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Utilization of Multispectral-UAV system for Rice Crop Management

BASCON, Maria Victoria Rabaca 名古屋大学

2022.12.19

概要

Remote sensing using unmanned aerial vehicles (UAVs) and satellites is a rapidly growing technology in rice crop management today. While satellites are expected to cover a wide area at the national level and become an important platform for future agriculture, they have a low temporal and spatial resolution, making it difficult to develop technology for farmland. Therefore, there is a need to develop technology that can be applied to wide-area monitoring using UAVs, which can acquire crop data with higher temporal and spatial resolution, using satellites.

Crop parameters such as above-ground biomass (AGB) and leaf area index (LAI) of paddy rice are indicators of productivity and can be used to evaluate rice crop management for optimal grain yield and agricultural decision making. Previous studies have shown that this LAI and AGB of rice can be estimated using the vegetation index (VI) derived from spectral reflectance. Assuming that the development of a good LAI and AGB estimation model would be useful in predicting grain yield of paddy rice, this study improved the LAI and AGB estimation method using UAV-derived multispectral images and developed a grain yield estimation method using them.

In general, the accuracy of crop prediction models depends on the type of feature variables, model algorithms, and timing of data measurement, and these factors need to be considered in developing accurate crop prediction models. Therefore, the following four items were considered in this study.

(1) Evaluation of the impact of the use of texture variables on the accuracy of LAI-VI estimation models

(2) Evaluation of the impact of feature selection methods and the use of vegetation fraction (VF) on the rice AGB estimation model

(3) Development of a grain yield prediction model using VI variables

(4) Evaluation of optimal timing of data acquisition for grain yield prediction. Experimental trials were conducted for two fertilizer trials and five rice cultivars during the 2020 and 2021 rice seasons. Multispectral images and the data of LAI, AGB, and grain yield were measured at four growth stage for each cultivar. UAV-derived variables such as VI and texture features calculated from the gray-level co-occurrence matrix (GLCM) were used as explanatory variables in the models.

Feature selection methods such as Recursive Feature Elimination (RFE), M-statistics, and z-tests were used in AGB estimation model while Exhaustive Feature Selection (EFS), variance threshold, and Variance Inflation Factor (VIF) were used in LAI estimation model to reduce the dimensions of the models by selecting parameters. For regression models, we evaluated the machine learning methods Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boost (XGBoost) for AGB estimation model while Multiple Linear Regression (MLR), SVR, RF, and Ridge Regression for LAI estimation model.

The results showed that RF gave stable results for LAI estimation of rice (R2 = 0.60–0.62, RMSE = 0.68–0.73, m2 / m2), suggesting that feature selection had little effect on the performance of this model. Furthermore, combining specific reflectance data (RVI, GRVI) with texture data (DTI(NIR,R), RTI(NIR,G)) improved the accuracy of LAI estimation for all five cultivars tested (R2 = 0.68–0.82). This clearly shows that the use of texture and appropriate reflectance data can improve the accuracy of LAI estimation for rice.

We also considered the use of VF, a known alternative indicator of LAI, in the AGB estimation model. However, it is difficult to develop a model that stably distinguishes between plant and non-plant areas under outdoor conditions in this study, and no significant improvement in AGB estimation accuracy was observed even when VF was used as an additional explanatory variable.

We also developed a model to predict grain yield using time -specific VI measured at tillering, stem elongation, booting, and heading stages. Correlations between VI and grain yield showed that it was low among growth stages (r = 0.07–0.39). To improve the accuracy of grain yield prediction using VI as an explanatory variable, a multivariate regression model was selected. The results showed that RF performed the best among the regression models used in this study, including SVR, MLR, and Ridge regression, and the grain yield prediction using five VIs, including the normalized VI with Red Edge as an explanatory variable, was moderately weak (R2 = 0.35 and RMSE = 0.78 ton/ha). The normalized VI with Red Edge contributes to grain yield prediction by providing optimal variability to the model. On the other hand, grain yield prediction was not substantially improved (R2 = 0.39, RMSE = 0.75 ton/ha) when VI for the booting and heading stages were added as explanatory variables. When prediction models were created for each cultivar, better performance was observed for the cultivars Hatsushimo (R2 = 0.50, RMSE = 0.84 ton/ha) and Nikomaru (R2 = 0.50, RMSE = 0.53 ton/ha).

