Using unmanned aerial vehicle and machine learning algorithm to monitor leaf nitrogen in coffee




Nitrogen is an essential element for coffee production. However, when fertilization do not consider the spatial variability of the agricultural parameters, it can generate economic losses, such as low productivity, and environmental impacts, such as pollution of air and eutrophication of water bodies. Thus, the monitoring of the nitrogen during different phases of the production is a key factor for the fertilization management, and remote sensing based on unmanned aerial vehicles imagery has been evaluated for this task. Thus, this work aimed to evaluate the potential of visible vegetation indices obtained from such images to monitor the spatial variability of the leaf nitrogen content in a coffee farm located in Divisa Nova Municipality, Minas Gerais. Therefore, we performed a leaf analysis using the Kjeldahl method to determine leaf nitrogen, and to process the images and produce the vegetation indices, we use Geographic Information Systems and photogrammetry software. As analyze methods, we used the Random Forest classification algorithm as an estimator and performed ordinary kriging to visualize the spatial variability as nitrogen content. Lastly, the Pearson correlation coefficient was employed to evaluate the relationship between the variables. However, the Random Forest models were unable to explain nitrogen variability, and we did not find any significant correlations between the tested vegetation indices and nitrogen content. Therefore, it is indicated the replication of the study in the vegetative phase of the coffee plants, with the establishment of different fertilization treatments, as well as the use of multispectral sensors and radiometric calibration techniques.

Keys words: Vegetation indices; RGB; machine learning; Coffea arabica.


ALVARES, C. A. et al. Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, 22(6):711-728, 2013.

ARROYO, J.; GUIJARRO, M.; PAJARES, G. An instancebased learning approach for thresholding in crop images under different outdoor conditions. Computers and Electronics in Agriculture, 127:669-679, 2016.

BALLESTER, C. et al. Assessment of In-Season Cotton Nitrogen Status and Lint Yield Prediction from Unmanned Aerial System Imagery. Remote Sensing, 9(11):1149, 2017.

BENIAICHI, A. et al. Determination of vegetation cover index under different soil management systems of cover plants by using an unmanned aerial vehicle with an onboard digital photographic camera. Semina: Ciências Agrárias, 40(1):49-66, 2019.

BIAU, G. Analysis of a Random Forests model. Journal of Machine Learning Research, 13:1063-1095, 2012.

CATUREGLI, L. et al. Normalized Difference Vegetation Index versus Dark Green Colour Index to estimate nitrogen status on bermudagrass hybrid and tall fescue. International Journal of Remote Sensing, p. 1-16, 2019.

CECHIM JÚNIOR, C.; JOHANN, J. A.; ANTUNES, J. F. G. Mapping of sugarcane crop area in the Paraná State using Landsat/TM/OLI and IRS/LISS-3 images. Revista Brasileira de Engenharia Agrícola e Ambiental, 21(6):427-432, 2017.

COHEN, J. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1):37-46, 1960.

CUNHA, J. P. A. R.; SIRQUEIRA NETO, M. A.; HURTADO, S. M. C. Estimating vegetation volume of coffee crops using images from unmanned aerial vehicles. Engenharia Agrícola, 39:41-47, 2019.

ESCALANTE, H. J. et al. Barley yield and fertilization analysis from UAV imagery: A deep learning approach. International Journal of Remote Sensing, 40(7):2493-2516, 2019. Coffee Science, 15:e151736, 2020

ENVIRONMENTAL SYSTEMS RESEARCH INSTITUTE - INC. – ESRI. ARCGIS Professional GIS for the desktop version 10.5. Redlands, California, 2017. Available in: <>. Access in: January 10, 2019.

FERRAZ, G. A. S. et al. Agricultura de precisão no estudo de atributos químicos do solo e da produtividade de lavoura cafeeira. Coffee Science, 7(1):56-67, 2012.

GAMER, M. et al. irr: various Coefficients of Interrater Reliability and Agreement. R package version 0.84.1. 2019 Available in: <>. Access in: April 30, 2020.

