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

Abstract

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.

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