AGB and LAI estimations using multi-temporal VI were redeveloped in the XGBoost model and simulated throughout the growing season using Gompertz curves to determine the optimal timing of data acquisition for grain yield prediction for each cultivar. A single-day linear regression model was also constructed to examine prediction performance using simulated AGB and LAI values. The results showed that AGB and LAI could be estimated from VI (R 2 = 0.56–0.83, 0.57–0.73), and that the optimal timing of UAV flight varied from 4 to 31 days between the tillering and early heading periods for each cultivar. These findings are expected to help researchers save resources and time for numerous UAV flights to predict rice grain yield.

These results suggest that LAI and AGB estimates for two fertilizer trials and five varieties in a two-year experiment can be reasonably estimated using UAV-derived variables and machine learning models. Furthermore, direct prediction of grain yield using the cumulative VI provides comparable or better predictions than that of the estimated AGB and LAI using VI for some cultivars. Even variables with low correlation to crop parameters can be employed as explanatory variables indicating that a variety of inputs are essential for improved prediction.

In summary, our results showed that rice grain yield could be predicted before the heading stage using either VI or VI -estimated AGB and LAI, although it is cultivar-dependent. The analysis of the suitable timing of observations, which has not been evaluated in previous reports, allowed us to identify when yield estimation by satellite remote sensing can be used, and we believe this will contribute to the future widespread use of agricultural remote sensing.

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Chapter3

1. Buresh, R.J.; Castillo, R.L.; Dela Torre, J.C.; Laureles, E.V.; Samson, M.I.; Sinohin, P.J.; Guerra, M. Site-Specific Nutrient Management for Rice in the Philippines: Calculation of Field-Specific Fertilizer Requirements by Rice Crop Manager. Field Crops Research. 2019, 239, 56–70, doi:10.1016/j.fcr.2019.05.013.

2. Deusing, C.A.; Rojas, J.P.; Petro, E.; Martinez, C.; Mondragon, I.F.; Patino, D.; Rebolledo, M.C.; Colorado, J. High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery. Journal of Inteligent & Robotic System. 2019, 96, 573–589, doi:10.1007/s10846-019- 01001-5.

3. Feng, H.; Jiang, N.; Huang, C.; Fang, W.; Yang, W.; Chen, G.; Xiong, L.; Liu, Q. A Hyperspectral Imaging System for an Accurate Prediction of the Above-Ground Biomass of Individual Rice Plants. Review of Science Instruments. 2013, 84(9), 095107, doi:10.1063/1.4818918.

4. Gnyp, M.L.; Miao, Y.; Yuan, F.; Ustin, S.L.; Yu, K.; Yao, Y.; Huang, S.; Bareth, G. Hyperspectral Canopy Sensing of Paddy Rice Aboveground Biomass at Different Growth Stages. Field Crops Research. 2014, 155, 42–55, doi:10.1016/j.fcr.2013.09.023.

5. Adeluyi, O.; Harris, A.; Foster, T.; Clay, G.D. Exploiting Centimetre Resolution of Drone-Mounted Sensors for Estimating Mid-Late Season above Ground Biomass in Rice. European Journal of Agronomy. 2022, 132, 126411, doi:10.1016/j.eja.2021.126411.

6. G. Poley, L.; J. McDermid, G. A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems. Remote Sensing. 2020, 12(7), 1052, doi:10.3390/rs12071052.

