Use of remote sensing in agriculture: Applications in banana crop




synthetic aperture radar, unmanned aerial vehicles, satellite images, vegetation index, radar


Introduction. Remote sensors offer the ability to observe an object without being in contact with it. They are widely used in agricultural applications and have large development potential in banana (Musa AAA) plantations. During the past decades, the research in remote sensing and agriculture has increased through the availability of high-resolution satellite images (spatial, spectral, and temporal) and the use of remotely piloted vehicles that generate base information for research. Objective. To carry out a general review on the applications of the use of remote sensors for banana plantations in three specific aspects: determination of the cultivation area, productivity estimation, and disease diagnosis. Development. The extension of land covered by commercial banana plantations can be detected visually or easily by means of remote image classifications, such as the Synthetic Aperture Radar (SAR) sensor, which hve resulted in classification accuracies of around 95%. This is due to the high backscattering of the large leaves of the plant. However, the studies on productivity are scarce for banana cultivation and have been limited to the use of vegetation index, showing poor results in their correlations. As for the identification of diseases, work has been done on the main diseases affecting production with correlation levels above 90 % for some diseases. Conclusion. This review shows that banana plantations can be detected through the use of remote sensors and, likewise, these allow the identification of the main diseases in the crop. However, the results obtained to determine productivity are scarce and with little precision.


Download data is not yet available.


Beaulieu, N., Leclerc, N., Velásquez, S., Pigeonnat, S., Gribius, N., Escalant, J. V., & Bonn, F. (1994). Investigations at CATIE on the potential of high-resolution radar images for monitoring of agriculture in Central America. Centro Agronómico Tropical de Investigación y Enseñanza.

Bendini, H. N., Jacon, A. D., Moreira Pessôa, A. C., Pompeu Pavenelli, J. A., Moraes, W. S., Ponzoni, F. J., & Fonseca, L. M. (2015). Caracterização espectral de folhas de bananeira (Musa spp.) para detecção e diferenciação da Sigatoka Negra e Sigatoka Amarela. In D. F. Marcolino Gherardi, & L. E. Oliveira e Cruz de Aragão (Eds.), Anais XVII Simpósio Brasileiro de Sensoriamento Remoto (pp. 2536–2543). Instituto Nacional de Pesquisas Espaciais.

Campos Calou, V. B., dos Santos Teixeira, A., Moreira, L. C. J., Souza Lima, C., de Oliveira, J. B., & Rabelo de Oliveira, M. R. (2020). The use of UAVs in monitoring yellow sigatoka in banana. Biosystems Engineering, 193, 115–125.

Chemura, A. (2017). Modelling spatial variability of coffee (Coffea Arabica L.) crop condition with multispectral remote sensing data [Doctoral Dissertation, University of KwaZulu-Natal]. University of KwaZulu-Natal Repository.

Clark, A., & McKechnie, J. (2020). Detecting banana plantations in the wet tropics, Australia, using aerial photography and U-net. Applied Sciences, 10(6), Article 2017.

Clevers, J. G. P. W., & Kooistra, L. (2012). Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(2), 574–583.

Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79–97.

Corporación Bananera Nacional. (2019). Estadisticas de exportación bananera 2019. CORBANA S.A.

Corporación Bananera Nacional. (2021, 15 mayo). Banano de Costa Rica: Estadísticas.

Fagan, M. E., DeFries, R. S., Sesnie, S. E., Arroyo-Mora, J. P., Soto, C., Singh, A., Townsend, P. A., & Chazdon, R. L. (2015). Mapping species composition of forests and tree plantations in northeastern Costa Rica with an integration of hyperspectral and multitemporal landsat imagery. Remote Sensing, 7(5), 5660–5696.

Giovos, R., Tassopoulos, D., Kalivas, D., Lougkos, N., & Priovolou, A. (2021). Remote sensing vegetation indices in viticulture: A critical review. Agriculture, 11(5), Article 457.

Gitelson, A. A., Viña, A., Ciganda, V., Rundquist, D. C., & Arkebauer, T. J. (2005). Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32(8), Article L08403.

Gomez Selvaraj, M., Vergara, A., Montenegro, F., Alonso Ruiz, H., Safari, N., Raymaekers, D., Ocimati, W., Ntamwira, J., Tits, L., Bonaventure Omondi, A., & Blomme, G. (2020). Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin. ISPRS Journal of Photogrammetry and Remote Sensing, 169, 110–124.

Harto, A. B., Dwi Prastiwi, P. A., Ariadji, F. N., Suwardhi, D., Dwivany, F. M., Nuarsa, I. W., & Wikantika, K. (2019). Identification of banana plants from unmanned aerial vehicles (UAV) photos using object based image analysis (OBIA) method (a case study in Sayang Village, Jatinangor District, West Java). HAYATI Journal of Biosciences, 26(1), 7–14.

