Bean yield estimation using unmanned aerial vehicle imagery


  • Diane Gomes Campos Federal Institute of Northern Minas Gerais - IFNMG, Campus Araçuaí, MG, Brazil.
  • Rodrigo Nogueira Martins Federal Institute of Northern Minas Gerais - IFNMG, Campus Araçuaí, MG, Brazil.



Digital agriculture, UAV, Vegetation index, Phaseolus vulgaris L.


The common bean is a crop of substantial socioeconomic importance that is cultivated throughout the Brazilian territory. Despite that, studies conducted so far have shown limitations in the methodologies used for yield estimation. In this sense, emerging technologies such as unmanned aerial vehicles (UAVs) can help both in crop monitoring and in assessing crop yield. Therefore, this study aimed: (1) to estimate the bean yield using spectral variables derived from UAV imagery and (2) to define the best vegetative stage for yield estimation. For this, data from a field experiment were used. The beans were planted in a conventional system in an area of 600 m² (20 x 30 m). During the crop cycle, six flights were carried out using a UAV equipped with a five-band multispectral camera (Red, Green, Blue, Red Edge, and Near-infrared). After that, 10 spectral variables composed of the bands and five vegetation indices (VIs) were obtained. At the end of the season, the area was harvested, and the yield (kg ha-1) was determined. Then, the data was submitted to correlation (r), and regression analysis. Overall, all developed models showed moderate performance, but in accordance with the literature, with R² and RMSE values ranging from 0.52 to 0.57 and from 252.79 to 208.84 kg ha-1, respectively. Regarding the best vegetative stage for yield estimation, the selected models used data from the second flight (52 days after planting) at the beginning of pod formation and filling (between stages R7 and R8).


Download data is not yet available.


Alvares, C. A., Stape, J. L., Sentelhas, P. C., Gonçalves, J. D. M., & Sparovek, G. (2013). Köppen’s climate classification map for Brazil. Meteorologische zeitschrift, 22(6), 711-728. DOI:

Araus, J. L., & Cairns, J. E. (2014). Field high-throughput phenotyping: the new crop breeding frontier. Trends in plant science, 19(1), 52-61. DOI:

Baio, F. H. R., Neves, D. C., Campos, C. D. S., & Teodoro, P. E. (2018). Relationship between cotton productivity and variability of NDVI obtained by Landsat images. Bioscience Journal, 34(Supplement 1), 197-205. DOI:

Baloloy, A. B., Blanco, A. C., Ana, R. R. C. S., & Nadaoka, K. (2020). Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 166, 95-117. DOI:

Baptista, G. D. M. (2015). Aplicação do Índice de Vegetação por Profundidade de Feição Espectral (SFDVI–Spectral Feature Depth Vegetation Index) em dados RapidEye.(Application of Spectral Feature Depth Vegetation Index (SFDVI) to RapidEye data). Anais XVII Simpósio Brasileiro de Sensoriamento Remoto-SBSR, João Pessoa-PB, Brasil, 25.

Bellvert, J., Zarco-Tejada, P. J., Girona, J., & Fereres, E. J. P. A. (2014). Mapping crop water stress index in a ‘Pinot-noir’vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle. Precision agriculture, 15, 361-376. DOI:

Brasil, M. da A. P. e A. (2009). Regras para Análise de Sementes (1st ed.).

CONAB, C. N. de A. (2021). Acompanhamento da safra brasileira: grãos. Safra 2020/21. Décimo segundo levantamento, Junho 2022. Monitoramento Agricola - Safra 2020.

CONAB, C. N. de A. (2022). Acompanhamento da safra brasileira: grãos. Safra 2021/22. Nono levantamento, Julho 2022. Monitoramento Agricola - Safra 2022.

