Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Design and Field Data Collection
2.2. Hyperspectral Image Acquisition and Pre-Processing
2.3. Spectral Feature Extraction and Reduction
2.4. Ensemble Model Development
3. Results
3.1. Yield Statistics and Spectral Profiles
3.2. Feature Importance
3.3. Model Comparison and Performance
3.4. Model Adaptability for Different Compaction Treatments
4. Discussion
4.1. Selection of the Vegetation Indices
4.2. Advantages of the Ensemble Model
4.3. Effects of Machinery Compaction
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix
Feature | Model | R2 | RMSE (kg/ha) | MAE (kg/ha) |
---|---|---|---|---|
First derivatives of full bands | RF | 0.848 | 249.623 | 187.215 |
SVR | 0.845 | 251.307 | 198.938 | |
KNN | 0.823 | 267.859 | 210.981 | |
Ensemble | 0.869 | 231.887 | 180.150 | |
Second derivatives of full bands | RF | 0.820 | 270.324 | 209.001 |
SVR | 0.814 | 275.077 | 217.356 | |
KNN | 0.800 | 283.308 | 217.900 | |
Ensemble | 0.836 | 258.841 | 201.951 | |
First and second derivatives of full bands | RF | 0.856 | 241.011 | 181.244 |
SVR | 0.854 | 243.162 | 195.826 | |
KNN | 0.803 | 281.108 | 217.741 | |
Ensemble | 0.874 | 226.434 | 177.255 | |
Selected VIs | RF | 0.833 | 252.912 | 185.317 |
SVR | 0.842 | 247.593 | 185.869 | |
KNN | 0.850 | 241.430 | 183.557 | |
Ensemble | 0.874 | 220.799 | 164.787 |
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Treatment | Name | Simulated Traffic | Description |
---|---|---|---|
T1 | Single Pass Silage/Hay | Mower | One application of compaction immediately after harvest covering the entire plot. |
T2 | Three Passes Silage | Mower, merger, forage harvester | Three applications of compaction. One immediately after harvest, one 24 h after harvest, and one 26 h after harvest. Full plot application. |
T3 | Five Passes Silage | Mower, merger, rake, forage harvester, transport vehicle | Five applications of compaction. One immediately after harvest, two passes 24 h after harvest, and two passes 26 h after harvest. Full plot application. |
T4 | Simulated Silage producer | Mower, merger or rake, forage harvester, transport vehicle | Two-wheel tracks applied within the plot. One pass immediately after harvest, one pass 24 h after harvest, and two passes 26 h after harvest. |
T5 | Three Passes Hay | Mower, merger or rake, bailer | Three applications of compaction. One immediately after harvest, one 48 h after harvest, and one 72 h after harvest. Full plot application. |
T6 | Five Passes Hay | Mower, merger, rake, bailer, transport vehicle | Five applications of compaction. One immediately after harvest, two passes 48 h after harvest, and two passes 72 h after harvest. Full plot application. |
T7 | Zero Passes | No | No machine traffic applied. |
Full Form | Index | Formula | Reference |
---|---|---|---|
Normalized difference vegetation index | NDVI[471,584] | (R584 − R471)/(R584 + R471) | [69] |
NDVI[521,689] | (R689 − R521)/(R689 + R521) | [69] | |
NDVI[550,760] | (R760 − R550)/(R760 + R550) | [70] | |
