was established for the second round of the yield-prediction models assessment (Table S6).

#### 3.5. Establishment and Evaluation of Yield-Prediction Models Using Normalized Difference Vegetation Index (NDVI) and Ration Vegetation Index (RVI) at R5

_{M}

^{2}and the modelling root mean square error (RMSE

_{M}) and their verification precision, including the verification determination coefficient R

_{V}

^{2}and the verification root mean square error (RMSE

_{V}). For a comprehensive evaluation to balance the modelling and verification, these two parts were summed up as R

_{S}

^{2}and RMSE

_{S}, respectively. In Table 4, the model M

_{A1}presented the largest R

_{S}

^{2}= 1.30, in turn followed by M

_{A1+B1}, M

_{B1}, M

_{A5}, M

_{A2}, M

_{A6}and M

_{B5}with R

_{S}

^{2}1.21, 1.19, 1.19, 1.13, 1.12 and 1.06. Their corresponding RMSE

_{S}were 0.541, 0.651, 0.740, 0.580, 0.503, 0.519 and 0.674, respectively. These models were established from modelling sample size from 48 to 266 lines from a single yield-test. As for the models M

_{A4}, M

_{B4}, and M

_{A4+B4}based on modelling a sample size of 275~551 lines composed from three sets of yield-tests, their R

_{S}

^{2}were all 0.91 and RMSE

_{S}were 0.724, 0.802 and 0.819, respectively. The other models were inferior to the above ones with respect to their precision.

#### 3.6. Establishment and Evaluation of Yield-Prediction Models Using NDVI and RVI at Multiple Stages

^{2}was 0.73. The best combination of the three growth stages were R2, R5, and R6; when 10 vegetation index variables were introduced, the maximum model R

^{2}was 0.74. As the number of growth stages and vegetation indices increased, the model R

_{M}

^{2}increased but not significantly, Table S6 shows that two growth stages models are better than single-stage models, the combinations of R2 and R5, then R6 and R5, R4 and R5 are in turn better than the others among the two-stage models. However, not very large difference was among the vegetation index numbers involved, so less number (2 vegetation indices) was preferred for simplicity of the models.

_{M}

^{2}, R

_{V}

^{2}and R

_{S}

^{2}) and root mean squares error (RMSE

_{M}, RMSE

_{V}and RMSE

_{S}) for all the 17 material sets. Among the different material sets, the best set of models were those obtained from 1stYYT 2015, i.e., models of M

_{A1+B1-2}, M

_{A1-1}, M

_{A1-2}, M

_{A1-3}and M

_{B1-2}; the second were those from 2ndYYT 2015, i.e., models of M

_{A2+B2-2}, M

_{A2-2}, M

_{B2-2}; the third were those of M

_{A6+B6-2}, M

_{A6-1}, M

_{A6-2}and M

_{A6-3}, but not M

_{B6-2}, and M

_{A5-2}and M

_{B5-2}; the fourth were those from the total of the three sets of breeding lines, i.e., models of M

_{A4+B4-2}, M

_{A4-2}, M

_{B4-2}. This situation coincides roughly with the situation of the R5 single growth-stage models that the model precision depends on their source materials. Those from a same test were usually better than those from different tests even the sample size (number of total lines) increased, such as M

_{A1+B1-2}and M

_{A2+B2-2}but not M

_{A3+B3-2}are better than M

_{A4+B4-2}.

_{S}

^{2}of M

_{A1-1}, M

_{A1-2}, M

_{A1-3}, M

_{B1-2}and M

_{A1+B1-2}models (-1 means R5 and R2, -2 means R5 and R6, -3 means R5 and R4) were 1.41, 1.34, 1.32, 1.24 and 1.22 with the RMSE

_{S}0.457, 0.540, 0.541, 0.640 and 0.631, respectively. Among the 2ndYYT 2015 models, the R

_{S}

^{2}of M

_{A2+B2-2}, M

_{A2-2}and M

_{B2-2}models were 1.28, 1.17 and 1.00 with the RMSE

_{S}0.703, 0.606 and 0.603, respectively. Among the material sets with two years data, the R

_{S}

^{2}of M

_{A6+B6-2}, M

_{A6-1}, M

_{A6-2}, M

_{A6-3}and M

_{B6-2}were 1.03, 1.17, 1.15, 1.17, and 0.44 with the RMSE

_{S}0.680, 0.517, 0.550, 0.550 and 0.709, respectively. The R

_{S}

^{2}of M

_{A5-2}and M

_{B5-2}were 1.26 and 1.09 with their RMSE

_{S}0.622 and 0.615, respectively. Among the combined material sets, the R

_{S}

^{2}of M

_{A4+B4-2}, M

_{A4-2}and M

_{B4-2}models were 0.94, 0.94 and 0.93 with their RMSE

_{S}0.761, 0.653 and 0.814. From the above, the superior models were constructed from A1, A1+B1, B1, A2+B2, A5, A6 material sets, the superior growth stage combination was R5+R4, provided the best vegetation index combination was NDVI and RVI. All the models were potential for breeding line yield selection except those of M

_{A3+B3-2}, M

_{A3-2}, M

_{B3-2}and M

_{B6-2}, while M

_{A4+B4-2}, M

_{A4-2}and M

_{B4-2}were for further checking.

#### 3.7. Further Comparison and Selection of Best-Fitted Plot-Yield Prediction Models for Yield Breeding Programs

_{V}) for all the breeding line sets tested (in a total of 1103 lines), the other was to evaluate the coincidence between the model-predicted and breeders’ actual yield selection results.