Da Silva, E. E., Baio, F. H. R., Teodoro, L. P. R., da Silva Junior, C. A., Borges, R. S., & Teodoro, P. E. (2020). UAV-multispectral and vegetation indices in soybean grain yield prediction based on in situ observation. Remote Sensing Applications: Society and Environment, 18, 100318. DOI:

De Andrade, E. K. V., Rodrigues, R., Bard, G. D. C. V., da Silva Pereira, L., Baptista, K. E. V., Cavalcanti, T. F. M., ... & Gomes, V. M. (2020). Identification, biochemical characterization and biological role of defense proteins from common bean genotypes seeds in response to Callosobruchus maculatus infestation. Journal of stored products research, 87, 101580. DOI:

Finger, R., Swinton, S. M., El Benni, N., & Walter, A. (2019). Precision farming at the nexus of agricultural production and the environment. Annual Review of Resource Economics, 11, 313-335. DOI:

Gao, F., Anderson, M., Daughtry, C., Karnieli, A., Hively, D., & Kustas, W. (2020). A within-season approach for detecting early growth stages in corn and soybean using high temporal and spatial resolution imagery. Remote Sensing of Environment, 242, 111752. DOI:

Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote sensing of Environment, 58(3), 289-298. DOI:

Gitelson, A., & Merzlyak, M. N. (1994). Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Journal of plant physiology, 143(3), 286-292. DOI:

Hassan, M. A., Yang, M., Rasheed, A., Yang, G., Reynolds, M., Xia, X., ... & He, Z. (2019). A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant science, 282, 95-103. DOI:

Hebbali, A., & Hebbali, M. A. (2017). Package ‘olsrr’. Version 0.5, 3.

Hiolanda, R., Machado, D. H., Candido, W. J., de Faria, L. C., & Dalchiavon, F. C. (2018). Desempenho de genótipos de feijão carioca no Cerrado Central do Brasil. Revista de ciências agrárias, 41(3), 815-824. DOI:

ISPA. (2024). Precision Ag Definition. International Society of Precision Agriculture.

Ji, Y., Chen, Z., Cheng, Q., Liu, R., Li, M., Yan, X., ... & Yang, T. (2022). Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.). Plant Methods, 18(1), 26. DOI:

Jw, R. (1973). Monitoring vegetation systems in the great plains with ERTS. In Third NASA Earth Resources Technology Satellite Symposium, 1973 (Vol. 1, pp. 309-317).

Li, B., Xu, X., Zhang, L., Han, J., Bian, C., Li, G., ... & Jin, L. (2020). Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 161-172. DOI:

Lipovac, A., Bezdan, A., Moravčević, D., Djurović, N., Ćosić, M., Benka, P., & Stričević, R. (2022). Correlation between Ground Measurements and UAV Sensed Vegetation Indices for Yield Prediction of Common Bean Grown under Different Irrigation Treatments and Sowing Periods. Water, 14(22), 3786. DOI:

Macedo, F. L., Nóbrega, H., de Freitas, J. G., Ragonezi, C., Pinto, L., Rosa, J., & Pinheiro de Carvalho, M. A. (2023). Estimation of productivity and above-ground biomass for corn (Zea mays) via vegetation indices in Madeira Island. Agriculture, 13(6), 1115. DOI:

Martins, R. N., Portes, M. F., e Moraes, H. M. F., Junior, M. R. F., Rosas, J. T. F., & Junior, W. D. A. O. (2021). Influence of tillage systems on soil physical properties, spectral response and yield of the bean crop. Remote Sensing Applications: Society and Environment, 22, 100517. DOI:

Mercante, E., Lamparelli, R. A., Uribe-Opazo, M. A., & Rocha, J. V. (2009). Características espectrais da soja ao longo do ciclo vegetativo com imagens landsat 5/TM em área agrícola no oeste do Paraná. Engenharia Agrícola, 29, 328-338. DOI:

Merzlyak, M. N. et. al. Does a leaf absorb radiation in the near infrared (780-900 nm) region? A new approach to quantifying optical reflection, absorption and transmission of leaves. Photosynthesis Research, v. 72, 2002. DOI:

Nogueira Martins, R., de Carvalho Pinto, F. D. A., Marçal de Queiroz, D., Magalhães Valente, D. S., & Fim Rosas, J. T. (2021). A novel vegetation index for coffee ripeness monitoring using aerial imagery. Remote Sensing, 13(2), 263. DOI:

