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Volume 17
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10.3390/rs17040632
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Article

Rice Yield Prediction Using Spectral and Textural Indices Derived from UAV Imagery and Machine Learning Models in Lambayeque, Peru

by
Javier Quille-Mamani
1,
Lia Ramos-Fernández
2,*,
José Huanuqueño-Murillo
2,
David Quispe-Tito
2,
Lena Cruz-Villacorta
3,
Edwin Pino-Vargas
4,
Lisveth Flores del Pino
5,
Elizabeth Heros-Aguilar
6 and
Luis Ángel Ruiz
1
1
Geo-Environmental Cartography and Remote Sensing Group (CGAT), Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain
2
Department of Water Resources, National Agrarian University La Molina, Lima 15024, Peru
3
Department of Territorial Planning and Doctoral Program in Engineering and Environmental Sciences, Universidad Nacional Agraria La Molina, Lima 15024, Peru
4
Department of Civil Engineering, Jorge Basadre Grohmann National University, Tacna 23000, Peru
5
Center for Research in Chemistry, Toxicology and Environmental Biotechnology, National Agrarian University La Molina, Lima 15024, Peru
6
Department of Phytotechnics, National Agrarian University La Molina, Lima 15024, Peru
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(4), 632; https://doi.org/10.3390/rs17040632
Submission received: 6 December 2024 / Revised: 31 January 2025 / Accepted: 11 February 2025 / Published: 12 February 2025
(This article belongs to the Special Issue Perspectives of Remote Sensing for Precision Agriculture)
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Abstract

Predicting rice yield accurately is crucial for enhancing farming practices and securing food supplies. This research aims to estimate rice yield in Peru’s Lambayeque region by utilizing spectral and textural indices derived from unmanned aerial vehicle (UAV) imagery, which offers a cost-effective alternative to traditional approaches. UAV data collection in commercial areas involved seven flights in 2022 and ten in 2023, focusing on key growth stages such as flowering, milk, and dough, each showing significant predictive capability. Vegetation indices like NDVI, SP, DVI, NDRE, GNDVI, and EVI2, along with textural features from the gray-level co-occurrence matrix (GLCM) such as ENE, ENT, COR, IDM, CON, SA, and VAR, were combined to form a comprehensive dataset for model training. Among the machine learning models tested, including Multiple Linear Regression (MLR), Support Vector Machines (SVR), and Random Forest (RF), MLR demonstrated high reliability for annual data with an R2 of 0.69 during the flowering and milk stages, and an R2 of 0.78 for the dough stage in 2022. The RF model excelled in the combined analysis of 2022–2023 data, achieving an R2 of 0.58 for the dough stage, all confirmed through cross-validation. Integrating spectral and textural data from UAV imagery enhances early yield prediction, aiding precision agriculture and informed decision-making in rice management. These results emphasize the need to incorporate climate variables to refine predictions under diverse environmental conditions, offering a scalable solution to improve agricultural management and market planning.
Keywords: vegetation indices (VIs); textural indices (TIs); multiple linear regression (MLR); support vector regression (SVR); random forest (RF); cross-validation; machine learning vegetation indices (VIs); textural indices (TIs); multiple linear regression (MLR); support vector regression (SVR); random forest (RF); cross-validation; machine learning
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MDPI and ACS Style

Quille-Mamani, J.; Ramos-Fernández, L.; Huanuqueño-Murillo, J.; Quispe-Tito, D.; Cruz-Villacorta, L.; Pino-Vargas, E.; Flores del Pino, L.; Heros-Aguilar, E.; Ángel Ruiz, L. Rice Yield Prediction Using Spectral and Textural Indices Derived from UAV Imagery and Machine Learning Models in Lambayeque, Peru. Remote Sens. 2025, 17, 632. https://doi.org/10.3390/rs17040632

AMA Style

Quille-Mamani J, Ramos-Fernández L, Huanuqueño-Murillo J, Quispe-Tito D, Cruz-Villacorta L, Pino-Vargas E, Flores del Pino L, Heros-Aguilar E, Ángel Ruiz L. Rice Yield Prediction Using Spectral and Textural Indices Derived from UAV Imagery and Machine Learning Models in Lambayeque, Peru. Remote Sensing. 2025; 17(4):632. https://doi.org/10.3390/rs17040632

Chicago/Turabian Style

Quille-Mamani, Javier, Lia Ramos-Fernández, José Huanuqueño-Murillo, David Quispe-Tito, Lena Cruz-Villacorta, Edwin Pino-Vargas, Lisveth Flores del Pino, Elizabeth Heros-Aguilar, and Luis Ángel Ruiz. 2025. "Rice Yield Prediction Using Spectral and Textural Indices Derived from UAV Imagery and Machine Learning Models in Lambayeque, Peru" Remote Sensing 17, no. 4: 632. https://doi.org/10.3390/rs17040632

APA Style

Quille-Mamani, J., Ramos-Fernández, L., Huanuqueño-Murillo, J., Quispe-Tito, D., Cruz-Villacorta, L., Pino-Vargas, E., Flores del Pino, L., Heros-Aguilar, E., & Ángel Ruiz, L. (2025). Rice Yield Prediction Using Spectral and Textural Indices Derived from UAV Imagery and Machine Learning Models in Lambayeque, Peru. Remote Sensing, 17(4), 632. https://doi.org/10.3390/rs17040632

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