7. Zhang, D.; Mansaray, L.R.; Jin, H.; Sun, H.; Kuang, Z.; Huang, J. A Universal Estimation Model of Fractional Vegetation Cover for Different Crops Based on Time Series Digital Photographs. Computers and Electronics in Agriculture. 2018, 151, 93–103, doi:10.1016/j.compag.2018.05.030.

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9. Zhang, G.; Ganguly, S.; Nemani, R.R.; White, M.A.; Milesi, C.; Hashimoto, H.; Wang, W.; Saatchi, S.; Yu, Y.; Myneni, R.B. Estimation of Forest Aboveground Biomass in California Using Canopy Height and Leaf Area Index Estimated from Satellite Data. Remote Sensing of Environment. 2014, 151, 44–56, doi:10.1016/j.rse.2014.01.025.

10. Peprah, C.O.; Yamashita, M.; Yamaguchi, T.; Sekino, R.; Takano, K.; Katsura, K. SpatioTemporal Estimation of Biomass Growth in Rice Using Canopy Surface Model from Unmanned Aerial Vehicle Images. Remote Sensing. 2021, 13(12), 2388, doi:10.3390/rs13122388.

11. Li, W.; Niu, Z.; Chen, H.; Li, D.; Wu, M.; Zhao, W. Remote Estimation of Canopy Height and Aboveground Biomass of Maize Using High-Resolution Stereo Images from a Low-Cost Unmanned Aerial Vehicle System. Ecological Indicators. 2016, 67, 637–648, doi:10.1016/j.ecolind.2016.03.036.

12. Zheng, H.; Cheng, T.; Zhou, M.; Li, D.; Yao, X.; Tian, Y.; Cao, W.; Zhu, Y. Improved Estimation of Rice Aboveground Biomass Combining Textural and Spectral Analysis of UAV Imagery. Precision Agriculture. 2019, 20, 611–629, doi:10.1007/s11119-018-9600-7.

13. Jiang, Q.; Fang, S.; Peng, Y.; Gong, Y.; Zhu, R.; Wu, X.; Ma, Y.; Duan, B.; Liu, J. UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features. Remote Sensing. 2019, 11(7), 890, doi:10.3390/rs11070890.

14. Liu, C.; Liu, Y.; Lu, Y.; Liao, Y.; Nie, J.; Yuan, X.; Chen, F. Use of a Leaf Chlorophyll Content Index to Improve the Prediction of Above-Ground Biomass and Productivity. PeerJ. 2019, 6, e6240, doi:10.7717/peerj.6240.

15. Colorado, J.D.; Calderon, F.; Mendez, D.; Petro, E.; Rojas, J.P.; Correa, E.S.; Mondragon, I.F.; Rebolledo, M.C.; Jaramillo-Botero, A. A Novel NIR-Image Segmentation Method for the Precise Estimation of above-Ground Biomass in Rice Crops. PloS one 2020, 15(10), e0239591, doi:10.1371/journal.pone.0239591.

16. Xu, T.; Wang, F.; Xie, L.; Yao, X.; Zheng, J.; Li, J.; Chen, S. Integrating the Textural and Spectral Information of UAV Hyperspectral Images for the Improved Estimation of Rice Aboveground Biomass. Remote Sens. 2022, 14(11), 2534, doi:10.3390/rs14112534.

17. Vaesen, K.; Gilliams, S.; Nackaerts, K.; Coppin, P. Ground-Measured Spectral Signatures as Indicators of Ground Cover and Leaf Area Index: The Case of Paddy Rice. Field Crops Research. 2001, 69(1), 13–25, doi:10.1016/S0378-4290(00)00129-5.

