Multispectral aerial image processing system for precision agriculture

Authors

  • Samy Kharuf-Gutierrez Universidad Central “Marta Abreu” de las Villas
  • Rubén Orozco-Morales Universidad Central Marta Abreu de Las Villas, Santa Clara
  • Osmany de la C. Aday Díaz Estación Territorial de Investigaciones de la Caña de Azúcar
  • Emma Pineda Ruiz Estación Territorial de Investigaciones de la Caña de Azúcar

DOI:

https://doi.org/10.18046/syt.v16i47.3221

Keywords:

Near infrared; precision agriculture; Sequoia: unmanned aerial vehicle; vegetation index.

Abstract

Cuban agriculture has the growing need to increase its productivity. To achieve this, precision agriculture can play a fundamental role. It is necessary to develop an image processing system able to process all the crops information and calculate vegetation indexes in a satisfactory way. This will entail in accurate measurements of the nitrogen lack, the hydric stress, and the vegetal strength, among other aspects, seeking to improve the accuracy in the care of these aspects. This document reports the results of an investigation pointed to develop a procedure for capturing and processing multispectral aerial images obtained from Unmanned Aerial Vehicles [UAV]. This procedure searched to measure the vegetation indexes of sugarcane crops that may be correlated with the level of vegetal strength, the number of stems, and the foliar mass per lot. We used a USENSE-X8 UAV together with a Sequoia multispectral sensor and the QGIS processing software. The procedure was experimentally validated.

Author Biographies

  • Samy Kharuf-Gutierrez, Universidad Central “Marta Abreu” de las Villas

    Automation Engineer from the Universidad Central “Marta Abreu” de Las Villas [UCLV] (Cuba, 2014). He is a professor at the Department of Automation and Computer Systems from the UCLV’s Faculty of Engineering and member of its Automation, Robotics and Perception Group [GARP]. His areas of professional interest include: multispectral image processing, modeling and control and guidance of unmanned vehicles.

  • Rubén Orozco-Morales, Universidad Central Marta Abreu de Las Villas, Santa Clara

    Engineer in Electronics, Master in Telecommunications (1994) and Ph.D., in Technical Sciences (1998) from the Electrical Engineering School of the Universidad Central de Las Villas (Santa Clara, Cuba). He is a professor at the Department of Automation and Computer Systems of the Faculty of Engineering of the same university and member of the Automation, Robotics and Perception Group [GARP]. His areas of professional interest include image analysis and recognition of patterns in images.

  • Osmany de la C. Aday Díaz, Estación Territorial de Investigaciones de la Caña de Azúcar

    Agronomics Engineer from the Universidad Central de Las Villas (Santa Clara, Cuba and PhD in Technical Sciences (2015) from the Universidad Agraria de la Habana “Fructuoso Rodríguez Pérez”. He works for ETICA the main research entity in matters related with sugar cane in Cuba. His main area of professional interest is vegetal sanitation.

  • Emma Pineda Ruiz, Estación Territorial de Investigaciones de la Caña de Azúcar

    Agronomics Engineer from the Universidad Central de Las Villas (Santa Clara, Cuba, 1981) and PhD in Technical Sciences (2002). She works for ETICA the main research entity in matters related with sugar cane in Cuba. Her main area of professional interest is the edaphology.

References

Bachmann, F., Herbst, R., Gebbers, R., & Hafner, V.V. (2013). Micro UAV based georeferenced orthophoto generation in VIS+NIR for precision agriculture. In: Proceedings of the UAV. Remote Sensing and Spatial Information Sciences, (Vol. 40. pp. 11-16).

Basso, B. (2014). Perspectivas y avances del uso de UAV en AP en USA. Retrieved from: https://inta.gob.ar/sites/default/files/script-tmp-inta_g1-perspectivas_y_avances_del_uso_de_uav_en_ap_e.pdf

Best, S. & Zamora, I. (2008). Tecnologías aplicables en agricultura de precisión: uso de tecnología de precisión en evaluación, diagnóstico y solución de problemas productivos. Santiago de Chile: Fundación para la Innovación Agraria.

Best, S., León, L., & Claret, M. (2005). Use of precision viticulture tools to optimize the harvest of high quality grapes. Proceedings of the fruits and nuts and vegetable production engineering TIC (Frutic05), (pp. 249-258).

Campo, L., Corrales, J. & Ledezma, A. (2015). Remote sensing for agricultural crops based on a low cost quadcopter. Sistemas & Telemática, 13(34), 49-63. doi:10.18046/syt.v13i34.2092

Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., & Gattelli, M. (2015). Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sensing, 7(4), 4026-4047.

Chuvieco, L. (2000). The use of qualitative airbone multispectral imaging for managing agricultural crops: a case study in South- Eastern Australia. Aust. J. Exp. Agric, 40, 725-738.

Dennis, L., Wright, J. & Philip, R. (2003). Managing protein in hard red spring wheat with remote sensing [paper in The 6th Annual National Wheat Industry Research Forum, 2003. Retrieved from: https://www.researchgate.net/publication/252140884_Managing_Grain_Protein_in_Wheat_Using_Remote_Sensing

Gago, J., Douthe, C., Coopman, R., Gallego, P., Ribas-carbo, M., ... & Medrano, H. (2015). UAVs challenge to assess water stress for sustainable agriculture. Agricultural Water Management, 153, 9-19.

