Multiple Linear Regression models for estimating the plant height of red pepper using UAV-based multispectral imagery

HoJun Kwon; Ye-Seong Kang; ChanSeok Ryu; ChangHyeok Park; GangIn Je

doi:10.22765/pastj.20250026

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2025 Vol.7, Issue 4 Preview Page Next Page

Research Article

Multiple Linear Regression models for estimating the plant height of red pepper using UAV-based multispectral imagery

31 December 2025. pp. 388-396

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Abstract

Red pepper (Capsicum annuum L.) is a major field crop in Korea; however, its productivity and cultivation area have recently declined due to labor shortages caused by an aging population and climate variability. This study was conducted on the ‘Colormura’ cultivar, transplanted on April 29 in a farm field located in Bangyo-ri, Gimje-si. The objective was to develop a Multiple Linear Regression (MLR) model to estimate plant height—a key growth indicator highly correlated with yield—using vegetation indices derived from Unmanned Aerial Vehicle (UAV)-based multispectral imagery. Plant height data and UAV multispectral images were collected at three time points: mid-June, mid-July, and mid-August. To evaluate the model's generalization performance, the data were partitioned into calibration and validation datasets at ratios of 8:2, 7:3, and 6:4, and performance was assessed using R², RMSE, and MAPE. Because individual monthly analyses resulted in low generalization performance or overfitting, the model was constructed using time-series data spanning from June to August. The model utilizing PRI, TCARI, NDRE, and OSAVI exhibited the best performance. Notably, the RedEdge-based indices, NDRE and TCARI, made significant contributions to the prediction of plant height. This study demonstrates the potential of estimating red pepper growth using UAV multispectral data. Future research should focus on enhancing model performance by expanding the dataset and applying diverse analytical techniques.

Keywords

Red pepper

Multiple linear regression

Vegetation indices

UAV

Multispectral

References

Basso, B., Cammarano, D., De Vita, P. 2004. Remotely sensed vegetation indices: Theory and applications for crop management. B Rivista Italiana Agrometeorologia 36-53.

Boiarskii, B., Hasegawa, H. 2019. Comparison of ndvi and ndre indices to detect differences in vegetation and chlorophyll content. Journal of Mechanics of Continua And Mathematical Sciences 4: 20-29. https://doi.org/10.26782/jmcms.spl.4/2019.11.00003

10.26782/jmcms.spl.4/2019.11.00003

Delavarpour, N., Koparan, C., Nowatzki, J., Bajwa, S., Sun, X. 2021. A technical study on uav characteristics for precision agriculture applications and associated practical challenges. Remote sensing 13: 1204. https://doi.org/10.3390/rs13061204

10.3390/rs13061204

Gupta, C., Tewari, V., Machavaram, R., Shrivastava, P. 2022. An image processing approach for measurement of chili plant height and width under field conditions. Journal of the Saudi Society of Agricultural Sciences 21: 171-179. https://doi.org/10.1016/j.jssas.2021.07.007

10.1016/j.jssas.2021.07.007

Gyeongsangbuk-do Agricultural Research & Extension Service. 2024. Current Status of Pepper Cultivation and Management Characteristics. [in Korean]

Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J., Dextraze, L. 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote sensing of environment 81: 416-426. https://doi.org/10.1016/S0034-4257(02)00018-4

10.1016/S0034-4257(02)00018-4

Harwin, S., Lucieer, A. 2012. Assessing the accuracy of georeferenced point clouds produced via multi-view stereopsis from unmanned aerial vehicle (uav) imagery. Remote sensing 4: 1573-1599. https://doi.org/10.3390/rs4061573

10.3390/rs4061573

Jackson, R.D., Huete, A.R. 1991. Interpreting vegetation indices. Preventive veterinary medicine 11: 185-200. https://doi.org/10.1016/S0167-5877(05)80004-2