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20. Kuhn, M. Building Predictive Models in R Using the Caret Package. Journal of Statistical Sofware. 2008, 28, 1–26, doi:10.18637/jss.v028.i05

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22. Chen, T.; He, H.T.; Benesty, M.; Khotilovich, V.; Tang, Y.; Cho, H.; Chen, K.; Mitchell, R.; Cano, I.; Zhou, T.; Li, M.; Xie, J.; Lin, M.; Geng, Y.; Li, Y.; Yuan, J. xgboost: Extreme Gradient Boosting. R package version 1.6.0.1. 2022, Accessed on 25 June 2022. https://CRAN.Rproject.org/package=xgboost.

23. Binte Mostafiz, R., Noguchi, R., & Ahamed, T. Agricultural land suitability assessment using satellite remote sensing-derived soil-vegetation indices. Land. 2021. 10(2), 223, https://doi.org/10.3390/land10020223.

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26. Xiao, X.; Boles, S.; Liu, J.; Zhuang, D.; Frolking, S.; Li, C.; Salas, W.; Moore, B. Mapping Paddy Rice Agriculture in Southern China Using Multi-Temporal MODIS Images. Remote Sens. Environ. 2005.95(4), 480-492. doi:10.1016/j.rse.2004.12.009.

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28. Qiu, C.; Liao, G.; Tang, H.; Liu, F.; Liao, X.; Zhang, R.; Zhao, Z. Derivative Parameters of Hyperspectral NDVI and Its Application in the Inversion of Rapeseed Leaf Area Index. Appl. Sci. 2018, 8, 1300.

29. Kang, Y.; Nam, J.; Kim, Y.; Lee, S.; Seong, D.; Jang, S.; Ryu, C. Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery. Remote Sens. 2021, 13, 1508, doi:10.3390/rs13081508.

30. Teal, R.K.; Tubana, B.; Girma, K.; Freeman, K.W.; Arnall, D.B.; Walsh, O.; Raun, W.R. In-Season Prediction of Corn Grain Yield Potential Using Normalized Difference Vegetation Index. Agron. J. 2006, 98, 1488–1494, doi:10.2134/agronj2006.0103.

31. Tian, Y.C.; Yao, X.; Yang, J.; Cao, W.X.; Hannaway, D.B.; Zhu, Y. Assessing Newly Developed and Published Vegetation Indices for Estimating Rice Leaf Nitrogen Concentration with Groundand Space-Based Hyperspectral Reflectance. Field Crops Res. 2011.120(2), 299-310.

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Chapter4

1. Ge, H.; Ma, F.; Li, Z.; Du, C. Grain Yield Estimation in Rice Breeding Using Phenological Data and Vegetation Indices Derived from UAV Images. Agronomy 2021, 11(12), 2439, doi:10.3390/agronomy11122439.

2. Yang, Q.; Shi, L.; Han, J.; Zha, Y.; Zhu, P. Deep Convolutional Neural Networks for Rice Grain Yield Estimation at the Ripening Stage Using UAV-Based Remotely Sensed Images. Field Crops Res. 2019, 235, 142–153, doi:10.1016/j.fcr.2019.02.022.

3. Jeong, S.; Ko, J.; Yeom, J.-M. Predicting Rice Yield at Pixel Scale through Synthetic Use of Crop and Deep Learning Models with Satellite Data in South and North Korea. Science of the Total Environment. 2022, 802, 149726, doi:10.1016/j.scitotenv.2021.149726.

4. Harrell, D.L.; Tubaña, B.S.; Walker, T.W.; Phillips, S.B. Estimating Rice Grain Yield Potential Using Normalized Difference Vegetation Index. Agron. J. 2011, 103(6), 1717–1723, doi:10.2134/agronj2011.0202.

5. Rehman, T.H.; Lundy, M.E.; Linquist, B.A. Comparative Sensitivity of Vegetation Indices Measured via Proximal and Aerial Sensors for Assessing N Status and Predicting Grain Yield in Rice Cropping Systems. Remote Sens. 2022, 14(12), 2770, doi:10.3390/rs14122770.

6. Perros, N.; Kalivas, D.; Giovos, R. Spatial Analysis of Agronomic Data and UAV Imagery for Rice Yield Estimation. Agriculture 2021, 11(9), 809, doi:10.3390/agriculture11090809.

