Classification of the Nutritional Status of Peach Trees Using Indexes from Hyperspectral Images

Abstract

This paper presents a procedure for the comparison of two technologies developed to classify peach trees according to their nutritional status. The first technology uses the leaf SPAD-502 meter value to characterize tree classes as indicated by agronomist experts: sound, intermediate, and strong chlorosis trees. It is used as a reference for the second technology, which uses a combination of two multispectral indexes computed from reflectance hyperspectral images. Specifically, R_NDVI = (R800 − R670)/(R800 + R670) and HyperSPAD = (R940/R650) are computed for each leaf pixel. An automated methodology is proposed that sets two optical thresholds (three hyperspectral categories) in view of the outliers according to a normal distribution, together with an iterative optimization of the bounding that determines the best assignment of trees to one of the three SPAD_502 levels of nutritional status, as required for practical agronomical purposes such as fertilization. The Chi 2 distribution is used to confirm the similarity of both nutritional classifications. These results encourage the use of on-board multispectral cameras to monitor the nutritional status of trees and to establish a more efficient fertilization strategy where inputs are applied according to individual status, with the consequent reduction in losses of fertilizers such as nitrogen to the atmosphere, soil, and water resulting from over-application.

Keywords

• precision agriculture
• site-specific fertilization
• optical properties
• chlorosis
• non-destructive on-board measurements