_{V}). All the models were evaluated with the three sets of yield-tested breeding lines 1stYYT 2015 (A1 + B1), 2ndYYT 2015 (A2 + B2), 2ndYYT 2016 (A3 + B3) and their total set (A4 + B4). The models M

_{A1-2}, M

_{A2+B2-2}, M

_{A2-2}and M

_{A6-2}are models with less RMSE

_{V}for all the four breeding line sets in addition to higher determination coefficient in Table 5, while the models M

_{A4+B4-2}, M

_{A4-2}and M

_{B4-2}were of small RMSE

_{V}for all the four material sets but with medium size of determination coefficient in Table 5.

_{A1-2}, M

_{A2+B2-2}, M

_{A2-2}and M

_{A6-2}) selected from Table 6. After a further comparison comprehensively, the models of M

_{A1-2}, M

_{A6-2}and M

_{A4-2}were good in coincidence rates for all the selection categories (eliminated, reserved and promoted) in all the populations and were chosen for utilization in plot-yield prediction in yield breeding programs (see Table 7 and its notes for details).

_{A1-2}is a linear model derived from the material set which is a first part with 133 breeding lines of the 1stYYT 2015, with its yield ranging between 1.836 and 4.680 t ha

^{−1}, growth period ranging between 99 d and 112 d. M

_{A4-2}is also a linear model derived from the material set which is a first part with 275 breeding lines of the three sets of tests, with its yield ranging between 1.656 and 4.757 t ha

^{−1}, growth period ranging between 96 d and 116 d. M

_{A6-2}is also a linear model derived from the material set which is a group of the selected and retained breeding lines from 1stYYT 2015 and 2ndYYT 2015 with two years’ data of 106 breeding lines, with its yield ranging between 2.380 and 4.925 t ha

^{−1}, growth period ranging between 101 d and 116 d. The formulae of the three recommended and other prediction models are listed in Table S8 with their corresponding hyperspectral reflectance bands. The three plot-yield prediction models can be used for breeding lines in yield-test nurseries within the corresponding yield and growth period range, single model or all the three models can be used simultaneously in a same yield-test nursery.

_{A1-2}and M

_{A4-2}(but not M

_{A6-2}) were also suitable for yield-prediction of the plant-derived-line selection.

## 4. Discussion

_{A1-2}, M

_{A4-2}and M

_{A6-2}for yield-test nurseries and the former two for plant-derived line nursery.

^{2}value of 0.66. Wu et al. [27] obtained the canopy spectral reflectance information of 30 soybean cultivars using the FieldSpec Pro FR2500 Analytical SpectralDevice (ASD) and constructed a large number of spectral parameters. The multiple regression values of the yield obtained with NPH1280 at flowering stage (R2), V_Area1190 at full podding stage (R4), and NPH560 at initial seed filling stage (R5) were found to provide the best yield prediction with an R

^{2}value of 0.68. Qi [54] systematically studied a method to monitor soybean yields based on the FieldSpec Pro FR2500 hyperspectral spectrometer, but the application of the method is limited due to its low accuracy, low efficiency, and inability to obtain the data in real time and for a large area. Sankaran et al [67] found that the vehicle-mounted platform achieved rapid and non-destructive acquisition of plant phenotype information under field conditions. However, this method has a limitation in a crop-planting scheme and low operational efficiency in large areas. Anyway, our results on vegetation index selection (NDVI and RVI), R5 growth stage for remote-sensing are basically in accordance with the previous results, but our results on regression type was consistently multiple linear regression model while curvilinear regressions involved in other reports. The especially meaningful element results in the present study is those of the sampling-unit size of hyperspectral reflectance which is a specific requirement due to the UAV-based remote sensing covering a whole plot influenced by neighboring plots.

#### 4.1. The Major Elements and Potential Utilization of the Established Plot-Yield Prediction Models

_{A1-2}, M

_{A6-2}and M

_{A4-2}all involve R5 and R4 two growth stages for remote sensing, there is enough time (about one month from R5 to harvesting) for calculating the predicted plot-yield and field checking. Based on the prediction, the selection plan for breeding lines can be prepared, and some of the inferior lines can be eliminated in advance to save labor for harvesting.

_{A1-2}, M

_{A6-2}and M

_{A4-2}can be used for model-assisted selection for yield-tests in higher ranks of nurseries. While M

_{A1-2}and M

_{A6-2}can be used for plant-derived line selection in early nursery. At this stage, the plant-derived-line experiment is usually without replication, therefore, the real field selection with reference to model-based selection must be more efficient and effective than the ordinary procedure.

#### 4.2. Potential Improvement of Plot-Yield Prediction Models in Soybean Breeding Program

_{A1+B1-2}better than M

_{A3+B3-2}, and M

_{A1-2}and M

_{B1-2}better than M

_{A2-2}and M

_{B2-2}. Different years (environment) may cause different model precision even for a same material set, such as M

_{A6-2}better than M

_{B6-2}. Thus it was recognized that the model precision depends on their source breeding lines, those from a same test was usually better than those from different tests even the sample size (number of total lines) increased, such as M

_{A1+B1}and M

_{A2+B2}(but not M

_{A3+B3}) were better than M

_{A4+B4}, and different material tests may provide different model precision, such as M

_{A1+B1}is better than M

_{A3+B3}, and M

_{A1}and M

_{B1}better than M

_{A2}and M

_{B2}, and different year (environment) may cause different model precision even for a same set of breeding lines, such as M

_{A6}better than M

_{B6}. Based on the above points, the optimal models were selected as M

_{A1-2}, M

_{A2+B2-2}, M

_{A2-2}and M

_{A6-2}with less RMSE

_{V}and higher R

_{V}

^{2}and M

_{A4+B4-2}, M

_{A4-2}and M

_{B4-2}with small RMSE

_{V}and medium size of R

_{V}

^{2}, and finally combined with the real breeding decision, M

_{A1-2}, M

_{A6-2}and M

_{A4-2}were chosen for utilization in practical breeding programs in Shandong Shofine Seed Technology Co. Ltd.

