Discrimination of Corn from Monocotyledonous Weeds with Ultraviolet (UV) Induced Fluorescence BERNARD PANNETON,* SERGE GUILLAUME, GUY SAMSON, and JEAN-MICHEL ROGER Horticultural R&D Centre, Agriculture and Agri-Food Canada, St-Jean-sur-Richelieu, Qc, Canada J3B 3E6 (B.P.); Cemagref, UMR ITAP, 34196 Montpellier, France (S.G., J.-M.R.); and De ´partement de chimie-biologie, Universite ´ du Que ´bec a ` Trois-Rivie `res, Trois-Rivie `res, QC, Canada, G9A 5H7 (G.S.) In production agriculture, savings in herbicides can be achieved if weeds can be discriminated from crop, allowing the targeting of weed control to weed-infested areas only. Previous studies demonstrated the potential of ultraviolet (UV) induced fluorescence to discriminate corn from weeds and recently, robust models have been obtained for the discrimination between monocots (including corn) and dicots. Here, we developed a new approach to achieve robust discrimination of monocot weeds from corn. To this end, four corn hybrids (Elite 60T05, Monsanto DKC 26-78, Pioneer 39Y85 (RR), and Syngenta N2555 (Bt, LL)) and four monocot weeds (Digitaria ischaemum (Schreb.) I, Echinochloa crus-galli (L.) Beauv., Panicum capillare (L.), and Setaria glauca (L.) Beauv.) were grown either in a greenhouse or in a growth cabinet and UV (327 nm) induced fluorescence spectra (400 to 755 nm) were measured under controlled or uncontrolled ambient light intensity and temperature. This resulted in three contrasting data sets suitable for testing the robustness of discrimination models. In the blue-green region (400 to 550 nm), the shape of the spectra did not contain any useful information for discrimination. Therefore, the integral of the blue-green region (415 to 455 nm) was used as a normalizing factor for the red fluorescence intensity (670 to 755 nm). The shape of the normalized red fluorescence spectra did not contribute to the discrimination and in the end, only the integral of the normalized red fluorescence intensity was left as a single discriminant variable. Applying a threshold on this variable minimizing the classification error resulted in calibration errors ranging from 14.2% to 15.8%, but this threshold varied largely between data sets. Therefore, to achieve robustness, a model calibration scheme was developed based on the collection of a calibration data set from 75 corn plants. From this set, a new threshold can be estimated as the 85% quantile on the cumulative frequency curve of the integral of the normalized red fluorescence. With this approach the classification error was nearly constant (16.0% to 18.5%), thereby indicating the potential of UV-induced fluorescence to reliably discrim- inate corn from monocot weeds. Index Headings: Fluorescence; Weeds; Corn; Monocots; Discrimination; Model robustness; Site-specific weed control. INTRODUCTION Sensor-based automatic discrimination of weeds from a crop could be of great benefit for production agriculture. With such a sensing system, field areas infested by weeds can be identified and control measures can be applied only to these areas. In all cases, limiting application of herbicides to a fraction of the cultivated fields results in time and cost savings for the farmer and lowered environmental impacts. Previous studies indicated that UV-induced plant fluores- cence can be used to discriminate plant groups. 1 Under UV excitation, plants can emit a blue-green fluorescence (BGF) with a wide peak around 440 nm and also the chlorophyll fluorescence (ChlF), characterized by its two peaks in the red and far-red regions (685 and 735 nm) of the spectrum. The characteristics of the fluorescence emission spectra depend on different leaf properties, notably the concentrations of ferulic acid (the main emitter of BGF) and chlorophylls, and also the presence of non-fluorescent UV-absorbing compounds in leaf epidermis that decrease the UV excitation of chlorophylls in the leaf mesophyll. 2,3 For plants grown under similar conditions and having similar developmental stage, these leaf properties vary according to plant species. Therefore, the characteristics of plant emission fluorescence spectra represent distinct signatures that may be used for plant discrimination. In the context of plant discrimination, several factors can affect the fluorescence signal and these were reviewed in a previous paper. 4 The potential of UV-induced plant fluorescence spectra for discriminating between plant groups or plant species has been evaluated. Early work 1 demonstrated that dicotyledonous plants (dicots) can be distinguished from herbaceous mono- cotyledonous plants (monocots) based on the ratio F440 2 /F685 (F440 is the fluorescence intensity measured at 440 nm). The content of ferulic acid in monocotyledonous plants, and particularly in species of the Poaceae family, is several times higher than in dicotyledonous plants. In consequence, BGF emission is more intense in monocot leaves than in dicot leaves, resulting in higher F440/F685 ratio. 5 Also, the ratio F685/F735 was used to discriminate four plant species: peas, barley, clover, and Shepherd’s purse. 6 But since this ratio is mainly influenced by leaf chlorophyll-a concentrations, 7 the robustness of this fluorescence ratio for plant discrimination may be limited. Recently, Panneton and co-workers specifically tested the potential of UV-induced fluorescence spectra to discriminate weeds from crop by using a larger number of species that are relevant to a crop-weed field environment. They developed a model calibrated for four corn hybrids, four monocot weeds, and four dicot weeds and obtained a cross-validation error of 8.2%. 8 It is noteworthy that this low calibration error was obtained with a data set composed of fluorescence spectra measured on leaves from plants of different ages (10 to 30 days after emergence) and measured from different positions (leaf base and leaf apex), two factors known to significantly affect intrinsic leaf properties and thereby plant fluorescence emission spectrum. 9 On the same group of plant species, it was shown that by using proper normalization 10 robust discrimination between monocots (including corn) and dicots can be performed with a classification error in prediction between 1.5% and 5.2%. 4 This was achieved using the average normalized signal in two bands: F400–425 and F425–490. Regarding the discrimination of monocot weeds from corn, Received 16 August 2010; accepted 4 October 2010. * Author to whom correspondence should be sent. E-mail: Bernard. panneton@agr.gc.ca. DOI: 10.1366/10-06100 10 Volume 65, Number 1, 2011 APPLIED SPECTROSCOPY 0003-7028/11/6501-0010$2.00/0 Ó 2011 Society for Applied Spectroscopy