Weeds are a severe pest in agriculture, causing extensive yield loss. Weed control of grass and broadleaf weeds is commonly performed by applying selective herbicides homogeneously all over the field. As presented in several studies, applying the herbicide only where needed has economical as well as environmental benefits. Combining remote sensing tools and techniques with the concept of precision agriculture has the potential to automatically locate and identify weeds in order to allow precise control. The objective of this work was to detect annual grasses and broadleaf weeds among cereal as well as broadleaf crops, with the aid of field spectroscopy tools. Spectral reflectance values of: (1) crops (wheat and chickpea); (2) grass as well as broadleaf weeds; and (3) soil background, were obtained by ASD in the range of 400-2400 nm, in leaf and canopy levels. Leaf spectral classification for botanical genera was almost perfect (99%). Canopy spectral classification for targets was accurate (total of 95%) when the field of view (FOV) contained the same target. Classification of 87% was achieved for canopy spectra (25-40 days after emergence) of target in heterogeneous FOV, providing an applicative herbicide implementation. When the partial least squares (PLS) regression was applied, it was found that only several wavelengths were selected for qualitative and quantitative prediction. These wavelengths were in the range of 400-1000 nm and the red-edge region was constantly selected. Therefore the Spectral Camera HS (Specim) with 1600 pixel per line and 849 bands in the range of 400-1000 nm was used to continue this study. The properties of the camera should improve the ability to separate spectrally between targets by applying spatial factor. The data obtained by the camera will be applied for resampling bands of the superspectral future satellite Vegetation and Environmental New Micro Spacecraft (VENmS). Thereafter, superspectral with high spatial resolution satellites and/or ground systems would enable precise weeds control.
