Unmanned aerial vehicles (uavs) for agronomic monitoring of peanut crops
DOI:
https://doi.org/10.56124/sapientiae.v8i17.011Keywords:
Precision agriculture, spectral indices, crop vigor, spatial analysis, agricultural technologyAbstract
This study aimed to monitor the agronomic conditions of peanut (Arachis hypogaea) crops using unmanned aerial vehicles (UAVs) equipped with RGB cameras to improve the efficiency of agricultural resource use. Five spectral indices (GLI, SAVI, RGBVI, MGRVI, and VIgreen) were evaluated in a 7,000 m² experimental plot, using a factorial design that included four peanut varieties, two planting densities, and two types of tillage. Data were captured with a DJI Phantom 4 Pro drone and processed using photogrammetric software and spatial analysis tools. The results showed that the RGBVI index had the highest reflectance values (0.52 ± 0.05), making it the most effective for evaluating crop vigor. The INIAP-381 and INIAP-383 varieties demonstrated higher vigor, while conventional tillage favored crop development compared to zero tillage. The interaction between the INIAP-381 variety and a planting density of 62.500 plants/hectare resulted in higher reflectance, indicating better resource utilization. These findings suggest that UAVs equipped with RGB sensors and the RGBVI spectral index can be accessible and effective tools for peanut crop monitoring, reducing costs and facilitating decision-making in precision agriculture. The application of these technologies can contribute to public policy formulation aimed at revitalizing the peanut sector in Ecuador, strengthening the value chain and increasing productivity.
Downloads
References
Al-Qubati, A., Zhang, L., & Forkel, M. (2024). Urban and peri-urban agriculture under climate change: A review on carbon emissions and sequestration. Sustainable Cities and Society, 115, 105830. https://doi.org/10.1016/J.SCS.2024.105830
Bello Parra, R. O., Valarezo Beltrón, C. O., & Valarezo Molina, M. J. (2023). Diagnóstico de la cadena de valor de mantequilla de maní en Tosagua, Ecuador. ECA Sinergia, 14(3), 91–104. https://doi.org/10.33936/ECASINERGIA.V14I3.5754
Betiol, O., Bolonhezi, D., Leal, É. R. P., Gruener, C. E., Michelotto, M. D., Furlani, C. E. A., & Ruiz, F. F. (2023). Conservation agriculture practices in a peanut cropping system: Effects on pod yield and soil penetration resistance. Revista Brasileira de Ciencia Do Solo, 47. https://doi.org/10.36783/18069657RBCS20230004
Cedeño Vélez, S. J. (2023). Niveles de fertilización en el cultivo de maní (Arachis hypogaea L.) [Tesis de grado, Universidad Laica Eloy Alfaro de Manabí]. Repositorio de la Uleam. https://repositorio.uleam.edu.ec/handle/123456789/4628
Cuenca, K., Quizhpe C., W. R., & Ramírez-Iglesias, E. (2021). Evaluación de sustentabilidad en sistemas de producción de maíz y maní en la provincia de Loja, Ecuador. Agronomía Tropical, 71: e4567645. https://doi.org/10.5281/ZENODO.4567645
El-Hendawy, S., Al-Suhaibani, N., Salem, A. E. A., Ur Rehman, S., & Schmidhalter, U. (2015). Spectral reflectance indices as a rapid and nondestructive phenotyping tool for estimating different morphophysiological traits of contrasting spring wheat germplasms under arid conditions. Turkish Journal of Agriculture and Forestry, 39(4), 572–587. https://doi.org/10.3906/tar-1406-164
FAO. (2018). Perspectivas por sectores principales. 1–43. https://www.fao.org/3/y3557s/y3557s04.pdf
Fernando, K. M. C., Ehoche, O. G., Atkinson, J. A., & Sparkes, D. L. (2021). Root system architecture and nitrogen uptake efficiency of wheat species. Journal of Agricultural Sciences - Sri Lanka, 16(1), 37–53. https://doi.org/10.4038/jas.v16i1.9182
Ingole, R. S., Ingole, N. S., Khandelwal, R. R., & Kalambe, J. P. (2024). Detection of Crop Disease and Spraying of Pesticides using Drone. 2024 IEEE 9th International Conference for Convergence in Technology, I2CT 2024. https://doi.org/10.1109/I2CT61223.2024.10543617
Jiménez Guayanay, T. A., & Peña Merino, S. B. (2021). Análisis de mercadeo y comercialización en la cadena de valor de maní arachis hypogaea l., en la provincia de Loja[Universidad Nacional deLoja]. https://dspace.unl.edu.ec//handle/123456789/23759
Kamenova, I., Filchev, L., & Ilieva, I. (2017). REVIEW OF SPECTRAL VEGETATION INDICES AND METHODS FOR ESTIMATION OF CROP BIOPHYSICAL VARIABLES. 29.
