Toward the discrimination of manganese, zinc, copper, and iron deficiency in 'Bragg' soybean using spectral detection methods

Early visual symptoms of Mn, Zn, Cu, and Fe deficiency are often difficult to interpret and incorrect diagnoses are common. Reflectance and fluorescence measures may be useful for early and more reliable detection of Mn, Zn, Cu, and Fe deficiencies, but only if one or more spectral measures change uniquely with each deficiency. A discriminant analysis was performed to determine whether selected fluorescence and reflectance measures could be used to discriminate effectively marginal Mn, Zn, Cu, Fe deficiencies, and nutrient-adequate soybean [Glycine mar (L.) Merr. cv. Bragg] leaves from plants grown in solution culture. Predictors were yellowness index (YI), a new measure sensitive to chlorosis; normalized difference vegetation index (NDVI); the ratio of minimal fluorescence (F-o) to variable fluorescence (F-v), F-o/F-v; and the ratio of minimal fluorescence to the fluorescence yield after 5 min of illumination (F-5min), F-o/F-5min. Manganese, Zn, Cu, and Fe deficiencies were correctly identified 62, 40, 92, and 30% of the time, respectively, as estimated by cross-validation. Controls were identified correctly 77% of the time. One-third to one-half of the leaves identified as nutrient deficient by tissue analysis did not exhibit visual symptoms. Lack of a spectral measure sensitive specifically to Zn and Fe deficiency contributed to the low identification rates for Zn and Fe deficiencies. While the development of spectral measures sensitive to Zn and Fe deficiencies is required for further development of this rule, discriminant analysis is a suitable method for the development of classification rules for identifying marginal stresses.