Discrimination of tomato plants under different irrigation regimes: analysis of hyperspectral sensor data

M. Rinaldi, A. Castrignanò, D. De Benedetto, D. Sollitto, S. Ruggieri, P. Garofalo, F. Santoro, B. Figorito, S. Gualano and R. Tamborrino

Environmetrics, 2015, vol. 26, issue 2, 77-88

Abstract: The development and implementation of both economically and environmentally sustainable precision crop management systems can be greatly enhanced through the use of hyperspectral sensing. In this study, the potential of narrow‐waveband hyperspectral observations for the discrimination of water‐stressed tomato plants (Solanum lycopersicum L.) was investigated in a field experiment conducted in southern Italy. The tomato crop was grown in a 1.8‐ha test field that was split into two plots with different irrigation treatments: optimal and deficit water supplies, with the deficit supply using half of the water of the optimal supply in the second half of the crop growing cycle. Hyperspectral measurements were taken with a field spectroradiometer. To reduce the number of variables, principal component analysis was applied to each of six wavelength band sub‐intervals across the whole wavelength interval from 400 to 1000 nm. The retained principal components were then submitted to canonical discriminant analysis. Finally, the principal components and the canonical component were interpolated using multivariate and univariate geostatistical techniques, respectively, and then mapped. The two irrigation treatments produced different plant biomass and leaf area indices, which were higher under optimal than deficit water conditions, as was the plant water potential. These data show that the correlation between the individual bands varied during the crop cycle, so it was not feasible to choose a specific band to discriminate between the water treatments. However, we show that only a combination of all of the bands that use the full spectral information with differential weighting leads to clear discrimination of the two differently irrigated areas, with a mean accuracy of 75% to 77%. The processing of hyperspectral reflectance data using canonical discriminant analysis can thus provide valuable information for the agricultural producer for the identification of within‐field areas of plant stress, so as to implement site‐specific irrigation strategies. Copyright © 2014 John Wiley & Sons, Ltd.

Date: 2015
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