García, C. & Herrera, F. (2015). Percepción remota en cultivos de caña de azúcar usando una cámara multiespectral en vehículos aéreos no tripulados [paper in: Anais XVII Simpósio Brasileiro de Sensoriamento Remoto-SBSR. Retrieved from: http://www.dsr.inpe.br/sbsr2015/files/p0873.pdf

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.

Guo, T., Kujirai, T.. & Watanabe, T. (2012). Mapping crop status from an unmanned aerial vehicle for precision agriculture applications. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (Vol. 39-B1 pp.485-490). ISPRS.

Gutierrez-Rodriguez, M., Escalante-Estrada, J. A., & Rodriguez-Gonzalez, M. T. (2005). Canopy reflectance, stomatal conductance, and yield of Phaseolus vulgaris L. and Phaseolus coccinues L. under saline field conditions. Int. J. Agric. Biol, 7, 491-494.

Hatfield, J. L. & Prueger, J. H. (2010). Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices. Remote Sensing, 2, 562-578.

Hernández, L., Rodríguez, E., Martínez, A., Álvarez, H., Kharuf, S., & Morales, L. H. (2016). Levantamiento fotogramétrico de la UBPC “Desembarco del Granma” utilizando aviones no tripulados, solución de bajo costo para la agricultura nacional. In: VII Edición de la Conferencia Científica Internacional sobre Desarrollo Agropecuario y Sostenibilidad 2016. Santa Clara, Cuba: UCLV.

Hernández-Morales, L., Valeriano-Medina, Y., Hernández-Julián, A. & Hernández-Santana, L. (2017). Estudio sobre la estrategia de guiado L1 para el seguimiento de caminos rectos y curvos en UAV. Ingeniería Electrónica, Automática y Comunicaciones, 38, 14-25

Huete, A.R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295-309.

Hunt, E. R., Hively, W. D., Fujikawa, S. J., Linden, D. S., Daughtry, C. S., & McCarty, G. W. (2010). Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sensing, 2, 290-305.

Johansen, K., Sallam, N., Robson, A., Samson, P., Chandler, K., Derby, L., ... & Jennings, J. (2018). Using GeoEye-1 Imagery for Multi-Temporal Object-Based Detection of Canegrub Damage in Sugarcane Fields in Queensland, Australia. GIScience & Remote Sensing, 55(2), 285-305.

Lofton, J., Tubana, B. S., Kanke, Y., Teboh, J., Viator, H., & Dalen, M. (2012). Estimating sugarcane yield potential using an in-season determination of normalized difference vegetative index. Sensors, 12, 7529-7547.

Lopes, M. S. & Reynolds, M. P. (2012). Stay-green in spring wheat can be determined by spectral reflectance measurements (normalized difference vegetation index) independently from phenology. Journal of Experimental Botany, 63, 3789-3798.

Marote, M. (2010). Agricultura de Precisión. Ciencia y Tecnología, 10, 151.

Martínez, L. J. (2017). Relationship between crop nutritional status, spectral measurements and Sentinel 2 images. Agronomía Colombiana, 35, 205-215.

Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J. M., Tucker, C. J., & Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution, 20(9), 503-510.

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

Salamí, E., Barrado, C., & Pastor, E. (2014). UAV flight experiments applied to the remote sensing of vegetated areas. Remote Sensing, 6(11), 11051-11081

Saxena, L., & Armstrong, L. (2014). A survey of image processing techniques for agriculture. In: Proceedings of Asian Federation for Information Technology in Agriculture (pp. 401-413). Perth, W.A: Australian Society of Information and Communication Technologies in Agriculture. Retrieved from: https://ro.ecu.edu.au/ecuworkspost2013/854

Torres, A., Gómez, A., & Jiménez, A. (2015). Development of a multispectral system for precision agriculture applications using embedded devices. Sistemas & Telemática, 13(33), 27-44. https://doi.org/10.18046/syt.v13i33.2079

Trotter, T. F., Frazier, P., Trotter, M. G. & Lamb, D. W. (2008). Objective biomass assessment using an active plant sensor (Crop Circle), preliminary experiences on a variety of agricultural landscapes [white paper]. Retrieved from: https://www.researchgate.net/profile/Paul_Frazier2

Vibhute, B. S. & Bodhe, S. K. (2012). Applications of Image Processing in Agriculture: A Survey. International Journal of Computer Applications, 52, 34 - 40.

Virlet, N., Costes, E., Martinez, S., Kelner, J. J., & Regnard, J. L. (2015). Multispectral airborne imagery in the field reveals genetic determinisms of morphological and transpiration traits of an apple tree hybrid population in response to water deficit. Journal of Experimental Botany, 66(18), 5453-5465.

Zhao, Y., Della-Justina, D., Kazama, Y., Rocha, J., Graziano, P., & Camargo, R. (2016). Dynamics modeling for sugar cane sucrose estimation using time series satellite imagery. In: Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII (pp. 99980J). International Society for Optics and Photonics. https://doi.org/10.1117/12.2242490

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Published

2018-10-31

Issue

Vol. 16 No. 47 (2018): October - December, 2018

Section

Original Research

License

This journal is licensed under the terms of the CC BY 4.0 licence (https://creativecommons.org/licenses/by/4.0/legalcode).