GARZA, B. N. et al. Quantifying Citrus Tree Health Using True Color UAV Images. Remote Sensing, 12(1):170, 2020.

HUNT JUNIOR, E. R. et al. Monitoring nitrogen status of potatoes using small unmanned aerial vehicles. Precision Agriculture, 19:314-333, 2018.

GITELSON, A. A. et al. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80:76-87, 2002.

GUIMARÃES, P. T. G. et al. Cafeeiro. In: RIBEIRO, A. C.; GUIMARÃES, P. T. G.; ALVARES, V. H. Recomendações para o uso de corretivos e fertilizantes em Minas Gerais: 5ª aproximação. Viçosa: UFV, 1999. p.289-302.

INSTITUTO NACIONAL DE METEOROLOGIA – INMET. Estações onvencionais. 2020. Available in: <>. Access in: January 27, 2020.

KATAOKA, T. et al. Crop growth estimation system using machine vision. Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2(1):1079-1083, 2003.

LAU, M. K. DTK: Dunnett-Tukey-Kramer Pairwise Multiple Comparison Test Adjusted for Unequal Variances and Unequal Sample Sizes. R package version 3.5. 2015. Available in: <>. Access in: April 30, 2020.

LIAW, A.; WIENER, M. Classification and Regression by randomForest. R News, 2(3):18-22, 2002.

LOUHAICHI, M. et al. Spatially Located Platform and Aerial Photography for Documentation of Grazing Impacts on Wheat. Geocarto International, 16(1):65-70, 2001.

MAES, W. H.; STEPPE, K. Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends in Plant Science, 24(2):152-164, 2019.

NÄSI, R. et al. Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features. Remote Sensing, 10(7):1082, 2018.

OSCO, L. P. et al. Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery. Remote Sensing, 11(24),2925, 2019.

PONZONI, F. J.; SHIMABUKURO, Y. E.; KUPLICH, T. M. Sensoriamento Remoto da Vegetação. 2. ed. São Paulo: Oficina de Textos, 2012. 176p.

PÔRTO, M. L. et al. Índice SPAD para o diagnóstico do estado de nitrogênio na cultura da abobrinha. Horticultura Brasileira, 29(3):311-315, 2011.

QGIS Development Team, 2020. QGIS Geographic Information System. Open Source Geospatial Foundation Project. Available in: http:/>. Access in March 30, 2020.

R CORE TEAM. R: A language and environment for statistical computing. R Foundation for Statistical Computing, 2019.

REIS, A. R. et al. Photosynthesis, Chlorophylls, and SPAD Readings in Coffee Leaves in Relation to Nitrogen Supply. Communications in Soil Science and Plant Analysis, 40:1512-1528, 2009.

SCHIRRMANN, M. et al. Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery. Remote Sensing, 8(9):706, 2016.

TAIZ, L. et al. Fisiologia e Desenvolvimento Vegetal. 6. ed. Porto Alegre: Artmed, 2017. 858p.

TUCKER, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8:127-150, 1979.

VEGA, F. A. et al. Multi-temporal imaging using na unmanned aerial vehicle for monitoring a sunflower crop. Biosystems Engineering, 132:19-27, 2015.

WOEBBECKE, D. M. et al. Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE, 38(1):259-269, 1995.

ZHANG, M. et al. Estimation of maize yield and effects of variable-rate nitrogen application using UAV-based RGB imagery. Biosystems Engineering, 189:24-35, 2020



How to Cite

MINCATO, R. L.; CRISTO PARREIRAS, T. .; HENRIQUE EXPEDITO LENSE, G. .; SANTOS MOREIRA, R. .; BRANDÃO SANTANA, D. . Using unmanned aerial vehicle and machine learning algorithm to monitor leaf nitrogen in coffee. Coffee Science - ISSN 1984-3909, v. 15, p. e151736, 6 Jul. 2020.