Estimating above-ground biomass in the context of fertilization management requires the monitoring of crops at early stages. Conventional remote sensing techniques make use of vegetation indices such as the normalized difference vegetation index (NDVI), but they do not exploit the high spatial resolution (ground sampling distance < 5 mm) now achievable with the introduction of unmanned aerial vehicles (UAVs) in agriculture. The aim of this study was to compare image mosaics to single images for the estimation of corn biomass and the influence of viewing angles in this estimation. Nadir imagery was captured by a high spatial resolution camera mounted on a UAV to generate orthomosaics of corn plots at different growth stages (from V2 to V7). Nadir and oblique images (30° and 45° with respect to the vertical) were also acquired from a zip line platform and processed as single images. Image segmentation was performed using the difference color index Excess Green-Excess Red, allowing for the discrimination between vegetation and background pixels. The apparent surface area of plants was then extracted and compared to biomass measured in situ. An asymptotic total least squares regression was performed and showed a strong relationship between the apparent surface area of plants and both dry and fresh biomass. Mosaics tended to underestimate the apparent surface area in comparison to single images because of radiometric degradation. It is therefore conceivable to process only single images instead of investing time and effort in acquiring and processing data for orthomosaic generation. When comparing oblique photography, an angle of 30° yielded the best results in estimating corn biomass, with a low residual standard error of orthogonal distance (RSEOD = 0.031 for fresh biomass, RSEOD = 0.034 for dry biomass). Since oblique imagery provides more flexibility in data acquisition with fewer constraints on logistics, this approach might be an efficient way to monitor crop biomass at early stages.

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 discriminate corn from monocot weeds.

Precision weeding by spot spraying in real time requires sensors to discriminate between weeds and crop without contact. Among the optical based solutions, the ultraviolet (UV) induced fluorescence of the plants appears as a promising alternative. In a first paper, the feasibility of discriminating between corn hybrids, monocotyledonous, and dicotyledonous weeds was demonstrated on the basis of the complete spectra. Some considerations about the different sources of fluorescence oriented the focus to the blue-green fluorescence (BGF) part, ignoring the chlorophyll fluorescence that is inherently more variable in time. This paper investigates the potential of performing weed/crop discrimination on the basis of several large spectral bands in the BGF area. A partial least squares discriminant analysis (PLS-DA) was performed on a set of 1908 spectra of corn and weed plants over 3 years and various growing conditions. The discrimination between monocotyledonous and dicotyledonous plants based on the blue-green fluorescence yielded robust models (classification error between 1.3 and 4.6% for between-year validation). On the basis of the analysis of the PLS-DA model, two large bands were chosen in the blue-green fluorescence zone (400–425 nm and 425–490 nm). A linear discriminant analysis based on the signal from these two bands also provided very robust inter-year results (classification error from 1.5% to 5.2%). The same selection process was applied to discriminate between monocotyledonous weeds and maize but yielded no robust models (up to 50% inter-year error). Further work will be required to solve this problem and provide a complete UV fluorescence based sensor for weed–maize discrimination.