Processing multispectral imaging captured by drones to evaluate the normalized difference vegetation index of Castillo coffee plantations
2021
Bonnaire Rivera,Lou | Montoya Bonilla,Bibiana | Obando-Vidal,Francisco
Abstract Coffee is an important product in the Colombian economy, contributing mainly to the income of growers in Cauca, especially those seeking to increase profitability through differentiated cultivation processes and value-added crops. The current agronomic management is traditional and at random, limiting the general view of lot condition. Precision agriculture is a tool that makes crop management more reliable by taking into account its agroclimatic characteristics. The present study shows how the plants’ nutritional status can be determined at an early stage using drone-borne multispectral imaging of the land. We obtained an information system to process images through an algorithm that calculates the normalized difference vegetation index (NDVI) in Castillo coffee growing. NDVI values higher than 0.8 were reached. When contrasting the data obtained by the drone with the data recorded on the ground using a leaf spectrometer with a Tukey test (p = 0.05), we found significant differences between the evaluation methods.
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