Abstract
It is important for farmers to know the level of chlorophyll in plants since this depends on the treatment they should give to their crops. There are two common classic methods to get chlorophyll values: from laboratory analysis and electronic devices. Both methods obtain the chlorophyll level of one sample at a time, although they can be destructive. The objective of this research is to develop a system that allows for obtaining the chlorophyll level of plants using multispectral images.
Python programming language and different libraries of that language were used to develop the solution. It was implemented as an image labeling module, a simple linear regression, and a prediction module. The first module was used to create a database that relates the values of the NDVI image with those of chlorophyll, which was then used to obtain a linear regression model for the prediction system to obtain chlorophyll values from the images. The model was trained with 92 images and was obtained a root-mean-square error (RMSE) of 7.27 units CCM (Chlorophyll Content Meter). While the testing was performed using 10 values obtaining a maximum error of 15.5%.
It is concluded that the system is appropriate for chlorophyll contents identification on maize leaves in field tests. However, it can also be adapted for other measurements and crops. The system can be downloaded at [1].
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Saverio, J., Alarcón, A.: NDVI-Checking. https://github.com/JoeSvr95/NDVI-Checking
Saberioon, M.M., et al.: Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale. Int. J. Appl. Earth Obs. Geoinf. 32(1), 35–45 (2014). https://doi.org/10.1016/j.jag.2014.03.018
Mogili, U.R., Deepak, B.B.V.L.: Review on application of drone systems in precision agriculture. Procedia Comput. Sci. 133, 502–509 (2018). https://doi.org/10.1016/j.procs.2018.07.063
Schut, A.G.T., et al.: Assessing yield and fertilizer response in heterogeneous smallholder fields with UAVs and satellites. Field Crops Res. 221, 98–107 (2017). https://doi.org/10.1016/j.fcr.2018.02.018
Cao, S., et al.: Radiometric calibration assessments for UAS-borne multispectral cameras: laboratory and field protocols. ISPRS J. Photogram. Remote Sens. 149, 132–145 (2019). https://doi.org/10.1016/j.isprsjprs.2019.01.016
Ampatzidis, Y., et al.: Citrus rootstock evaluation utilizing UAV-based remote sensing and artificial intelligence. Comput. Electron. Agric. 164, 104900 (2019). https://doi.org/10.1016/j.compag.2019.104900
Gilabert, M., Gonzalez-Piqueras, J., García-Haro, J.: Acerca de los índices de vegetación. Revista de teledetección: Revista de la Asociación Española de Teledetección (8), 1133-0953 (1997)
Solano, F., Di Fazio, S., Modica, G.: A methodology based on GEOBIA and WorldView-3 imagery to derive vegetation indices at tree crown detail in olive orchards. Int. J. Appl. Earth Obs. Geoinf. 83, 101912 (2019). https://doi.org/10.1016/j.jag.2019.101912
Kyratzis, A., et al.: Investigating correlation among NDVI index derived by unmanned aerial vehicle photography and grain yield under late drought stress conditions. Procedia Env. Sci. 29, 225–226 (2015). https://doi.org/10.1016/j.proenv.2015.07.284
Reyes, J., Godoy, A., Realpe, M.: Uso de software de código abierto para fusión de imágenes agrícolas multiespectrales adquiridas con drones. In: 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: Industry, Innovation, and Infrastructure for Sustainable Cities and Communities, 24–26 July 2019, Jamaica (2019)
Liakos, K.G., et al.: Machine learning in agriculture: a review. Sensors (Switz.) 18(8), 1–29 (2018). https://doi.org/10.3390/s18082674
Muñoz-Huerta, R.F., Guevara-Gonzalez, R.G., Contreras-Medina, L.M., Torres-Pacheco, I., Prado-Olivarez, J., Ocampo-Velazquez, R.V.: A review of methods for sensing the nitrogen status in plants: advantages, disadvantages and recent advances. Sensors 8(13), 10823–10843 (2013)
Sainz Rozas, H., Echeverría, H.E.: Relación entre las lecturas del medidor de clorofila (Minolta CCM 200 PLUS 502) en distintos estadios del ciclo del cultivo de maíz y el rendimiento en grano. Revista de la Facultad de Agronomía, p. 103 (1998)
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Saverio, J., Alarcón, A., Paillacho, J., Calderón, F., Realpe, M. (2020). Open Source System for Identification of Maize Leaf Chlorophyll Contents Based on Multispectral Images. In: Botto-Tobar, M., Zambrano Vizuete, M., Torres-Carrión, P., Montes León, S., Pizarro Vásquez, G., Durakovic, B. (eds) Applied Technologies. ICAT 2019. Communications in Computer and Information Science, vol 1194. Springer, Cham. https://doi.org/10.1007/978-3-030-42520-3_45
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DOI: https://doi.org/10.1007/978-3-030-42520-3_45
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