7. Wan, L.; Cen, H.; Zhu, J.; Zhang, J.; Zhu, Y.; Sun, D.; Du, X.; Zhai, L.; Weng, H.; Li, Y.; et al. Grain Yield Prediction of Rice Using Multi-Temporal UAV-Based RGB and Multispectral Images and Model Transfer – a Case Study of Small Farmlands in the South of China. Agricultural and Forest Meteorology. 2020, 291, 108096, doi:10.1016/j.agrformet.2020.108096.

8. Zhou, X.; Zheng, H.B.; Xu, X.Q.; He, J.Y.; Ge, X.K.; Yao, X.; Cheng, T.; Zhu, Y.; Cao, W.X.; Tian, Y.C. Predicting Grain Yield in Rice Using Multi-Temporal Vegetation Indices from UAV-Based Multispectral and Digital Imagery. ISPRS Journal of Photogrammetry and Remote Sensing. 2017, 130, 246–255, doi:10.1016/j.isprsjprs.2017.05.003.

9. Wang, F.; Wang, F.; Zhang, Y.; Hu, J.; Huang, J.; Xie, J. Rice yield estimation using parcel-level relative spectral variables from UAV-based hyperspectral imagery. Frontiers in plant science. 2019. 10, 453, https://doi.org/10.3389/fpls.2019.00453.

10. Wang, F.; Yao, X.; Xie, L.; Zheng, J.; Xu, T. Rice Yield Estimation Based on Vegetation Index and Florescence Spectral Information from UAV Hyperspectral Remote Sensing. Remote Sensing. 2021, 13(17), 3390, doi:10.3390/rs13173390.

11. Wang, F.; Yi, Q.; Hu, J.; Xie, L.; Yao, X.; Xu, T.; Zheng, J. Combining Spectral and Textural Information in UAV Hyperspectral Images to Estimate Rice Grain Yield. Internation Journal of Applied Earth Observation and Geoinformation. 2021, 102, 102397, doi:10.1016/j.jag.2021.102397.

12. Yuan, N.; Gong, Y.; Fang, S.; Liu, Y.; Duan, B.; Yang, K.; Wu, X.; Zhu, R. UAV Remote Sensing Estimation of Rice Yield Based on Adaptive Spectral Endmembers and Bilinear Mixing Model. Remote Sensing. 2021, 13(11), 2190, doi:10.3390/rs13112190.

13. Duan, B., Fang, S., Zhu, R., Wu, X., Wang, S., Gong, Y., & Peng, Y. Remote estimation of rice yield with unmanned aerial vehicle (UAV) data and spectral mixture analysis. Frontiers in Plant Science, 2019. 10, 204, https://doi.org/10.3389/fpls.2019.00204.

14. Reza, M.N.; Na, I.S.; Baek, S.W.; Lee, K.-H. Rice Yield Estimation Based on K-Means Clustering with Graph-Cut Segmentation Using Low-Altitude UAV Images. Biosyst. Eng. 2019, 177, 109– 121, doi:10.1016/j.biosystemseng.2018.09.014.

15. Goswami, S.; Choudhary, S.S.; Chatterjee, C.; Mailapalli, D.R.; Mishra, A.; Raghuwanshi, N.S. Estimation of Nitrogen Status and Yield of Rice Crop Using Unmanned Aerial Vehicle Equipped with Multispectral Camera. Journal of Applied Remote Sensing. 2021, 15(4), 042407, doi:10.1117/1.JRS.15.042407.

16. Hijmas, R.J. raster: Geographic Data Analysis and Modeling. R package version. 3.5-15. 2022. https://CRAN.R-project.org/package=raster17.

17. Wei, T.; Simko, V.; Levy, M.; Xie, Y.; Jin, Y.; Zemla, J. Package ‘Corrplot’. Statistician 2017, 56(316), e24, https://peerj.com/articles/9945/Supplemental_Data_S10.pdf.