_{M}

^{2}and modelling RMSE

_{M}(calculated from the random half population), verification R

_{V}

^{2}and verification RMSE

_{V}(calculated from another half population) and their sums R

_{S}

^{2}and RMSE

_{S}were compared and used. However, the three sets of indicators for M

_{A1-2}, M

_{A6-2}and M

_{A4-2}were 0.71, 0.63, 0.55 (R

_{M}

^{2}) and 0.308, 0.290, 0.381 (RMSE

_{M}), 0.63, 0.52, 0.39 (R

_{V}

^{2}) and 0.232, 0.260, 0.272 (RMSE

_{V}) and 1.34, 1.15, 0.94 (R

_{S}

^{2}) and 0.540, 0.550, 0.653 (RMSE

_{S}), respectively (Table 5). It is obvious that the determination coefficients are not very high even the RMSEs are relatively low. Therefore, we have to consider how to improve the models for a more precise prediction. Since in the present study we have noticed with regard to the optimal combination of the model construction elements that an increase of vegetative indices in a model did not increase R

_{M}

^{2}very much (Table S6) and an increase of hyperspectral reflectance stages did not increase R

_{M}

^{2}very much but increased R

_{V}

^{2}obviously (Table 4 and Table 5), two additional elements might be potential for the improvement of model precision. One is the precision of the experiment, the other is the representativeness of the breeding lines used for model establishment. In the present study, the error term CVs were 19.18% and 15.89% for 1stYYT 2015 and 2ndYYT 2015, respectively, this is a somewhat larger experiment error, it may have caused the not high enough determination coefficient. However, the error term CV of the 2ndYYT 2016 was 12.81% which is less than the other two yield-tests. The models established from 1stYYT 2015 (A1 + B1) are all better in R

_{M}

^{2}, but the models constructed from 2ndYYT 2016 (A3 + B3) are all poorer in R

_{M}

^{2}. While the models based on A4 + B4 which were combined from the three set of the tested breeding lines are all good in R

_{M}

^{2}. Therefore, experimental precision is not the only reason, it must be related to the representativeness of the breeding lines. Thus, for the establishment of a precise prediction model, both experiment precision and the representativeness of the breeding lines should be well-controlled.

#### 4.3. Innovation Potential of Plant Breeding Nursery System Using UAV-Based Hyperspectral Reflectance Techniques

^{2}, 1.2~5.2 m

^{2}and 1.0~2.7 m

^{2}for three different tests), the CVs were about the similar without very large difference, indicating the homogeneity of the hyperspectral reflectance between the central 20% to 80% of a plot if the plant in a plot has a normal uniform growth. This means that even 20% of the plot size can obtain the hyperspectral reflectance data as precise as 80% of the plot size. To make sure of the data precision and full-use of the data, we used the larger sampling unit data in our model establishment.

_{A1-2}and M

_{A6-2}can fit for plant-derived line yield prediction. The reason for that is the high density of the hyperspectral reflectance points and canopy homogeneity in a small area.

## 5. Conclusions

_{M}

^{2}and modelling RMSE

_{M}, verification R

_{V}

^{2}and verification RMSE

_{V}, and their sums R

_{S}

^{2}and RMSE

_{S}were evaluated and compared. Integrated with the coincidence rate between the model-predicted results and the real selection results, the models of M

_{A1-2}, M

_{A6-2}and M

_{A4-2}were chosen for utilization in real breeding programs. Here M

_{A1-2}is a linear model appropriate for local yield in 1.836~4.680 t ha

^{−1}, a growth period in 99 d~112 d; M

_{A4-2}is also a linear model appropriate for local yield in 1.656~4.757 t ha

^{−1}, a growth period in 96 d~116 d; M

_{A6-2}is also a linear model appropriate for local yield in 2.380~4.925 t ha

^{−1}, a growth period in 101 d~116 d. The established model construction elements and methods could be used in the establishment of local models for pre-harvest yield-selection and post-harvest integrated yield-selection in advanced breeding nurseries as well as yield potential prediction in plant-derived-line nurseries, furthermore, these models can be used jointly for plot-yield prediction in soybean breeding programs.

## Supplementary Materials

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**The field experiment and canopy hyperspectral reflectance measurement using an unmanned aerial vehicle (UAV) equipped with a remote-sensing monitoring system. (

**A**) A map showing Jiaxiang district in Jining City, Shandong Province. (

**B**) An unmanned aerial vehicle image of 894 soybean plots of the second-year yield-test (2ndYYT 2016) field (acquired on 2 August 2016). The resolution of the UAV is 0.01 m while the flight altitude is 50 m. The extraction area of each plot is 2.1~8.1 m

^{2}in different yield-tests, the number of spectral points collected per plot was 21,000~81,000 (2.1~8.1 m

^{2}/(0.01 × 0.01)).

**Figure 2.**A DJI Spreading Wings S1000+ equipped with Cubert UHD185 (for obtaining stable soybean canopy hyperspectral reflectance data) and Sony DSC-QX100 (For hyperspectral image stitching correction).

