Estimating Nitrogen and Chlorophyll Content in Corn Using Spectral Vegetation Indices Derived From UAV Multispectral Imagery

Document Type : Original Research

Authors

1 Agricultural Engineering Research Institute. Agricultural Research, Education and Extension Organization (AREEO). Karaj. Iran.

2 Varamin-Pishva Branch, Islamic Azad University, Varamin, Iran.

3 Novin Niro Shahbaz Company.

Abstract

Remote sensing is a unique and cost-effective tool that provides information about the nitrogen status of plants in a non-destructive way. The objective of this study is to evaluate the effectiveness of aerial multispectral imagery captured by UAV for estimating corn nitrogen (N) and chlorophyll (Chl) content at different growth stages. The study used a fully randomized experimental design with four treatments of nitrogen fertilizer (0, 50%, 100%, and 150%). Ten plants were randomly selected in each plot at the phenological stages of 8 leaves (V8) and tasseling growth stages (VT) for sampling. Leaf samples were taken to measure total nitrogen (N) and chlorophyll (Chl) content. Mathematical models were created using vegetation indices extracted from aerial multispectral images to estimate the amount of nitrogen and chlorophyll. The models were evaluated using the leave-one-out cross-validation method. The results showed that there is a significant positive relationship between the leaf dry weight (LDW), the Chl and N content with the amount of nitrogen fertilizer used. So, the results indicated that the REIP index is suitable for estimating chlorophyll content in both the V8 (R2 of 0.997) and VT (R2 of 0.980) growth stages. Additionally, the REIP index was found to be an appropriate index for estimating N content in both growth stages (R2 of 0.980). It can be concluded that aerial multispectral remote sensing technology is a reliable method for estimating corn nitrogen and chlorophyll content.