NDVI[667,740] | (R740 − R667)/(R740 + R667) | [71] | |
NDVI[670,800] | (R800 − R670)/(R800 + R670) | [72] | |
NDVI[705,750] | (R750 − R705)/(R750 + R705) | [73] | |
NDVI[710,750] | (R750 − R710)/(R750 + R710) | [74] | |
NDVI[710,780] | (R780 − R710)/(R780 + R710) | [75] | |
NDVI[717,732] | (R750 − R710)/(R750 + R710) | [76] | |
NDVI[717,770] | (R732 − R717)/(R732 + R717) | [76] | |
NDVI[720,820] | (R820 − R720)/(R820 + R720) | [77] | |
NDVI[734,750] | (R750 − R735)/(R750 + R734) | [76] | |
Physiological reflectance index | PRI[528,567] | (R528 − R567)/(R528 + R567) | [78] |
PRI[531,570] | (R570 − R531)/(R531 + R570) | [79] | |
Normalized difference red edge | NDRE | (R790 − R720)/(R790 + R720) | [80] |
Modified normalized difference vegetation index | mND705 | (R750 − R705)/(R750 + R705 − 2R445) | [81] |
Green normalized difference vegetation index | GNDVI | (R750 − R550)/(R750 + R550) | [82] |
Renormalized difference vegetation index | RDVI | ) | [83] |
Normalized difference cloud index | NDCI | (R762 − R527)/(R762 + R527) | [84] |
Curvature index | CI | R675 × R690/R6832 | [85] |
- | Datt1 | (R850 − R710)/(R850 − R680) | [86] |
Datt2 | R850/R710 | ||
Datt3 | R754/R704 | ||
Double Difference index | DD | (R749 − R720) − (R701 − R672) | [87] |
Double peak canopy nitrogen index | DCNI | (R720 − R700)/[(R700 − R670)(R720 − R670 + 0.03)] | [88] |
- | Gitelson1 | 1/R700 | [89] |
Gitelson2 | (R750-R800/R695-R740) − 1 | [90] | |
- | Carte1 | R695/R760 | [91] |
Carte2 | R605/R760 | ||
Carte3 | R710/R760 | ||
Carte4 | R695/R670 | ||
Simple ratio index | SRI[533,565] | R565/R533 | [92] |
SRI[550,750] | R750/R550 | [93] | |
SRI[550,760] | R760/R550 | [70] | |
SRI[560,810] | R810/R560 | [94] | |
SRI[629,734] | R734/R629 | [71] | |
SRI[660,810] | R810/R660 | [95] | |
SRI[670,700] | R700/R670 | [96] | |
SRI[670,800] | R800/R670 | [88] | |
SRI[675,700] | R675/R700 | [97] | |
SRI[680,800] | R800/R680 | [81] | |
SRI[690,752] | R752/R690 | [93] | |
SRI[700,750] | R750/R700 | [93] | |
SRI[705,750] | R750/R705 | [73] | |
SRI[706,755] | R706/R755 | [76] | |
SRI[708,747] | R747/R708 | [98] | |
SRI[710,750] | R750/R710 | [99] | |
SRI[717,741] | R741/R717 | [98] | |
SRI[720,735] | R735/R720 | [98] | |
SRI[720,738] | R738/R720 | [98] | |
Modified simple ratio index | mSRI[550,780] | R780/R550-1 | [100] |
mSRI[710,780] | R780/R710-1 | [101] | |
mSRI[720,750] | R750/R720-1 | [100] | |
mSR705 | (R750 − R445)/(R705 − R445) | [86] | |
mSR | [102] | ||
New vegetation index | NVI1 | (R777 − R747)/R673 | [103] |
NVI2 | R705/(R717 + R491) | [92] | |
Enhanced vegetation index | EVI | 2.5(R800 − R670)/(R800 − 6R670 − 7.5R475 + 1) | [104] |
Transformed Chlorophyll absorption in reflectance index | TCARI1 | 3[(R700 − R670) − 0.2(R700 − R550)(R700/R670)] | [105] |
TCARI2 | 3[(R750 − R705) − 0.2(R750 − R550)(R750/R705)] | [106] | |
Modified chlorophyll absorption ratio index | MCARI1 | [(R700 − R670) − 0.2(R700 − R550)](R700/R670) | [15] |
MCARI2 | [(R750 − R705) − 0.2(R750 − R550)](R750/R705) | [106] | |