**Figure 3.**Correlation between canopy spectral reflectance and soybean plot yield in 2ndYYT 2015 (

**A**), 1stYYT 2015 (

**B**), NJRIKY test 2015 (

**C**).

**Figure 4.**The contour map of determination coefficients (R

^{2}) in linear regression of plot yield on any two-band NDVI and RVI at R5 stage in the 2ndYYT 2015 (

**A**) and 1stYYT 2015 (

**B**). Zone a and Zone b (dark red) are the high correlation zone which showing that the sensitive band is located between 550 nm and 750 nm.

**Figure 5.**Coefficient of error variation (CV) of the hyperspectral reflectance values at red and near-infrared band and CV of the three vegetation indices values varied with sampling areas at R5 stage. (A2 + B2 = 2ndYYT 2015, A1 + B1 = 1stYYT 2015 and A + B = NJRIKY).

Vegetation Index | Full Name of Index | Algorithm Formula | Reference |
---|---|---|---|

NDVI | Normalized Difference Vegetation Index | (R_{x1} − R_{x2})/(R_{x1} + R_{x2}) | [57] |

RVI | Ratio Vegetation Index | R_{x1}/R_{x2} | [58] |

VOG1 | Vogelmann Red Edge Index 1 | R_{740}/R_{720} | [59] |

GNDVI | Green Normalized Difference Vegetation Index | (R_{780} − R_{550})/(R_{780} + R_{550}) | [60] |

NDVI_{705} | Normalized Difference Vegetation Index_{705} | (R_{750} − R_{705})/(R_{750} + R_{705}) | [61] |

PVI | Perpendicular Vegetation Index | (R_{NIR} − aR_{Red} − b)/(1 + a^{2}) | [62] |

RDVI | Renormalized Difference Vegetation Index | (R_{800} − R_{670})/(R_{800} + R_{670}) | [63] |

OSAVI | Optimized Soil-Adjusted Vegetation Index | (1 + 0.16)(R_{800} − R_{670})/(R_{800} + R_{670} + 0.16) | [64] |

EVI | Enhanced Vegetation Index | 2.5(R_{NIR} − R_{680})/(1 + R_{NIR} + 6R_{680} − 7.5R_{460}) | [65] |

DVI | Difference Vegetation Index | R_{NIR} − R_{Red} | [66] |

_{x1}and R

_{x2}represent hyperspectral reflectance bands in near infrared and visible red region, respectively.R

_{740}, R

_{720}, R

_{780}, etc. represent hyperspectral reflectance of bands at 740, 720 and 780 nm, etc.

**Table 2.**The frequency distribution of plot yield means averaging over replications in three sets of soybean breeding lines and one set of plant-to-lines tested in 2015–2016.

Material Data Set | Class Limit (t ha^{−1}) | Range (t ha ^{−1}) | Mean (t ha ^{−1}) | GCV (%) | CV (%) | F-Value | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

<2.0 | 2.0–2.3 | 2.3–2.6 | 2.6–2.9 | 2.9–3.2 | 3.2–3.5 | 3.5–3.8 | 3.8–4.1 | >4.1 | Σ | ||||||

1stYYT 2015 | 6 | 28 | 53 | 59 | 83 | 80 | 86 | 72 | 65 | 532 | 1.83–4.99 | 3.32 | 34.85 | 19.18 | 3.30 ^{**} |

2ndYYT 2015 | 1 | 2 | 17 | 25 | 42 | 44 | 53 | 51 | 39 | 274 | 1.65–4.91 | 3.50 | 29.35 | 15.89 | 3.41 ^{**} |

2ndYYT 2016 | 6 | 9 | 31 | 58 | 80 | 59 | 35 | 16 | 3 | 297 | 1.72–4.41 | 3.06 | 26.90 | 12.81 | 4.87 ^{**} |

NJRIKY2015 | 166 | 121 | 101 | 37 | 11 | 5 | 0 | 0 | 0 | 441 | 1.08–3.39 | 2.14 | 33.15 | 33.31 | 0.99 ^{**} |

**Table 3.**The sensitive bands and determination coefficient ranks of the 10 vegetation indices calculated from hyperspectral reflectance and plot yield in two sets of breeding lines yield-tested in 2015.

Item | R2 | R4 | R5 | R6 | |||||
---|---|---|---|---|---|---|---|---|---|

Breeding line yield-test | 1s tYYT | 2nd YYT | 1s tYYT | 2nd YYT | 1st YYT | 2nd YYT | 1st YYT | 2nd YYT | |

Sensitive band (nm) | λ1 | 750 | 482 | 750 | 514 | 634 | 514 | 550 | 550 |

λ2 | 770 | 590 | 770 | 606 | 674 | 606 | 710 | 710 | |

Vegetation index | NDVI | 1 | 1 | 2 | 2 | 2 | 1 | 2 | 2 |

RVI | 2 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | |

GNDVI | 4 | 4 | 4 | 4 | 9 | 9 | 3 | 9 | |

PVI | 5 | 9 | 9 | 10 | 10 | 10 | 10 | 3 | |

OSASI | 3 | 7 | 3 | 5 | 4 | 4 | 5 | 4 | |

EVI | 9 | 10 | 5 | 6 | 7 | 5 | 4 | 6 | |

RDVI | 6 | 3 | 6 | 9 | 3 | 8 | 6 | 5 | |

VOG1 | 8 | 8 | 8 | 8 | 8 | 7 | 7 | 8 | |

DVI | 10 | 6 | 10 | 3 | 5 | 3 | 9 | 10 | |

NDVI_{705} | 7 | 5 | 7 | 7 | 6 | 6 | 8 | 7 | |

Maximum R^{2} | 0.58 | 0.08 | 0.36 | 0.19 | 0.68 | 0.50 | 0.54 | 0.33 |

**Table 4.**Comparisons among the regression models of yield on R5 single-period UAV hyperspectral reflectance data for various sets of breeding lines.