Hassler, S. C., & Baysal-Gurel, F. (2019). Unmanned aircraft system (UAS) technology and applications in agriculture. Agronomy, 9(10), Article 618.

Hatfield, J. L., Prueger, J. H., Sauer, T. J., Dold, C., O’Brien, P., & Wacha, K. (2019). Applications of vegetative indices from remote sensing to agriculture: Past and future. Inventions, 4(4), Article 71.

Israeli, Y., & Lahav, E. (2017). Banana. In B. Thomas, B. G. Murray, & D. J. Murphy (Eds.), Encyclopedia of applied plant sciences (2nd ed., pp. 363–381). Academic Press.

Jin, X., Kumar, L., Li, Z., Feng, H., Xu, X., Yang, G., & Wang, J. (2018). A review of data assimilation of remote sensing and crop models. European Journal of Agronomy, 92, 141–152.

Johansen, K., Phinn, S., Witte, C., Philip, S., & Newton, L. (2009). Mapping Banana Plantations from Object-oriented Classification of SPOT-5 Imagery. Photogrammetric Engineering & Remote Sensing, 75(9), 1069–1081.

Johansen, K., Sohlbach, M., Sullivan, B., Stringer, S., Peasley, D., & Phinn, S. (2014). Mapping banana plants from high spatial resolution orthophotos to facilitate plant health assessment. Remote Sensing, 6(9), 8261–8286.

Lamour, J., Naud, O., Lechaudel, M., Le Moguédec, G., Taylor, J., & Tisseyre, B. (2020). Spatial analysis and mapping of banana crop properties: issues of the asynchronicity of the banana production and proposition of a statistical method to take it into account. Precision Agriculture, 21(4), 897–921.

Leclerc, G., & Hall, C. A. S. (2000). Remote sensing and land use analysis for agriculture in Costa Rica. In G. L. Charles, A. S. Hall, & C. Leon-Perez (Eds.), Quantifying sustainable development (pp. 295–346). Academic Press.

Machovina, B. L., Feeley, K. J., & Machovina, B. J. (2016). UAV remote sensing of spatial variation in banana production. Crop and Pasture Science, 67(12), 1281–1287.

Martínez-Solórzano, G. E., & Rey-Brina, J. C. (2021). Bananas (Musa AAA): Importancia, producción y comercio en tiempos de Covid-19. Agronomía Mesoamericana, 32(3), 1034–1046.

McNairn, H., & Shang, J. (2016). A review of multitemporal synthetic aperture radar (SAR) for crop monitoring. In Y. Ban (Ed.), Multitemporal remote sensing. Methods and applications (pp. 317–340). Springer International.

Montero González, H. J. (2016). Simulación de la floración del cultivo de banano (Musa AAA cv. ’Grande Naine’) mediante el modelo “SIMBA-CR” adaptado a la vertiente Caribe de Costa Rica [Tesis de Licenciatura, no publicada]. Universidad de Costa Rica.

Morel, J., Jay, S., Féret, J.-B., Bakache, A., Bendoula, R., Carreel, F., & Gorretta, N. (2018). Exploring the potential of PROCOSINE and close-range hyperspectral imaging to study the effects of fungal diseases on leaf physiology. Scientific Reports, 8, Article 15933.

Neupane, B., Horanont, T., & Hung, N. D. (2019). Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV). PLoS ONE, 14(10), Article e0223906.

Novero, A. U., Pasaporte, M. S., Aurelio Jr, R. M., Madanguit, C. J. G., Tinoy, M. R. M., Luayon, M. S., Oñez, J. P. L., Daquiado, E. G. B., Diez, J. M. A., Ordaneza, J. E., Riños, L. J., Capin, N. C., Pototan, B. L., Tan, H. G., Polinar, M. D. O., Nebres, D. I., & Nañola Jr, C. L. (2019). The use of light detection and ranging (LiDAR) technology and GIS in the assessment and mapping of bioresources in Davao Region, Mindanao Island, Philippines. Remote Sensing Applications: Society and Environment, 13, 1–11.

Nyombi, K. (2010). Understanding growth of East Africa highland banana: experiments and simulation [Doctoral Dissertation, Wageningen University]. Wageningen University Repository.

Organización de Naciones Unidas para la Alimentación y la Agricultura. (2020). Análisis del mercado del banano: resultados preliminares 2019.

Pedroni, L. (2003). Improved classification of Landsat Thematic Mapper data using modified prior probabilities in large and complex landscapes. International Journal of Remote Sensing, 24(1), 91–113.

Pena, J., Tan, Y., & Boonpook, W. (2019). Semantic segmentation based remote sensing data fusion on crops detection. Journal of Computer and Communications, 7(7), 53–64.