18. Kuhn, M. Building Predictive Models in R Using the Caret Package. Journal of Statistical Software. 2008, 28, 1–26, doi:10.18637/jss.v028.i05.

19. Kanke, Y.; Tubana, B.; Dalen, M.; Harrell, D. Evaluation of Red and Red-Edge Reflectance-Based Vegetation Indices for Rice Biomass and Grain Yield Prediction Models in Paddy Fields. Precision Agriculture. 2016. 17, doi:10.1007/s11119-016-9433-1.

20. Barzin, R.; Pathak, R.; Lotfi, H.; Varco, J.; Bora, G.C. Use of UAS Multispectral Imagery at Different Physiological Stages for Yield Prediction and Input Resource Optimization in Corn. Remote Sensing. 2020, 12(15), 2392, doi:10.3390/rs12152392.

21. Vincini, M.; Frazzi, E.; D’Alessio, P. A Broad-Band Leaf Chlorophyll Vegetation Index at the Canopy Scale. Precision Agriculture. 2008, 9, 303–319, doi:10.1007/s11119-008-9075-z.

22. Zhang, K.; Ge, X.; Shen, P.; Li, W.; Liu, X.; Cao, Q.; Zhu, Y.; Cao, W.; Tian, Y. Predicting Rice Grain Yield Based on Dynamic Changes in Vegetation Indexes during Early to Mid-Growth Stages. Remote Sensing. 2019, 11(4), 387, doi:10.3390/rs11040387.

23. Muharam, F.M.; Nurulhuda, K.; Zulkafli, Z.; Tarmizi, M.A.; Abdullah, A.N.H.; Che Hashim, M.F.; Mohd Zad, S.N.; Radhwane, D.; Ismail, M.R. UAV- and Random-Forest-AdaBoost (RFA)-Based Estimation of Rice Plant Traits. Agronomy. 2021, 11(5), 915, doi:10.3390/agronomy11050915.

24. Kang, Y.; Nam, J.; Kim, Y.; Lee, S.; Seong, D.; Jang, S.; Ryu, C. Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery. Remote Sensing. 2021, 13(8), 1508, doi:10.3390/rs13081508.

Chapter6

1. Hu, P.; Chapman, S.C.; Wang, X.; Potgieter, A.; Duan, T.; Jordan, D.; Guo, Y.; Zheng, B. Estimation of Plant Height Using a High Throughput Phenotyping Platform Based on Unmanned Aerial Vehicle and Self-Calibration: Example for Sorghum Breeding. Eur. J. Agron. 2018, 95, 24– 32, doi:10.1016/j.eja.2018.02.004.

2. Tao, H.; Feng, H.; Xu, L.; Miao, M.,; Yang, G.; Yang, X.; Fan, L. Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images. Sensors 2020, 20, 1231.

3. Madec, S.; Baret, F.; De Solan, B.; Thomas, S.; Dutartre, D.; Jezequel, S.; Comar, A HighThroughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates. Front. Plant Sci. 2002, 8,1-14, doi:10.3389/fpls.2017.02002.

4. Gong, Y.; Yang, K.; Lin, Z.; Fang, S.; Wu, X.; Zhu, R.; Peng, Y. Remote Estimation of Leaf Area Index (LAI) with Unmanned Aerial Vehicle (UAV) Imaging for Different Rice Cultivars throughout the Entire Growing Season. Plant Methods 2021, 17, 88, doi:10.1186/s13007-021-00789-4.

5. Hasan, U.; Sawut, M.; Chen, S. Estimating the Leaf Area Index of Winter Wheat Based on Unmanned Aerial Vehicle RGB-Image Parameters. Sustainability 2019, 11, 6829, doi:10.3390/su11236829.

