Discrimination of corn, grasses and dicot weeds by their UV-induced fluorescence spectral signature

Abstract

Real-time spot spraying of weed patches requires the development of sensors for the automatic detection of weeds within a crop. In this context, the potential of UVinduced fluorescence of green plants for corn-weed discrimination was evaluated. A total of 1 440 spectral signatures of fluorescence were recorded in a greenhouse from three plant groups (four corn hybrids, four dicotyledonous weed species and four monocotyledonous weed species) grown in a growth chamber. With multi-variate analysis, the full information contained in each spectrum was first reduced to the scores calculated from five principal components. Then, a linear discriminant analysis was applied on these scores to classify spectra on a species/hybrids basis and, subsequently, the resulting classes were aggregated according to the three plant groups. This two-step process minimized the error generated by heterogeneous groups such as dicotyledonous weeds. The output of this classification shows the significant potential of UV-induced fluorescence for plant group discrimination as the success rate reached 91.8%. No error was observed between corn and dicot weeds and most of the errors between corn and grasses came from confusion between the hybrid Pioneer 39Y85 and Setaria glauca L. (Beauv.). Analysis also determined that the position of the fluorescence sensor on the leaf and the plant age had negligible effects on the efficiency of fluorescence to discriminate plant groups. The factors to consider for transferring the results about UV-induced fluoro-sensing from laboratory to the field are discussed.

Automatic identification of crop and weed species with chlorophyll fluorescence induction curves

“…The results obtained in classifying plants to crop and weed species in this study are comparable to results obtained by using either leaf forms (Aitkenhead et al 2003;Persson and Å strand 2008), multispectral imaging (Vioix et al 2002;Wang et al 2007;Zhang et al 2009) or fluorescence spectra (Longchamps et al 2010). The 95% recognition accuracy of crop/weed classification (Table 4) of 1 s fluorescence curves measured in natural illumination conditions after only 1.2 s of shade indicates that the fluorescence fingerprinting method has potential in field conditions.…”

“…In reflectance measurements, the excess green color index (Meyer et al 1998;Rasmussen et al 2007;Meyer and Neto 2008) can be used to distinguish plants from ground. The fluorescence emission spectrum, obtained under excitation with ultraviolet light, has shown to be a promising method for discrimination of monocots and dicots (Longchamps et al 2010). …”

Using Optical Sensors to Identify Water Deprivation, Nitrogen Shortage, Weed Presence and Fungal Infection in Wheat

“…Different weed species may be discriminated based on their spectral reflectance curves [21,22]. Based on UV-induced fluorescence, Longchamps et al [23] successfully classified maize and weeds into three plant groups (four maize hybrids, four dicotyledonous weed species and four monocotyledonous weed species). Tyystjärvi et al [24] used chlorophyll fluorescence to classify six weed species, maize and barley into weeds and crop with a high correct classification rate (86.7%-96.1%).…”

Effect of planter and tractor wheels on row and inter-row weed populations

“…As scouting for, and counting, weeds is time consuming (Timmermann et al, 2003;Wyse-Pester et al, 2002) some form of machine-based weed detection is necessary (Gerhards and Oebel, 2006). Weed detection can be performed using remote (Armstrong et al, 2007), optoelectronic (Blackshaw et al, 1998), UV-induced fluorescence (Longchamps et al, 2010) or digital imaging sensors (BurgosArtizzu et al, 2009;Panneton and Brouillard, 2009;Schuster et al, 2007). All of these techniques require weed/crop discrimination.…”