The application of (smart) cameras for process control, mapping, and advanced imaging in agriculture has become an element of precision farming that facilitates the conservation of fertilizer, pesticides, and machine time. This technique additionally reduces the amount of energy required in terms of fuel. Although research activities have increased in this field, high camera prices reflect low adaptation to applications in all fields of agriculture. Smart, low-cost cameras adapted for agricultural applications can overcome this drawback. The normalized difference vegetation index (NDVI) for each image pixel is an applicable algorithm to discriminate plant information from the soil background enabled by a large difference in the reflectance between the near infrared (NIR) and the red channel optical frequency band. Two aligned charge coupled device (CCD) chips for the red and NIR channel are typically used, but they are expensive because of the precise optical alignment required. Therefore, much attention has been given to the development of alternative camera designs. In this study, the advantage of a smart one-chip camera design with NDVI image performance is demonstrated in terms of low cost and simplified design. The required assembly and pixel modifications are described, and new algorithms for establishing an enhanced NDVI image quality for data processing are discussed.

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Ambrosia artemisiifolia (ragweed) is an invasive plant in Europe. An optical detection system for effective monitoring requires differences in the spectral reflectance properties compared with other plant species. A. artemisiifolia often occurs together with Artemisia vulgaris (mugwort). Both plant species are of the Asteraceae family and they are almost indistinguishable in appearance. With the help of hyperspectral image analysis, a method was developed to determine characteristic wavelengths for their classification. High-resolution hyperspectral images (400-1000 nm) were generated indoors. The factors measured were two weed species, two tissue classes (leaf and stem) and three growth stages (rosette growth, inflorescence emergence and fruit development). Only the stems of A. artemisiifolia in the fruit development stage showed different reflectance behaviour compared with its leaves and with the stems and leaves of A. vulgaris. At wavelengths ranging from 550 to 650 nm, the reflectance increased, and then at wavelengths up to 680 nm, the reflectance decreased. The other tissue classes showed constantly decreasing spectral reflectance from 550 to 680 nm. In the two other early growth stages, the reflectance of all four tissue classes decreased similarly. Thus, using two wavelengths of 550 and 650 nm, classification between A. artemisiifolia and A. vulgaris at fruit development was achieved. The findings could be a first step to develop an optical outdoor detection system to identify hot spots of A. artemisiifolia.