MCARI3 | [(R750 − R710) − 0.2(R750 − R550)](R750/R715) | [106] | |
Optimized soil-adjusted vegetation index | OSAVI1 | (1 + 0.16)(R800 − R670)/(R800 + R670 + 0.16) | [107] |
OSAVI2 | (1 + 0.16)(R750 − R705)/(R750 + R705 + 0.16) | [106] | |
Combined TCARI/OSAVI | TCARI/OSAVI1 | TCARI1/OSAVI1 | [105] |
TCARI/OSAVI2 | TCARI2/OSAVI2 | [106] | |
Combined MCARI/OSAVI | MCARI/OSAVI1 | MCARI1/OSAVI1 | [106] |
MCARI/OSAVI2 | MCARI2/OSAVI2 | [106] | |
Triangular greenness index | TGI | −0.5[190(R670-R550) − 120(R670 − R480)] | [108] |
Modified triangular vegetation index | MTVI | 1.2[1.2(R800 − R550) − 2.5(670 − R550)] | [109] |
MERIS terrestrial chlorophyll index | MTCI1 | (R750 − R710)/(R710 − R680) | [110] |
MTCI2 | (R754 − R709)/(R709 − R681) | ||
Spectral polygon vegetation index | SPVI | 0.4 × [3.7(R800 − R670) − 1.2|R550 − R670|] | [111] |
Red edge position index | REP1 | 700 + 45[(R670 + R780)/2 − R700]/(R740 − R700) | [69] |
REP2 | 700 + 40[(R670 + R780)/2 − R700]/(R740 − R700) | [112] | |
- | VOG1 | R740/R720 | [113] |
VOG2 | (R734 − R747)/(R715 + R726) | ||
VOG3 | (R734 − R747)/(R715 + R720) | ||
Optimal vegetation index | Viopt | (1 + 0.45)(R8002 + 1)/(R670 + 0.45) | [114] |
Harvesting Time | Treatment | Mean (kg/ha) | Max. (kg/ha) | Min. (kg/ha) | STD (kg/ha) |
---|---|---|---|---|---|
August | T1 | 2256.319 | 3170.609 | 1333.134 | 450.226 |
T2 | 2172.798 | 2495.764 | 1066.013 | 383.013 | |
T3 | 2074.944 | 2447.826 | 1282.724 | 324.696 | |
T4 | 2150.558 | 2752.013 | 923.186 | 500.141 | |
T5 | 2037.878 | 2729.032 | 1436.424 | 373.870 | |
T6 | 1808.317 | 2453.262 | 1095.171 | 357.561 | |
T7 | 2215.053 | 2686.530 | 1328.933 | 359.785 | |
September | T1 | 1282.477 | 1528.347 | 1012.144 | 172.232 |
T2 | 1077.874 | 1441.119 | 295.044 | 304.434 | |
T3 | 951.109 | 1262.709 | 510.520 | 230.796 | |
T4 | 1171.774 | 1475.466 | 386.226 | 284.171 | |
T5 | 868.823 | 1182.646 | 678.304 | 170.256 | |
T6 | 701.285 | 1049.704 | 256.495 | 228.078 | |
T7 | 1241.705 | 1439.142 | 562.412 | 228.325 |
Feature | Ranking | Feature | Ranking |
---|---|---|---|
Datt1 | 1 | SPVI | 41 |
MCARI1 | 2 | mSRI[720,750] | 42 |
MTCI2 | 3 | VOG1 | 43 |
MCARI/OSAVI1 | 4 | Carte2 | 44 |
MTCI1 | 5 | TCARI1 | 45 |
REP2 | 6 | MCARI2 | 46 |
PRI[531,570] | 7 | Carte1 | 47 |
SR[675,700] | 8 | NVI1 | 48 |
NDVI[521,689] | 9 | NDVI[471,584] | 49 |
NDVI[717,732] | 10 | NDVI[667,740] | 50 |
REP1 | 11 | Datt2 | 51 |
TCARI/OSAVI1 | 12 | mSR | 52 |
NVI2 | 13 | RDVI | 53 |
TCARI2 | 14 | SRI[560,810] | 54 |
TCARI/OSAVI2 | 15 | NDVI[710,750] | 55 |
NDVI[720,820] | 16 | SRI[710,750] | 56 |
Carte4 | 17 | Datt3 | 57 |
NDVI[734,750] | 18 | mND705 | 58 |
VOG3 | 19 | mSRI[710,780] | 59 |
PRI[528,567] | 20 | Gitelson1 | 60 |
VOG2 | 21 | OSAVI1 | 61 |
NDRE | 22 | SRI[705,750] | 62 |
SRI[533,565] | 23 | Gitelson2 | 63 |
EVI | 24 | NDVI[717,770] | 64 |
SRI[720,735] | 25 | SRI[670,800] | 65 |
SRI[629,734] | 26 | NDCI | 66 |
DD | 27 | Carte3 | 67 |
MCARI/OSAVI2 | 28 | SRI[660,810] | 68 |
CI | 29 | OSAVI2 | 69 |
SRI[670,700] | 30 | mSRI[550,780] | 70 |
MTVI | 31 | NDVI[705,750] | 71 |