Model Code | Sensitive Band (nm) | Material No. | Model Precision | Verification Precision | Sum Precision | |||||
---|---|---|---|---|---|---|---|---|---|---|

λ1 | λ2 | Model | Verifi-Cation | R_{M}^{2} | RMSE_{M}(t ha ^{−1}) | R_{V}^{2} | RMSE_{V}(t ha ^{−1}) | R_{S}^{2} | RMSE_{S}(t ha ^{−1}) | |

M_{A1+B1} | 618 | 674 | 266 | 266 | 0.68 | 0.410 | 0.53 | 0.241 | 1.21 | 0.651 |

M_{A1} | 638 | 674 | 133 | 133 | 0.72 | 0.300 | 0.58 | 0.241 | 1.30 | 0.541 |

M_{B1} | 634 | 678 | 133 | 133 | 0.70 | 0.387 | 0.49 | 0.353 | 1.19 | 0.740 |

M_{A2+B2} | 514 | 606 | 137 | 137 | 0.60 | 0.382 | 0.42 | 0.261 | 1.02 | 0.643 |

M_{A2} | 514 | 614 | 68 | 69 | 0.70 | 0.331 | 0.43 | 0.172 | 1.13 | 0.503 |

M_{B2} | 514 | 582 | 68 | 69 | 0.45 | 0.420 | 0.25 | 0.411 | 0.70 | 0.831 |

M_{A3+B3} | 534 | 570 | 148 | 149 | 0.25 | 0.405 | 0.13 | 0.407 | 0.38 | 0.812 |

M_{A3} | 538 | 570 | 74 | 74 | 0.33 | 0.373 | 0.22 | 0.407 | 0.55 | 0.780 |

M_{B3} | 490 | 754 | 74 | 75 | 0.35 | 0.382 | 0.05 | 0.391 | 0.40 | 0.773 |

M_{A4+B4} | 486 | 618 | 551 | 552 | 0.46 | 0.454 | 0.45 | 0.355 | 0.91 | 0.809 |

M_{A4} | 570 | 730 | 275 | 276 | 0.52 | 0.377 | 0.39 | 0.347 | 0.91 | 0.724 |

M_{B4} | 494 | 618 | 276 | 276 | 0.51 | 0.465 | 0.40 | 0.348 | 0.91 | 0.812 |

M_{A5} | 486 | 586 | 165^{1} | 165^{1} | 0.70 | 0.356 | 0.49 | 0.224 | 1.19 | 0.580 |

M_{B5} | 478 | 738 | 48 ^{1} | 48 ^{1} | 0.68 | 0.378 | 0.38 | 0.296 | 1.06 | 0.674 |

M_{A6+B6} | 554 | 730 | 213 | 213 | 0.50 | 0.429 | 0.39 | 0.338 | 0.89 | 0.767 |

M_{A6} | 638 | 666 | 106 | 107 | 0.61 | 0.301 | 0.51 | 0.218 | 1.12 | 0.519 |

M_{B6} | 694 | 722 | 106 | 107 | 0.30 | 0.362 | 0.11 | 0.370 | 0.41 | 0.732 |

_{M}

^{2}is the model determination coefficient, RMSE

_{M}is the model root mean square error calculated from the difference between the predicted value and the observed value in the lines set from which the model is developed. In the Verification Precision column, R

_{V}

^{2}is the verification determination coefficient, RMSE

_{V}is the verification root mean square error calculated from the difference between the value predicted from the established model and the observed value in the lines set used for verification. In the Sum Precision column, R

_{S}

^{2}and RMSE

_{S}are sums of model and verification determination coefficient (R

_{M}

^{2}+ R

_{V}

^{2}) and root mean square error (RMSE

_{M}+ RMSE

_{V}), respectively. The same is true for later tables.

^{1}These two material sets were tested two years, therefore, the number of observations for modelling and verification are two times of the number of lines.

**Table 5.**The major comprehensive yield prediction models using NDVI and RVI constructed from two growth-period UAV hyperspectral reflectance data.

Model | Sensitive Bands (nm) | Material No. | Model Precision | Verification Precision | Sum Precision | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

R5 λ1 | R5 λ2 | R4 λ1 | R4 λ2 | Mo-del | Verification | R_{M}^{2} | RMSE_{M}(t ha ^{−1}) | P | R_{V}^{2} | RMSE_{V}(t ha ^{−1}) | P | R_{S}^{2} | RMSEs (t ha ^{−1}) | |

M_{A1+B1-2} (R5 + R4) | 618 | 674 | 750 | 770 | 266 | 266 | 0.71 | 0.364 | 2.68E-63 | 0.51 | 0.267 | 1.84E-47 | 1.22 | 0.631 |

M_{A1-1} (R5 + R2) | 638 | 674 | 722 | 730 | 133 | 133 | 0.74 | 0.315 | 2.36E-35 | 0.67 | 0.142 | 8.98E-33 | 1.41 | 0.457 |