6. Roosjen, P. P.; Brede, B.; Suomalainen, J. M.; Bartholomeus, H. M.; Kooistra, L.; Clevers, J. G. Improved Estimation of Leaf Area Index and Leaf Chlorophyll Content of a Potato Crop Using Multi-Angle Spectral Data – Potential of Unmanned Aerial Vehicle Imagery. International Journal.of Applied Earth Observation and Geoinformation 2018, 66, 14–26.

7. Yue, J.; Yang, G.; Tian, Q.; Feng, H.; Xu, K.; Zhou, C. Estimate of Winter-Wheat above-Ground Biomass Based on UAV Ultrahigh-Ground-Resolution Image Textures and Vegetation Indices. ISPRS Journa of Photogrammetry in Remote Sensing. 2019, 150, 226–244, doi:10.1016/j.isprsjprs.2019.02.022.

8. Niu, Y.; Zhang, L.; Zhang, H.; Han, W.; Peng, X. Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery. Remote Sensing. 2019, 11, 1261.

9. Lu, N.; Zhou, J.; Han, Z.; Li, D.; Cao, Q.; Yao, X.; Tian, Y.; Zhu, Y.; Cao, W.; Cheng, T. Improved Estimation of Aboveground Biomass in Wheat from RGB Imagery and Point Cloud Data Acquired with a Low-Cost Unmanned Aerial Vehicle System. Plant Methods 2019, 15, 17, doi:10.1186/s13007-019-0402-3.

10. Yang, H.; Li, F.; Wang, W.; Yu, K. Estimating Above-Ground Biomass of Potato Using Random Forest and Optimized Hyperspectral Indices. Remote Sensing. 2021, 13, 2339, doi:10.3390/rs13122339.

11. Zhang, J.; Cheng, T; Guo, W.; Xu, X.; Qiao, H.; Xie, Y.; Ma, X. Leaf Area Index Estimation Model for UAV Image Hyperspectral Data Based on Wavelength Variable Selection and Machine Learning Methods. Plant Methods. 2021.17, 1–14.

12. Osco, L.P.; Junior, J.M.; Ramos, A.P.M.; Furuya, D.E.G.; Santana, D.C.; Teodoro, L.P.R.; Gonçalves, W.N.; Baio, F.H.R.; Pistori, H.; Junior, C.A. da S.; et al. Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques. Remote Sensing. 2020, 12, 3237, doi:10.3390/rs12193237.

13. Prasad, N.R.; Patel, N.R.; Danodia, A.; Manjunath, K.R. Comparative Performance of SemiEmpirical Based Remote Sensing and Crop Simulation Model for Cotton Yield Prediction. Model. Earth Syst. Environ. 2022, 8, 1733–1747, doi:10.1007/s40808-021-01180-x.

14. Rehmani, M. I. A.; Ding, C.; Li, G.; Ata-Ul-Karim, S. T.; Hadifa, A.; Bashir, M. A.; Ding, Y. Vulnerability of Rice Production to Temperature Extremes during Rice Reproductive Stage in Yangtze River Valley, China. J. King Saud Univ.-Sci. 2021, 33, 101599, doi:10.1016/j.jksus.2021.101599.

15. Rehmani, M. I. A.; Wei, G.; Hussain, N.; Ding, C.; Li, G.; Liu, Z.,; Ding, Y. Yield and Quality Responses of Two Indica Rice Hybrids to Post-Anthesis Asymmetric Day and Night Open-Field Warming in Lower Reaches of Yangtze River Delta. Field Crops Res. 2014, 156, 231–241.

16. Onwuchekwa-Henry, C.B.; Ogtrop, F.V.; Roche, R.; Tan, D.K.Y. Model for Predicting Rice Yield from Reflectance Index and Weather Variables in Lowland Rice Fields. Agriculture 2022, 12, 130, doi:10.3390/agriculture12020130.