Nowak, B. (2021). Precision agriculture: Where do we stand? A review of the adoption of precision agriculture technologies on field crops farms in developed countries. Agricultural Research, 10(4), 515-522. DOI:

Prudente, V. H. R., Mercante, E., Johann, J. A., Souza, C. H. W. D., Cattani, C. E. V., Mendes, I. S., & Caon, I. L. (2021). Use of terrestrial remote sensing to estimate soybeans and beans biophysical parameters. Geocarto International, 36(7), 773-790. DOI:

Quille-Mamani, J., Porras-Jorge, R., Saravia-Navarro, D., Valqui-Valqui, L., Herrera, J., Chávez-Galarza, J., & Arbizu, C. I. (2022). Assessment of vegetation indices derived from UAV images for predicting biometric variables in bean during ripening stage. Idesia, 40(2), 39-45. DOI:

Ramos, A. P. M., Osco, L. P., Furuya, D. E. G., Gonçalves, W. N., Santana, D. C., Teodoro, L. P. R., ... & Pistori, H. (2020). A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices. Computers and Electronics in Agriculture, 178, 105791. DOI:

Ranjan, R., Chandel, A. K., Khot, L. R., Bahlol, H. Y., Zhou, J., Boydston, R. A., & Miklas, P. N. (2019). Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology. Information Processing in Agriculture, 6(4), 502-514. DOI:

Rehman, T. H., Borja Reis, A. F., Akbar, N., & Linquist, B. A. (2019). Use of normalized difference vegetation index to assess N status and predict grain yield in rice. Agronomy Journal, 111(6), 2889-2898. DOI:

Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote sensing of environment, 55(2), 95-107. DOI:

Sankaran, S., Zhou, J., Khot, L. R., Trapp, J. J., Mndolwa, E., & Miklas, P. N. (2018). High-throughput field phenotyping in dry bean using small unmanned aerial vehicle based multispectral imagery. Computers and Electronics in Agriculture, 151, 84-92. DOI:

Saravia, D., Valqui-Valqui, L., Salazar, W., Quille-Mamani, J., Barboza, E., Porras-Jorge, R., ... & Arbizu, C. I. (2023). Yield prediction of four bean (Phaseolus vulgaris) cultivars using vegetation indices based on multispectral images from UAV in an arid zone of Peru. Drones, 7(5), 325. DOI:

Soil Survey Staff. Keys to soil taxonomy by soil survey staff. United States. Department of Agriculture Natural Resources Conservation Service. 12º ed, 2014.

Team, Q. D. (2016). QGIS geographic information system. Open source geospatial foundation project.

Team, R. C. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing.

Yuan, D., & Elvidge, C. D. (1996). Comparison of relative radiometric normalization techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 51(3), 117-126. DOI:

Yue, J., Feng, H., Li, Z., Zhou, C., & Xu, K. (2021). Mapping winter-wheat biomass and grain yield based on a crop model and UAV remote sensing. International Journal of Remote Sensing, 42(5), 1577-1601. DOI:

Zhao, D., Reddy, K. R., Kakani, V. G., Read, J. J., & Koti, S. (2007). Canopy reflectance in cotton for growth assessment and lint yield prediction. European Journal of Agronomy, 26(3), 335-344. DOI:

Zhao, Y., Potgieter, A. B., Zhang, M., Wu, B., & Hammer, G. L. (2020). Predicting wheat yield at the field scale by combining high-resolution Sentinel-2 satellite imagery and crop modelling. Remote Sensing, 12(6), 1024. DOI:

Zhou, J., Khot, L. R., Boydston, R. A., Miklas, P. N., & Porter, L. (2018). Low altitude remote sensing technologies for crop stress monitoring: A case study on spatial and temporal monitoring of irrigated pinto bean. Precision agriculture, 19, 555-569. DOI:




How to Cite

Gomes Campos, D., & Nogueira Martins, R. (2024). Bean yield estimation using unmanned aerial vehicle imagery. Revista Brasileira De Engenharia De Biossistemas, 18.



Regular Section