M_{A1-2} (R5 + R4) | 638 | 674 | 554 | 850 | 133 | 133 | 0.71 | 0.308 | 1.57E-34 | 0.63 | 0.232 | 6.98E-28 | 1.34 | 0.540 |

M_{A1-3} (R5 + R6) | 638 | 674 | 586 | 698 | 133 | 133 | 0.73 | 0.333 | 1.53E-34 | 0.59 | 0.208 | 5.62E-28 | 1.32 | 0.541 |

M_{B1-2} (R5 + R4) | 634 | 678 | 754 | 770 | 133 | 133 | 0.71 | 0.385 | 1.94E-33 | 0.53 | 0.255 | 4.44E-23 | 1.24 | 0.640 |

M_{A2+B2-2} (R5 + R4) | 514 | 606 | 618 | 670 | 137 | 137 | 0.65 | 0.348 | 9.88E-28 | 0.63 | 0.355 | 2.91E-15 | 1.28 | 0.703 |

M_{A2-2} (R5 + R4) | 514 | 614 | 518 | 570 | 68 | 69 | 0.68 | 0.293 | 2.73E-15 | 0.49 | 0.313 | 2.40E-09 | 1.17 | 0.606 |

M_{B2-2} (R5 + R4) | 514 | 582 | 786 | 850 | 68 | 69 | 0.61 | 0.374 | 9.93E-13 | 0.39 | 0.229 | 8.72E-09 | 1.00 | 0.603 |

M_{A3+B3-2} (R5 + R4) | 534 | 570 | 706 | 714 | 148 | 149 | 0.29 | 0.431 | 1.62E-10 | 0.12 | 0.337 | 0.0001 | 0.41 | 0.768 |

M_{A3-2} (R5 + R4) | 538 | 570 | 634 | 730 | 74 | 74 | 0.42 | 0.425 | 9.76E-08 | 0.31 | 0.113 | 0.003 | 0.73 | 0.538 |

M_{B3-2} (R5 + R4) | 490 | 754 | 702 | 714 | 74 | 75 | 0.29 | 0.411 | 3.22E-05 | 0.19 | 0.325 | 0.0001 | 0.48 | 0.736 |

M_{A4+B4-2} (R5 + R4) | 486 | 618 | 554 | 742 | 551 | 552 | 0.52 | 0.445 | 1.41E-85 | 0.42 | 0.316 | 8.75E-65 | 0.94 | 0.761 |

M_{A4-2} (R5 + R4) | 570 | 730 | 554 | 742 | 275 | 276 | 0.55 | 0.381 | 1.24E-40 | 0.39 | 0.272 | 2.76E-34 | 0.94 | 0.653 |

M_{B4-2} (R5 + R4) | 494 | 618 | 642 | 678 | 276 | 276 | 0.50 | 0.475 | 3.08E-40 | 0.43 | 0.339 | 5.58-34 | 0.93 | 0.814 |

M_{A5-2} (R5 + R4) | 486 | 586 | 622 | 742 | 165 ^{1} | 165 ^{1} | 0.67 | 0.359 | 1.42E-37 | 0.53 | 0.263 | 1.75E-27 | 1.26 | 0.622 |

M_{B5-2} (R5 + R4) | 478 | 738 | 634 | 738 | 48 ^{1} | 48 ^{1} | 0.68 | 0.345 | 2.93E-10 | 0.41 | 0.270 | 7.49E-07 | 1.09 | 0.615 |

M_{A6+B6-2} (R5 + R4) | 554 | 730 | 622 | 738 | 213 | 213 | 0.57 | 0.402 | 2.90E-37 | 0.46 | 0.278 | 1.09E-30 | 1.03 | 0.680 |

M_{A6-1} (R5 + R2) | 638 | 666 | 754 | 770 | 106 | 107 | 0.63 | 0.303 | 1.88E-21 | 0.54 | 0.214 | 1.86E-19 | 1.17 | 0.517 |

M_{A6-2} (R5 + R4) | 638 | 666 | 754 | 774 | 106 | 107 | 0.63 | 0.290 | 4.71E-21 | 0.52 | 0.260 | 4.35E-17 | 1.15 | 0.550 |

M_{A6-3} (R5 + R6) | 638 | 666 | 554 | 710 | 106 | 107 | 0.64 | 0.301 | 5.77E-22 | 0.53 | 0.249 | 1.89E-19 | 1.17 | 0.550 |

M_{B6-2} (R5 + R4) | 694 | 722 | 706 | 774 | 106 | 107 | 0.33 | 0.397 | 1.67E-08 | 0.11 | 0.312 | 0.0004 | 0.44 | 0.709 |

_{A1-1}, M

_{A1-2}and M

_{A1-3}: the models based on the yield of A1 material set and the corresponding hyperspectral reflectance data of R5 and R2, R5 and R4, R5 and R6, respectively; M

_{A6-1}, M

_{A6-2}and M

_{A6-3}: the models based on the yield of A6 material set and the corresponding hyperspectral reflectance data of R5 and R2, R5 and R4 and R5 and R6, respectively; M

_{A4+B4-2}, M

_{A4-2}, M

_{B4-2}, M

_{A1+B1-2}, M

_{A6+B6-2}, M

_{A2+B2-2}, M

_{A3+B3-2}, etc.: the models based on the yield of A4+B4, A4, B4, A1+B1, A6+B6, A2+B2, A3+B3 etc. material set and the corresponding hyperspectral reflectance data of R5 and R4, respectively. R

_{M}

^{2}, R

_{V}

^{2}, R

_{S}

^{2}, RMSE, RMSE

_{M}and RMSE

_{V}: the same as in Table 4. P: P values of model significant test, expressed in exponential notation, such as, 2.68E-63, that is 2.68 multiplied by 10

^{−63}.