SRI[700,750] | 32 | NDVI[710,780] | 72 |
NDVI[550,760] | 33 | SRI[550,750] | 73 |
MCARI3 | 34 | SRI[706,755] | 74 |
SRI[717,741] | 35 | SRI[550,760] | 75 |
DCNI | 36 | SRI[708,747] | 76 |
TGI | 37 | mSR705 | 77 |
NDVI[670,800] | 38 | SRI[680,800] | 78 |
SRI[720,738] | 39 | GNDVI | 79 |
Viopt | 40 | SRI[690,752] | 80 |
Feature | Model | R2 | RMSE (kg/ha) | MAE (kg/ha) |
---|---|---|---|---|
Selected features | RF | 0.833 | 252.912 | 185.317 |
(0.052) | (36.243) | (27.611) | ||
SVR | 0.842 | 247.593 | 185.869 | |
(0.042) | (36.269) | (27.128) | ||
KNN | 0.850 | 241.430 | 183.557 | |
(0.035) | (31.183) | (25.998) | ||
Ensemble | 0.874 | 220.799 | 164.787 | |
(0.034) | (32.169) | (24.673) | ||
Full features | RF | 0.822 | 261.552 | 191.602 |
(0.054) | (35.718) | (28.109) | ||
SVR | 0.829 | 257.408 | 191.590 | |
(0.042) | (30.260) | (23.569) | ||
KNN | 0.822 | 262.907 | 198.293 | |
(0.044) | (35.417) | (28.847) | ||
Ensemble | 0.854 | 237.906 | 175.575 | |
(0.036) | (32.152) | (25.300) |
Feature | Model | t | p-Value |
---|---|---|---|
Selected features | Ensemble vs. RF | 18.355 | 0.000 |
Ensemble vs. SVR | 16.890 | 0.000 | |
Ensemble vs. KNN | 17.059 | 0.000 | |
Full features | Ensemble vs. RF | 15.935 | 0.000 |
Ensemble vs. SVR | 13.957 | 0.000 | |
Ensemble vs. KNN | 20.255 | 0.000 |
Model | Metrics | T1 | T2 | T3 | T4 | T5 | T6 | T7 |
---|---|---|---|---|---|---|---|---|
RF | R2 | 0.863 | 0.852 | 0.908 | 0.762 | 0.880 | 0.731 | 0.759 |
RMSE (kg/ha) | 221.319 | 249.490 | 190.915 | 310.648 | 226.252 | 327.119 | 281.774 | |
MAE (kg/ha) | 170.618 | 192.834 | 132.685 | 216.692 | 154.630 | 229.184 | 196.870 | |
SVR | R2 | 0.845 | 0.889 | 0.871 | 0.702 | 0.906 | 0.801 | 0.784 |
RMSE (kg/ha) | 235.242 | 215.894 | 225.637 | 347.210 | 200.501 | 281.140 | 266.777 | |
MAE (kg/ha) | 166.508 | 168.490 | 172.771 | 254.350 | 162.607 | 228.031 | 185.659 | |
KNN | R2 | 0.851 | 0.831 | 0.900 | 0.850 | 0.891 | 0.774 | 0.745 |
RMSE (kg/ha) | 230.624 | 266.081 | 198.819 | 246.571 | 216.394 | 299.885 | 289.737 | |
MAE (kg/ha) | 185.806 | 222.928 | 151.893 | 172.299 | 166.075 | 240.781 | 210.967 | |
Ensemble | R2 | 0.873 | 0.869 | 0.918 | 0.839 | 0.914 | 0.837 | 0.778 |
RMSE (kg/ha) | 212.574 | 234.157 | 180.469 | 255.500 | 192.307 | 254.738 | 270.159 | |
MAE (kg/ha) | 159.047 | 185.788 | 126.512 | 191.685 | 142.453 | 191.387 | 189.763 |
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Feng, L.; Zhang, Z.; Ma, Y.; Du, Q.; Williams, P.; Drewry, J.; Luck, B. Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning. Remote Sens. 2020, 12, 2028. https://doi.org/10.3390/rs12122028
Feng L, Zhang Z, Ma Y, Du Q, Williams P, Drewry J, Luck B. Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning. Remote Sensing. 2020; 12(12):2028. https://doi.org/10.3390/rs12122028
Chicago/Turabian StyleFeng, Luwei, Zhou Zhang, Yuchi Ma, Qingyun Du, Parker Williams, Jessica Drewry, and Brian Luck. 2020. "Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning" Remote Sensing 12, no. 12: 2028. https://doi.org/10.3390/rs12122028