Keywords

References

 Bagheri, N. (2017). Development of a high-resolution aerial remote-sensing system for precision agriculture. International journal of remote sensing, 38(8-10), 2053-2065. https://doi.org/https://doi.org/10.1080/01431161.2016.1225182
Bagheri, N., Ahmadi, H., Alavipanah, S. K., & Omid, M. (2013). Multispectral remote sensing for site-specific nitrogen fertilizer management. Pesquisa Agropecuária Brasileira, 48, 1394-1401. https://doi.org/https://doi.org/10.1590/S0100-204X2013001000011
Bagheri, N., Jahangirlou, M. R., & Aghdam, M. J. (2022). Predicting protein content of silage maize using remotely sensed multispectral imagery and proximal leaf sensing. Experimental Agriculture, 58, e41. https://doi.org/https://doi.org/10.1017/S0014479722000308
Ban, S., Liu, W., Tian, M., Wang, Q., Yuan, T., Chang, Q., & Li, L. (2022). Rice leaf chlorophyll content estimation using UAV-based spectral images in different regions. Agronomy, 12(11), 2832. https://doi.org/https://doi.org/10.3390/agronomy12112832
Barzin, R., Kamangir, H., & Bora, G. C. (2021). Comparison of machine learning methods for leaf nitrogen estimation in corn using multispectral UAV images. Transactions of the ASABE, 64(6), 2089-2101. https://doi.org/https://doi.org/10.13031/trans.14305
Cao, Q., Miao, Y., Feng, G., Gao, X., Li, F., Liu, B., . . . Khosla, R. (2015). Active canopy sensing of winter wheat nitrogen status: An evaluation of two sensor systems. Computers and Electronics in Agriculture, 112, 54-67. https://doi.org/https://doi.org/10.1016/j.compag.2014.08.012
Chen, X., Li, F., Shi, B., & Chang, Q. (2023). Estimation of Winter Wheat Plant Nitrogen Concentration from UAV Hyperspectral Remote Sensing Combined with Machine Learning Methods. Remote Sensing, 15(11), 2831. https://doi.org/https://doi.org/10.3390/rs15112831
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. https://doi.org/https://doi.org/10.1016/j.isprsjprs.2014.02.013
Corti, M., Cavalli, D., Cabassi, G., Vigoni, A., Degano, L., & Marino Gallina, P. (2019). Application of a low-cost camera on a UAV to estimate maize nitrogen-related variables. Precision Agriculture, 20, 675-696. https://doi.org/https://doi.org/10.1007/s11119-018-9609-y
Daughtry, C. S., Walthall, C., Kim, M., De Colstoun, E. B., & McMurtrey Iii, J. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote sensing of Environment, 74(2), 229-239. https://doi.org/https://doi.org/10.1016/S0034-4257(00)00113-9
Elvanidi, A., Katsoulas, N., Augoustaki, D., Loulou, I., & Kittas, C. (2018). Crop reflectance measurements for nitrogen deficiency detection in a soilless tomato crop. Biosystems engineering, 176, 1-11. https://doi.org/https://doi.org/10.1016/j.biosystemseng.2018.09.019
Escalante, H., Rodríguez-Sánchez, S., Jiménez-Lizárraga, M., Morales-Reyes, A., De La Calleja, J., & Vazquez, R. (2019). Barley yield and fertilization analysis from UAV imagery: a deep learning approach. International journal of remote sensing, 40(7), 2493-2516. https://doi.org/https://doi.org/10.1080/01431161.2019.1577571
Gabriel, J. L., Zarco-Tejada, P. J., López-Herrera, P. J., Pérez-Martín, E., Alonso-Ayuso, M., & Quemada, M. (2017). Airborne and ground level sensors for monitoring nitrogen status in a maize crop. Biosystems engineering, 160, 124-133. https://doi.org/https://doi.org/10.1016/j.biosystemseng.2017.06.003
Geipel, J., Link, J., Wirwahn, J. A., & Claupein, W. (2016). A programmable aerial multispectral camera system for in-season crop biomass and nitrogen content estimation. Agriculture, 6(1), 4. https://doi.org/https://doi.org/10.3390/agriculture6010004
Gitelson, A. A., Merzlyak, M. N., & Chivkunova, O. B. (2001). Optical properties and nondestructive estimation of anthocyanin content in plant leaves¶. Photochemistry and photobiology, 74(1), 38-45. https://doi.org/https://doi.org/10.1562/0031-8655(2001)074<0038:OPANEO>2.0.CO;2
Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., & Strachan, I. B. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote sensing of Environment, 90(3), 337-352. https://doi.org/https://doi.org/10.1016/j.rse.2003.12.013
Huang, S., Miao, Y., Zhao, G., Yuan, F., Ma, X., Tan, C., . . . Rascher, U. (2015). Satellite remote sensing-based in-season diagnosis of rice nitrogen status in Northeast China. Remote Sensing, 7(8), 10646-10667. https://doi.org/https://doi.org/10.3390/rs70810646
Huang, Y., Thomson, S. J., Hoffmann, W. C., Lan, Y., & Fritz, B. K. (2013). Development and prospect of unmanned aerial vehicle technologies for agricultural production management. International Journal of Agricultural and Biological Engineering, 6(3), 1-10. https://doi.org/https://doi.org/10.3965/j.ijabe.20130603.00
Jaberi-Aghdam, M., Momayezi, M. R., Bagheri, N., Azizi, P., & Nasri, M. (2024). Nitrogen assessment by remote sensing and multispectral imagery in maize (Zea mays L.). Journal of Crop Science and Biotechnology, 27(1), 31-41.
Krienke, B., Ferguson, R. B., Schlemmer, M., Holland, K., Marx, D., & Eskridge, K. (2017). Using an unmanned aerial vehicle to evaluate nitrogen variability and height effect with an active crop canopy sensor. Precision Agriculture, 18, 900-915. https://doi.org/https://doi.org/10.1007/s11119-017-9534-5
Lebourgeois, V., Bégué, A., Labbé, S., Houles, M., & Martiné, J.-F. (2012). A light-weight multi-spectral aerial imaging system for nitrogen crop monitoring. Precision Agriculture, 13, 525-541. https://doi.org/https://doi.org/10.1007/s11119-012-9262-9
