Using Real-Time Stereo Vision for Mobile Robot Navigation
Autonomous Robots
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This paper introduces a methodology able to discriminate between non-stressed plants and N, P and K stress symptoms in spring barley grown under controlled conditions, utilizing the spectral and spatial dimensions simultaneously. Nine spectral measurements in the range 450-1000 nm were taken for each plant. The measuring points were spatially located at the tip, middle and base of the last three fully developed leaves. This design generated a four-way data set consisting of measurements as a function of (i) the specific plant, (ii) the spectral wavelength, (iii) the plant leaf position and (iv) the position on the leaf. Multiway partial least squares regression analysis with dummy variables was able correctly to classify the four nutrient conditions with 92% accuracy regardless of the respective growth stages within a time window of 2 weeks. The addition of the spatial dimension to the spectral dimension proved to be a promising nutrient diagnostic tool. Without performance loss it was possible to reduce the hyperspectral resolution to a resolution of three wavelengths. The three selected 2 nm wide bands were R450, R700 and R810, which agrees well with the literature on plant spectral reflectance in relation to nutritional stress.