^{1}These two material sets were tested for two years, therefore, the number of observations for modelling and verification are two times the number of lines.

**Table 6.**Comparisons of the verification RMSE in A1 + B1, A2 + B2, A3 + B3 and A4 + B4 among models in Table 5.

Model | Growth Period Range (d) | Yield Range/ (t ha ^{−1}) | RMSE_{V} of (A1 + B1)(t ha ^{−1}) | RMSE_{V} of (A2 + B2)(t ha ^{−1}) | RMSE_{V} of (A3 + B3)(t ha ^{−1}) | RMSE_{V} of (A4 + B4)(t ha ^{−1}) |
---|---|---|---|---|---|---|

M_{A1+B1-2} | 99~113 | 1.831~4.995 | 0.440 | 0.536 | 0.932 | 0.632 |

M_{A1-1} | 99~112 | 1.836~4.680 | 0.473 | 1.037 | - | - |

M_{A1-2} | 99~112 | 1.836~4.680 | 0.433 | 0.486 | 0.663 | 0.517 |

M_{A1-3} | 99~112 | 1.836~4.680 | 0.463 | 0.509 | - | - |

M_{B1-2} | 99.7~113 | 1.831~4.995 | 0.460 | 0.547 | 1.620 | 0.940 |

M_{A2+B2-2} | 103~116 | 1.656~4.917 | 0.587 | 0.428 | 0.561 | 0.545 |

M_{A2-2} | 106~116 | 1.656~4.757 | 0.545 | 0.421 | 1.137 | 0.732 |

M_{B2-2} | 103~116 | 2.043~4.917 | 1.555 | 1.635 | 1.655 | 1.604 |

M_{A3+B3-2} | 96~116 | 1.724~4.410 | 6.651 | 6.940 | 5.260 | 6.390 |

M_{A3-2} | 96~116 | 1.724~4.304 | 1.881 | 2.029 | 1.694 | 1.873 |

M_{B3-2} | 99~115 | 1.820~4.410 | 0.843 | 3.795 | 0.437 | 1.996 |

M_{A4+B4-2} | 96~116 | 1.656~4.995 | 0.442 | 0.462 | 0.475 | 0.457 |

M_{A4-2} | 96~116 | 1.656~4.757 | 0.475 | 0.456 | 0.454 | 0.465 |

M_{B4-2} | 99~116 | 1.820~4.995 | 0.456 | 0.471 | 0.471 | 0.464 |

M_{A5-2} | 99~114 | 2.380~4.925 | 0.956 | 1.346 | 2.214 | 1.488 |

M_{B5-2} | 96~116 | 3.283~4.558 | 0.708 | 1.385 | 0.501 | 0.888 |

M_{A6+B6-2} | 96~116 | 2.380~4.925 | 0.581 | 0.533 | 0.444 | 0.536 |

M_{A6-1} | 101~116 | 2.380~4.925 | 0.522 | 0.553 | - | - |

M_{A6-2} | 101~116 | 2.380~4.925 | 0.501 | 0.547 | 1.022 | 0.690 |

M_{A6-3} | 101~116 | 2.380~4.925 | 0.568 | 0.702 | - | - |

M_{B6-2} | 96~116 | 2.380~4.925 | 0.862 | 2.071 | 0.428 | 1.215 |

_{V}: the verification RMSE value. Model: All models are listed in Table 5.

**Table 7.**Comparisons of coincidence between the breeders’ actual yield selection results and the model-predicted selection results among the models listed in Table 5 for the three yield-tests in 2015–2016 (Coincidence rate expressed in % while actual selection results expressed in number of lines).

Model | A1 + B1 | A2 + B2 | A3 + B3 | A4 + B4 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Eli | Res | Pro | Sum | Eli | Res | Pro | Sum | Eli | Res | Pro | Sum | Eli | Res | Pro | Sum | |

Actual selection | 177 | 203 | 152 | 532 | 60 | 118 | 96 | 274 | 142 | 131 | 24 | 297 | 379 | 452 | 272 | 1103 |

M_{A1+B1-2} | 69.5 | 56.7 | 63.2 | 62.8 | 31. 7 | 49.2 | 65.6 | 51.1 | 20.4 | 9.1 | 0 | 13.8 | 45.1 | 40.9 | 58.5 | 46.7 |

M_{A1-1} | 81.4 | 58.6 | 15.1 | 53.8 | 40.0 | 48.3 | 38.5 | 43.1 | - | - | - | - | 44.3 | 38.9 | 22.1 | 36.6 |

M_{A1-2} | 66.7 | 56.2 | 71.7 | 64.1 | 33.3 | 53.4 | 734.0 | 56.2 | 59.2 | 33.6 | 16. 7 | 44.4 | 58.6 | 48. 9 | 67.75 | 56.8 |

M_{A1-3} | 100.0 | 0 | 0 | 33.3 | 100.0 | 0 | 0 | 21.9 | - | - | - | - | 100.0 | 0 | 0 | 55.2 |

M_{B1-2} | 84.2 | 54.2 | 52.0 | 63.5 | 33.3 | 44.9 | 80.2 | 54.7 | 99.3 | 0 | 0 | 47.5 | 81.8 | 36.1 | 57.4 | 57.0 |

M_{A2+B2-2} | 23.2 | 45.8 | 71.7 | 45.7 | 35.0 | 60.2 | 78.1 | 61.0 | 11.3 | 84.9 | 34. 8 | 45.8 | 20.6 | 61.1 | 70.6 | 49.5 |