Lebourgeois, V., Bégué, A., Labbé, S., Mallavan, B., Prévot, L., & Roux, B. (2008). Can commercial digital cameras be used as multispectral sensors? A crop monitoring test. Sensors, 8(11), 7300-7322.
Li, J., Zhang, F., Qian, X., Zhu, Y., & Shen, G. (2015). Quantification of rice canopy nitrogen balance index with digital imagery from unmanned aerial vehicle. Remote Sensing Letters, 6(3), 183-189. https://doi.org/https://doi.org/10.1080/2150704X.2015.1021934
Lin, F. F., Deng, J. S., Shi, Y. Y., Chen, L. S., & Wang, K. (2010). Investigation of SPAD meter-based indices for estimating rice nitrogen status. Computers and Electronics in Agriculture, 71, S60-S65. https://doi.org/https://doi.org/10.1016/j.compag.2009.09.006
Liu, S., Li, L., Gao, W., Zhang, Y., Liu, Y., Wang, S., & Lu, J. (2018). Diagnosis of nitrogen status in winter oilseed rape (Brassica napus L.) using in-situ hyperspectral data and unmanned aerial vehicle (UAV) multispectral images. Computers and Electronics in Agriculture, 151, 185-195.
Maresma, Á., Ariza, M., Martínez, E., Lloveras, J., & Martínez-Casasnovas, J. A. (2016). Analysis of vegetation indices to determine nitrogen application and yield prediction in maize (Zea mays L.) from a standard UAV service. Remote Sensing, 8(12), 973. https://doi.org/https://doi.org/10.3390/rs8120973
Miao, Y., Mulla, D. J., Randall, G. W., Vetsch, J. A., & Vintila, R. (2009). Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn. Precision Agriculture, 10, 45-62. https://doi.org/https://doi.org/10.1007/s11119-008-9091-z
Mistele, B., & Schmidhalter, U. (2008). Spectral measurements of the total aerial N and biomass dry weight in maize using a quadrilateral-view optic. Field Crops Research, 106(1), 94-103. https://doi.org/https://doi.org/10.1016/j.fcr.2007.11.002
Narmilan, A., Gonzalez, F., Salgadoe, A. S. A., Kumarasiri, U. W. L. M., Weerasinghe, H. A. S., & Kulasekara, B. R. (2022). Predicting canopy chlorophyll content in sugarcane crops using machine learning algorithms and spectral vegetation indices derived from UAV multispectral imagery. Remote Sensing, 14(5), 1140. https://doi.org/https://doi.org/10.3390/rs14051140
Noh, H., & Zhang, Q. (2012). Shadow effect on multi-spectral image for detection of nitrogen deficiency in corn. Computers and Electronics in Agriculture, 83, 52-57. https://doi.org/https://doi.org/10.1016/j.compag.2012.01.014
Pádua, L., Vanko, J., Hruška, J., Adão, T., Sousa, J. J., Peres, E., & Morais, R. (2017). UAS, sensors, and data processing in agroforestry: A review towards practical applications. International journal of remote sensing, 38(8-10), 2349-2391. https://doi.org/https://doi.org/10.1080/01431161.2017.1297548
Reyniers, M., & Vrindts, E. (2006). Measuring wheat nitrogen status from space and ground‐based platform. International journal of remote sensing, 27(3), 549-567. https://doi.org/https://doi.org/10.1080/01431160500117907
Santana, D. C., Cotrim, M. F., Flores, M. S., Baio, F. H. R., Shiratsuchi, L. S., da Silva Junior, C. A., . . . Teodoro, P. E. (2021). UAV-based multispectral sensor to measure variations in corn as a function of nitrogen topdressing. Remote Sensing Applications: Society and Environment, 23, 100534. https://doi.org/https://doi.org/10.1016/j.rsase.2021.100534
Shanahan, J., Kitchen, N., Raun, W., & Schepers, J. S. (2008). Responsive in-season nitrogen management for cereals. Computers and Electronics in Agriculture, 61(1), 51-62. https://doi.org/https://doi.org/10.1016/j.compag.2007.06.006
Tripicchio, P., Satler, M., Dabisias, G., Ruffaldi, E., & Avizzano, C. A. (2015). Towards smart farming and sustainable agriculture with drones. 2015 international conference on intelligent environments,
Valente, J., Sanz, D., Barrientos, A., Del Cerro, J., Ribeiro, Á., & Rossi, C. (2011). An air-ground wireless sensor network for crop monitoring. Sensors, 11(6), 6088-6108. https://doi.org/https://doi.org/10.3390/s110606088
Vleugels, T., Rijckaert, G., & Gislum, R. (2017). Seed yield response to N fertilization and potential of proximal sensing in Italian ryegrass seed crops. Field Crops Research, 211, 37-47. https://doi.org/https://doi.org/10.1016/j.fcr.2017.06.018
Wang, H., Mortensen, A. K., Mao, P., Boelt, B., & Gislum, R. (2019). Estimating the nitrogen nutrition index in grass seed crops using a UAV-mounted multispectral camera. International journal of remote sensing, 40(7), 2467-2482. https://doi.org/https://doi.org/10.1080/01431161.2019.1569783
Xia, T., Miao, Y., Wu, D., Shao, H., Khosla, R., & Mi, G. (2016). Active optical sensing of spring maize for in-season diagnosis of nitrogen status based on nitrogen nutrition index. Remote Sensing, 8(7), 605. https://doi.org/https://doi.org/10.3390/rs8070605
Xu, S., Xu, X., Zhu, Q., Meng, Y., Yang, G., Feng, H., . . . Wang, B. (2023). Monitoring leaf nitrogen content in rice based on information fusion of multi-sensor imagery from UAV. Precision Agriculture, 24(6), 2327-2349. https://doi.org/https://doi.org/10.1007/s11119-023-10042-8
Xu, X., Fan, L., Li, Z., Meng, Y., Feng, H., Yang, H., & Xu, B. (2021). Estimating leaf nitrogen content in corn based on information fusion of multiple-sensor imagery from UAV. Remote Sensing, 13(3), 340. https://doi.org/https://doi.org/10.3390/rs13030340
Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 13, 693-712. https://doi.org/https://doi.org/10.1007/s11119-012-9274-5
 
Biomechanism and Bioenergy Research
Volume 3, Issue 1
June 2024
Pages 81-93

Files

History

  • Receive Date: 14 April 2024
  • Revise Date: 08 May 2024
  • Accept Date: 26 May 2024

Share

How to cite

Statistics