Evaluation of coffee plant attributes by field collection and remotely piloted aircraft system images

Keywords: precision farming, remote sensing, vegetation indices


Aim of study: To verify and evaluate the area occupied by coffee plants before and after the manual harvesting of fruits and the difference between such areas; demonstrate the correlation between data on chemical attributes of leaves, yield, vegetation indices, and areas occupied by coffee plants; and estimate yield based on the variable with the best statistical indicator.

Area of study: Bom Jardim Farm in Santo Antônio do Amparo city, Minas Gerais, Brazil.

Material and methods: We studied 52 sampling points composed of four coffee (Coffea arabica L.) plants each. Field data on leaf chemical attributes, yield, and aerial images of flights with the Remote Piloted Aircraft System were obtained over the study area. The images were processed in the Pix4D software, and the analyses were performed in the ArcGIS and Orange Canvas software.

Main results: We verified a reduction in the area occupied by coffee plants due to the action of the harvest; no significant correlations were detected between leaf chemical attributes, yield data, and area occupied by coffee plants; and only the NDVI was adequate for determining a linear equation to estimate yield.

Research highlights: The yield correlation and predicting estimates by applying vegetation indices optimize the time spent on field measurements using the remotely piloted aircraft system. The fall of leaves due to the action of harvesting was evidenced and promotes impacts on the next crop's yield.


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How to Cite
Bento, N. L., Ferraz, G. A. S., Barata, R. A. P., Santana, L. S., Faria, R. O., & Soares, D. V. (2022). Evaluation of coffee plant attributes by field collection and remotely piloted aircraft system images . Spanish Journal of Agricultural Research, 20(3), e0205. https://doi.org/10.5424/sjar/2022203-18808
Agricultural engineering