Pérez-Vicente, L., Guzmán, M., Pasberg-Gauhl, C., Gauhl, F., & Jones, D. R. (2018). Fungal diseases of the foliage: Sigatoka leaf spot. In D. R. Jones (Ed.), Handbook of diseases of banana, abacá and enset (pp. 41–206). CABI.

Ploetz, R. C. (2018). Fungal diseases of the root, corm and pseudostem. In D. R. Jones (Ed.), Handbook of diseases of banana, abacá and enset (pp. 207–228). CABI.

Qi, Z., Yeh, A. G.-O., Li, X., & Lin, Z. (2012). A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data. Remote Sensing of Environment, 118, 21–39.

Rabatel, G., Lamour, J., Moura, D., & Naud, O. (2019). A multispectral processing chain for chlorophyll content assessment in banana fields by UAV imagery. In J. V. Stafford (Ed.), Precision agriculture ‘19 (pp. 413–419). Wageningen Academic Publishers.

Sinha, P., Robson, A., Schneider, D., Kilic, T., Mugera, H. K., Ilukor, J., & Tindamanyire, J. M. (2020). The potential of in-situ hyperspectral remote sensing for differentiating 12 banana genotypes grown in Uganda. ISPRS Journal of Photogrammetry and Remote Sensing, 167, 85–103.

Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), Article 3136.

Soto Ballestero, M. (2014). Bananos I: conceptos básicos (1ª ed.). Editorial Tecnológica de Costa Rica.

Steele-Dunne, S. C., McNairn, H., Monsivais-Huertero, A., Judge, J., Liu, P. -W., & Papathanassiou, K. (2017). Radar remote sensing of agricultural canopies: A review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5), 2249–2273.

Stoorvogel, J. J., Verhoeven, R., van Leeuwen, H., & Orlich, R. (2001). AeroBanMan: the aerial detection of plant distribution and fungus infection for precision banana management. Lab of Soil Science and Geology WAU.

Tixier, P., Dorel, M., & Malézieux, E. (2007). A model-based Approach to maximise gross income by selection of banana planting date. Biosystems Engineering, 96(4), 471–476.

Ugarte Fajardo, J., Bayona Andrade, O., Criollo Bonilla, R., Cevallos Cevallos, J., Mariduena-Zavala, M., Ochoa Donoso, D., & Vicente Villardón, J. L. (2020). Early detection of black Sigatoka in banana leaves using hyperspectral images. Applications in Plant Sciences, 8(8), Article e11383.

Usha, K., & Singh, B. (2013). Potential applications of remote sensing in horticulture—A review. Scientia Horticulturae, 153, 71–83.

Veldkamp, E., Huising, E. J., Stein, A., & Bouma, J. (1990). Variation of measured banana yields in a Costa Rican plantation as explained by soil survey and thematic mapper data. Geoderma, 47(3–4), 337–348.

Verhoeye, J., & De Wulf, R. (1999). An image processing chain for land-cover classification using multitemporal ERS-1 data. Photogrammetric Engineering and Remote Sensing, 65(10), 1179–1186.

Wang, X., Wang, Q., Ling, F., Zhu, X., & Jiang, H. (2009). Principal component analysis and its application on banana fields mapping using ENVISAT ASAR data in Zhangzhou, Fujian Province. Geo-Spatial Information Science, 12(2), 142–145.

Weiss, M., Jacob, F., & Duveiller, G. (2020). Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment, 236, Article 111402.

Wójtowicz, M., Wójtowicz, A., & Piekarczyk, J. (2016). Application of remote sensing methods in agriculture. Communications in Biometry and Crop Science, 11(1), 31–50.

Ye, H., Huang, W., Huang, S., Cui, B., Dong, Y., Guo, A., Ren, Y., & Jin, Y. (2020a). Identification of banana fusarium wilt using supervised classification algorithms with UAV-based multi-spectral imagery. International Journal of Agricultural and Biological Engineering, 13(3), 136–142.

Ye, H., Huang, W., Huang, S., Cui, B., Dong, Y., Guo, A., Ren, Y., & Jin, Y. (2020b). Recognition of banana Fusarium wilt based on UAV remote sensing. Remote Sensing, 12(6), Article 938.

Zhao, H., Chen, Z., Jiang, H., Jing, W., Sun, L., & Feng, M. (2019). Evaluation of three deep learning models for early crop classification using Sentinel-1A imagery time series—A case study in Zhanjiang, China. Remote Sensing, 11(22), Article 2673.



How to Cite

Guzman-Alvarez, J. A., González-Zuñiga, M., Sandoval Fernandez, J. A., & Calvo-Alvarado, J. C. (2022). Use of remote sensing in agriculture: Applications in banana crop. Agronomía Mesoamericana, 33(3), 48279.