López Rodríguez, S., van Bussel, L. G. J., & Alkemade, R. (2024). Classification of agricultural land management systems for global modeling of biodiversity and ecosystem services. Agriculture, Ecosystems & Environment, 360, 108795. https://doi.org/10.1016/J.AGEE.2023.108795
Lóránt, B., Veronika, K.-B., & József, B. (2024). Comparison of RGB Indices used for Vegetation Studies based on Structured Similarity Index (SSIM). Journal of Plant Science and Phytopathology, 8(1), 007–012. https://doi.org/10.29328/JOURNAL.JPSP.1001124
Lv, X., Zhang, X., Gao, H., He, T., Lv, Z., & Zhangzhong, L. (2024). When crops meet machine vision: A review and development framework for a low-cost nondestructive online monitoring technology in agricultural production. Agriculture Communications, 2(1), 100029. https://doi.org/10.1016/J.AGRCOM.2024.100029
Marin, D. B., Rossi, G., Araújo, G., Ferraz, S., Dorbu, F., & Hashemi-Beni, L. (2024). Detection of Individual Corn Crop and Canopy Delineation from Unmanned Aerial Vehicle Imagery. Remote Sensing 2024, Vol. 16, Page 2679, 16(14), 2679. https://doi.org/10.3390/RS16142679
Moreira, B. R. de A., Marra, T. M., Silva, E. A. da, Brito Filho, A. L. de, Barbosa Júnior, M. R., Santos, A. F. dos, Silva, R. P. da, & Vellidis, G. (2024). Advancements in peanut mechanization: Implications for sustainable agriculture. Agricultural Systems, 215, 103868. https://doi.org/10.1016/J.AGSY.2024.103868
Pacheco Gil, H. A., & Montilla Pacheco, A. de J. (2020). RGB Spectral Indices for the Analysis of Soil Protection by Vegetation Cover against Erosive Processes. Soil Erosion - Current Challenges and Future Perspectives in a Changing World. https://doi.org/10.5772/INTECHOPEN.95055
Ponce Conforme, A. M., Rodríguez Mala, A. S., & Pacheco Gil, H. A. (2024). Condiciones agronómicas del cultivo de arroz con imágenes de vehículos aéreos no tripulados. Revista Científica Multidisciplinaria SAPIENTIAE. ISSN: 2600-6030, 7(14), 35–54. https://doi.org/10.56124/SAPIENTIAE.V7I14.0003
Qu, H., Zheng, C., Ji, H., Barai, K., & Zhang, Y. J. (2024). A fast and efficient approach to estimate wild blueberry yield using machine learning with drone photography: Flight altitude, sampling method and model effects. Computers and Electronics in Agriculture, 216, 108543. https://doi.org/10.1016/J.COMPAG.2023.108543
Raghuwanshi, S., Chaudhary, R. S., Somasundaram, J., Sinha, N. K., Trivedi, S. K., Kurmi, P., Vijayaraje, R., Krishi, S., Vidyalaya, V., Gwalior, M., & Pradesh, I. (2024). A Comparative Study of Long-Term Conventional and No-Tillage Practices on the Basis of Available Soil Nutrients, Soil Organic Carbon and Crop Productivity in Black Soils of Central India. Asian Journal of Soil Science and Plant Nutrition, 10(3), 561–571. https://doi.org/10.9734/AJSSPN/2024/V10I3369
Shojaeezadeh, S., Elnashar, A., & Weber, T. K. D. (2024). Estimating Crop Phenology from Satellite Data using Machine Learning. EGU24. https://doi.org/10.5194/EGUSPHERE-EGU24-15347
Sumesh, K. C., Ninsawat, S., & Som-ard, J. (2021). Integration of RGB-based vegetation index, crop surface model and object-based image analysis approach for sugarcane yield estimation using unmanned aerial vehicle. Computers and Electronics in Agriculture, 180, 105903. https://doi.org/10.1016/J.COMPAG.2020.105903
Swethasree, M., Sudhakar, P., Umamahesh, V., Prathima, T., & Krishna, T. G. (2024). Effect of planting density on yield and architecture suitability of groundnut (Arachis hypogaea) varieties. Indian Journal of Agricultural Sciences, 94(3), 297–302. https://doi.org/10.56093/IJAS.V94.I3.138914
Wajhat, N., V, N., SA, N., & ZA, D. (2019). Drought tolerance mechanism in wheat: A Review. The Pharma Innovation, 714–724. https://doi.org/https://dx.doi.org/10.22271/tpi
Zhang, P., Lu, B., Ge, J., Wang, X., Yang, Y., Shang, J., La, Z., Zang, H., & Zeng, Z. (2025). Using UAV-based multispectral and RGB imagery to monitor above-ground biomass of oat-based diversified cropping. European Journal of Agronomy, 162, 127422. https://doi.org/10.1016/J.EJA.2024.127422
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Revista Científica Multidisciplinaria SAPIENTIAE. ISSN: 2600-6030.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

2.jpg)