M_{A2-2} | 29.4 | 68.0 | 48.7 | 49.6 | 40.0 | 73.7 | 55.2 | 59.9 | 1.4 | 18.9 | 95.7 | 16.5 | 20.6 | 55.3 | 54. 8 | 43.3 |

M_{B2-2} | 1.1 | 0 | 99.3 | 28.8 | 0 | 0 | 100.0 | 35.0 | 0 | 0 | 100.0 | 7.7 | 0.5 | 0 | 99.3 | 24.7 |

M_{A3+B3-2} | 0 | 0 | 100.0 | 28.6 | 0 | 0 | 100.0 | 35.0 | 0 | 0 | 100.0 | 7.7 | 0 | 0 | 99.6 | 24.6 |

M_{A3-2} | 9.0 | 60.1 | 24.3 | 32.9 | 38.3 | 27.1 | 66.7 | 43.4 | 44.4 | 62.9 | 30.4 | 51.5 | 26.9 | 52.4 | 39.7 | 40.5 |

M_{B3-2} | 91.0 | 8.4 | 0 | 33.5 | 56.7 | 59.3 | 7.3 | 40.5 | 73.2 | 41.7 | 17.4 | 54.9 | 78.9 | 31.4 | 4.0 | 41.0 |

M_{A4+B4-2} | 64.4 | 60.6 | 62.5 | 62.4 | 71.7 | 55.9 | 38.5 | 53.3 | 41.6 | 70. 5 | 8.7 | 51.9 | 57.0 | 62.4 | 49.3 | 57.3 |

M_{A4-2} | 61.6 | 68.0 | 39.5 | 57.7 | 50.0 | 55.9 | 83.3 | 64.2 | 33.1 | 87.8 | 0 | 54.6 | 49.1 | 70.6 | 51.5 | 58.5 |

M_{B4-2} | 63.3 | 60.1 | 61.8 | 61.7 | 70.0 | 54.2 | 50.0 | 56.2 | 47.9 | 60.6 | 8.7 | 50.5 | 58.6 | 58.9 | 52.9 | 57.3 |

M_{A5-2} | 94.4 | 33.5 | 27.6 | 52.1 | 96.7 | 14.4 | 7.3 | 29.9 | 97.2 | 0.8 | 0 | 46.8 | 95.8 | 19.0 | 18.0 | 45.2 |

M_{B5-2} | 0 | 81.8 | 4.0 | 32.3 | 0 | 0 | 100 | 35.0 | 33.8 | 73.5 | 13.0 | 49.8 | 12.7 | 58.2 | 38.6 | 37.7 |

M_{A6+B6-2} | 17.5 | 47.3 | 76.3 | 45.7 | 16. 7 | 43.2 | 94.8 | 55.5 | 47.2 | 65.9 | 0 | 51.9 | 28.5 | 51.8 | 76.1 | 49.8 |

M_{A6-1} | 2.3 | 35.5 | 94.1 | 41.2 | 0 | 38.1 | 91.7 | 48.5 | - | - | - | - | 1.1 | 25.9 | 84.9 | 31.9 |

M_{A6-2} | 42.9 | 47.3 | 82.9 | 56.0 | 16.7 | 44.9 | 82.3 | 51.8 | 95.8 | 1.5 | 0 | 46.5 | 58.6 | 33.4 | 75.4 | 52.4 |

M_{A6-3} | 31.1 | 51.7 | 84.9 | 54.3 | 10.0 | 42.4 | 87.5 | 51.1 | - | - | - | - | 16.1 | 34.3 | 78.3 | 38.9 |

M_{B6-2} | 94.4 | 19.2 | 0 | 38.7 | 73.3 | 39.8 | 8.3 | 36.1 | 66.2 | 59.9 | 30.4 | 60.6 | 80.5 | 36.5 | 5.5 | 44.0 |

^{−1}) to be reserved (Res, yields between 3.00 t ha

^{−1}and 3.75 t ha

^{−1}) and to be promoted (Pro, yields above 3.75 t ha

^{−1}) in A1 + B1, A2 + B2 and A3 + B3. The models used in model-predicted yield selection are those listed in Table 5 and Table S8.

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Zhang, X.; Zhao, J.; Yang, G.; Liu, J.; Cao, J.; Li, C.; Zhao, X.; Gai, J.
Establishment of Plot-Yield Prediction Models in Soybean Breeding Programs Using UAV-Based Hyperspectral Remote Sensing. *Remote Sens.* **2019**, *11*, 2752.
https://doi.org/10.3390/rs11232752

**AMA Style**

Zhang X, Zhao J, Yang G, Liu J, Cao J, Li C, Zhao X, Gai J.
Establishment of Plot-Yield Prediction Models in Soybean Breeding Programs Using UAV-Based Hyperspectral Remote Sensing. *Remote Sensing*. 2019; 11(23):2752.
https://doi.org/10.3390/rs11232752

**Chicago/Turabian Style**

Zhang, Xiaoyan, Jinming Zhao, Guijun Yang, Jiangang Liu, Jiqiu Cao, Chunyan Li, Xiaoqing Zhao, and Junyi Gai.
2019. "Establishment of Plot-Yield Prediction Models in Soybean Breeding Programs Using UAV-Based Hyperspectral Remote Sensing" *Remote Sensing* 11, no. 23: 2752.
https://doi.org/10.3390/rs11232752