Hyperspectral characterization and estimation models for agronomic parameters of coffee cultivars after pruning

Abstract

Spectral characterization of coffee cultivars after pruning may help predict the crop’s diverse phytotechnical behaviors. Therefore, this study aimed to differentiate coffee genotypes based on leaf reflectance following pruning and to develop models capable of estimating the agronomic parameters of vegetative growth. This study included eight coffee cultivars. The biochemical and leaf structure parameters were quantified for the spectral characterization of each genotype and adopted as the basis for the application of vegetation indices and to generate models that can estimate agronomic parameters after pruning. The cultivar Topázio MG-1190 proved to be the most efficient at absorbing electromagnetic radiation in the blue (220–510 nm), green (520–590 nm), and red (690–730 nm) spectral ranges among all other cultivars after pruning. The regression models could estimate the following vegetative growth parameters of the coffee cultivars: height using the normalized difference water index (root mean square error (RMSE) = 5.78%), crown diameter and plagiotropic branch length using the enhanced vegetation index (RMSE = 10.21% and 12.69%, respectively), the number of nodes using the chlorophyll b index (RMSE = 14.47%), chlorophyll a content using the photochemical reflectance index (RMSE = 2.39%), and leaf water potential using the leaf water vegetation index-1 (40.72%). The hyperspectral characterization of coffee cultivars with respect to the phytotechnical development of the genotypes after pruning was successfully accomplished. This tool can assist coffee growers to enhance their competitiveness and productivity through the development of management strategies such as the early detection of plant growth issues.

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