17. Ge, H.; Ma, F.; Li, Z.; Du, C. Grain Yield Estimation in Rice Breeding Using Phenological Data and Vegetation Indices Derived from UAV Images. Agronomy 2021, 11, 2439, doi:10.3390/agronomy11122439.

18. Freeman, K.W.; Girma, K.; Arnall, D.B.; Mullen, R.W.; Martin, K.L.; Teal, R.K.; Raun, W.R. ByPlant Prediction of Corn Forage Biomass and Nitrogen Uptake at Various Growth Stages Using Remote Sensing and Plant Height. Agron. J. 2007, 99, 530–536, doi:10.2134/agronj2006.0135.

19. Rahman, M.M.; Crain, J.; Haghighattalab, A.; Singh, R.P.; Poland, J. Improving Wheat Yield Prediction Using Secondary Traits and High-Density Phenotyping Under Heat-Stressed Environments. Front. Plant Sci. 2021, 12, 1977, doi:10.3389/fpls.2021.633651.

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Chapter7

1. Zhang, Z.-H.; Li, P.; Wang, L.-X.; Hu, Z.-L.; Zhu, L.-H.; Zhu, Y.-G. Genetic Dissection of the Relationships of Biomass Production and Partitioning with Yield and Yield Related Traits in Rice. Plant Science. 2004, 167(1), 1–8, doi:10.1016/j.plantsci.2004.01.007.

2. G. Poley, L.; J. McDermid, G. A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems. Remote Sensing. 2020, 12(7), 1052, doi:10.3390/rs12071052.

3. Zhang, D.; Mansaray, L.R.; Jin, H.; Sun, H.; Kuang, Z.; Huang, J. A Universal Estimation Model of Fractional Vegetation Cover for Different Crops Based on Time Series Digital Photographs. Computers and Electronics in Agriculture. 2018, 151, 93–103, doi:10.1016/j.compag.2018.05.030.

4. Yang, K.; Gong, Y.; Fang, S.; Duan, B.; Yuan, N.; Peng, Y.; Wu, X.; Zhu, R. Combining Spectral and Texture Features of UAV Images for the Remote Estimation of Rice LAI throughout the Entire Growing Season. Remote Sensing. 2021, 13(15), 3001, doi:10.3390/rs13153001.

5. Wang, L.; Chang, Q.; Yang, J.; Zhang, X.; Li, F. Estimation of Paddy Rice Leaf Area Index Using Machine Learning Methods Based on Hyperspectral Data from Multi-Year Experiments. PlosS one. 2018, 13(12), e0207624, doi:10.1371/journal.pone.0207624.

6. Chen, Z.; Jia, K.; Xiao, C.; Wei, D.; Zhao, X.; Lan, J.; Wei, X.; Yao, Y.; Wang, B.; Sun, Y.; et al. Leaf Area Index Estimation Algorithm for GF-5 Hyperspectral Data Based on Different Feature Selection and Machine Learning Methods. Remote Sensing. 2020, 12(13), 2110, doi:10.3390/rs12132110.

7. Yang, W.; Peng, S.; Laza, R.C.; Visperas, R.M.; Dionisio-Sese, M.L. Grain Yield and Yield Attributes of New Plant Type and Hybrid Rice. Crop Science. 2007, 47(4), 1393–1400, doi:10.2135/cropsci2006.07.0457.

8. Mohamad, O.; Suhaimi, O.; Abdullah, M. Z. The relationships between harvest index, grain yield and biomass in rice. MARDI Research Journal. 1994, 22(1), 29-3, https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1045.1417&rep=rep1&type=pdf

9. Aboelghar, M.; Arafat, S.; Abo Yousef, M.; El-Shirbeny, M.; Naeem, S.; Massoud, A.; Saleh, N. Using SPOT Data and Leaf Area Index for Rice Yield Estimation in Egyptian Nile Delta. Egyptian Journal of Remote Sensing and Space Science. 2011, 14(2), 81–89, doi:10.1016/j.ejrs.2011.09.002.

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