10.1016/S0167-5877(05)80004-2

Karunasingha, D.S.K. 2022. Root mean square error or mean absolute error? Use their ratio as well. Information Sciences 585: 609-629. https://doi.org/10.1016/j.ins.2021.11.036

10.1016/j.ins.2021.11.036

Liakos, K.G., Busato, P., Moshou, D., Pearson, S., Bochtis, D. 2018. Machine learning in agriculture: A review. Sensors 18: 2674. https://doi.org/10.3390/s18082674

10.3390/s1808267430110960PMC6111295

Loncan, L., De Almeida, L.B., Bioucas-Dias, J.M., Briottet, X., Chanussot, J., Dobigeon, N., Fabre, S., Liao, W., Licciardi, G.A., Simoes, M. 2015. Hyperspectral pansharpening: A review. IEEE Geoscience and remote sensing magazine 3: 27-46. https://doi.org/10.1109/MGRS.2015.2440094

10.1109/MGRS.2015.2440094

MDS (Ministry of Data and Statistics). 2024. Survey on 2024 Cultivated Area for Rice and Chili Peppers. [in Korean]

Montesinos López, O.A., Montesinos López, A., Crossa, J. 2022. Overfitting, model tuning, and evaluation of prediction performance. pp. 109-139. Multivariate statistical machine learning methods for genomic prediction. Springer. https://doi.org/10.1007/978-3-030-89010-0_4

10.1007/978-3-030-89010-0_4

O’brien, R.M. 2007. A caution regarding rules of thumb for variance inflation factors. Quality & quantity 41: 673-690. https://doi.org/10.1007/s11135-006-9018-6

10.1007/s11135-006-9018-6

Radočaj, D., Šiljeg, A., Marinović, R., Jurišić, M. 2023. State of major vegetation indices in precision agriculture studies indexed in web of science: A review. Agriculture 13: 707. https://doi.org/10.3390/agriculture13030707

10.3390/agriculture13030707

Sharma, P., Dadheech, P., Aneja, N., Aneja, S. 2023. Predicting agriculture yields based on machine learning using regression and deep learning. IEEE Access 11: 111255-111264. https://doi.org/10.1109/ACCESS.2023.3321861

10.1109/ACCESS.2023.3321861

Sishodia, R.P., Ray, R.L., Singh, S.K. 2020. Applications of remote sensing in precision agriculture: A review. Remote sensing 12: 3136. https://doi.org/10.3390/rs12193136

10.3390/rs12193136

Tsouros, D.C., Bibi, S., Sarigiannidis, P.G. 2019. A review on uav-based applications for precision agriculture. Information 10: 349. https://doi.org/10.3390/info10110349

10.3390/info10110349

Uyanık, G.K., Güler, N. 2013. A study on multiple linear regression analysis. Procedia-Social and Behavioral Sciences 106: 234-240. https://doi.org/10.1016/j.sbspro.2013.12.027

10.1016/j.sbspro.2013.12.027

Wu, C., Niu, Z., Tang, Q., Huang, W. 2008. Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. Agricultural and forest meteorology 148: 1230-1241. https://doi.org/10.1016/j.agrformet.2008.03.005

10.1016/j.agrformet.2008.03.005

Yang, J., Rahardja, S., Fränti, P. 2019. Outlier detection: How to threshold outlier scores? pp. 1-6 in Proceedings of the international conference on artificial intelligence, information processing and cloud computing. https://doi.org/10.1145/3371425.3371427

10.1145/3371425.3371427

Information

Publisher :Korean Society of Precision Agriculture
Publisher(Ko) :한국정밀농업학회
Journal Title :Precision Agriculture Science and Technology
Journal Title(Ko) :정밀농업과학기술
Volume : 7
No :4
Pages :388-396
Received Date : 2025-11-26
Revised Date : 2025-12-04
Accepted Date : 2025-12-05
DOI :https://doi.org/10.22765/pastj.20250026

Precision Agriculture Science and Technology ISSN:2672-0086(Print) 2713-5632(Online) 정밀농업과학기술

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