Accessing a plant's 3D geometry has become of significant importance for phenotyping during the last few years. Close-up laser scanning is an established method to acquire 3D plant shapes in real time with high detail, but it is stationary and has high investment costs. 3D reconstruction from images using structure from motion (SfM) and multi-view stereo (MVS) is a flexible cost-effective method, but requires post-processing procedures. The aim of this study is to evaluate the potential measuring accuracy of an SfM- and MVS-based photogrammetric method for the task of organ-level plant phenotyping. For this, reference data are provided by a high-accuracy close-up laser scanner. Using both methods, point clouds of several tomato plants were reconstructed at six following days. The parameters leaf area, main stem height and convex hull of the complete plant were extracted from the 3D point clouds and compared to the reference data regarding accuracy and correlation. These parameters were chosen regarding the demands of current phenotyping scenarios. The study shows that the photogrammetric approach is highly suitable for the presented monitoring scenario, yielding high correlations to the reference measurements. This cost-effective 3D reconstruction method depicts an alternative to an expensive laser scanner in the studied scenarios with potential for automated procedures.

In viticulture, phenotypic data are traditionally collected directly in the field via visual and manual means by an experienced person. This approach is time consuming, subjective and prone to human errors. In recent years, research therefore has focused strongly on developing automated and non-invasive sensor-based methods to increase data acquisition speed, enhance measurement accuracy and objectivity and to reduce labor costs. While many 2D methods based on image processing have been proposed for field phenotyping, only a few 3D solutions are found in the literature. A track-driven vehicle consisting of a camera system, a real-time-kinematic GPS system for positioning, as well as hardware for vehicle control, image storage and acquisition is used to visually capture a whole vine row canopy with georeferenced RGB images. In the first post-processing step, these images were used within a multi-view-stereo software to reconstruct a textured 3D point cloud of the whole grapevine row. A classification algorithm is then used in the second step to automatically classify the raw point cloud data into the semantic plant components, grape bunches and canopy. In the third step, phenotypic data for the semantic objects is gathered using the classification results obtaining the quantity of grape bunches, berries and the berry diameter.

In grapevine research the acquisition of phenotypic data is largely restricted to the field due to its perennial nature and size. The methodologies used to assess morphological traits and phenology are mainly limited to visual scoring. Some measurements for biotic and abiotic stress, as well as for quality assessments, are done by invasive measures. The new evolving sensor technologies provide the opportunity to perform non-destructive evaluations of phenotypic traits using different field phenotyping platforms. One of the biggest technical challenges for field phenotyping of grapevines are the varying light conditions and the background. In the present study the Phenoliner is presented, which represents a novel type of a robust field phenotyping platform. The vehicle is based on a grape harvester following the concept of a moveable tunnel. The tunnel it is equipped with different sensor systems (RGB and NIR camera system, hyperspectral camera, RTK-GPS, orientation sensor) and an artificial broadband light source. It is independent from external light conditions and in combination with artificial background, the Phenoliner enables standardised acquisition of high-quality, geo-referenced sensor data.

Amperometric electrochemical gas sensors have the advantage of combining good sensitivity and selectivity at relatively low cost. However, their use is restricted to the detection of gases that are very reactive at potentials positive of that at which oxygen is reduced. We describe here a new detection method for less reactive species such as benzene and halogenated hydrocarbons, which cannot be directly oxidized. Instead, the species is continuously adsorbed at a potential below 0.5 V and then oxidized at potentials where oxygen coadsorbs. The oxidation current is corrected for the oxygen adsorption current by performing an additional potential cycle, in which no adsorption is allowed to occur. We have so far obtained sensitivities in the lower ppm range for benzene, toluene, tetrachloroethene, epichlorohydrin, and vinyl acetate; for saturated halogenated hydrocarbons such as CFCl 3 , it is still below 1000 ppm.

PO2444 Observatory Toravere. Tartu, Estonia, U.S.S.R, For calculation of the plant BDRF the Monte Carlo method is used. The plant canopy architecture is given by a universal geometrical model allowing to consider structural parameters, such as canopy density and height, number of leaves per plant, distance between single leaves on the stem, dimensions and orientation of leaves and stems, etc and their influence on the shape of the BDR 8s a function of solar and view directions. The most valuable information about plant architecture contains the BDRF on the principal plene in the near-nadir directions, in the hot-spot area, and around specular directions of the solar beam. Bidirectional reflectance function, Monte Carlo method, plant canopy architecture.For the calculation of the plant canop bidirectional reflectance factor BDRFY the Monte Carlo method is used [ 6 , 7 j . The plant canopy architecture is given by a rather universal geometrical model which allows to coneider such structural parameters as canopy density and height, the number of leaves per plant, distance between leaves, dimensions and orientations of leaves and stems etc. and their influence on the shape of the BDR as a function on solar and view directions. The model plant consist of a vertical cylindrical stems with a diameter dst and a height H , of N L elliptical leaves with a length dL1 and a width d,. The Height of the first leaf above the soil Z is a random quantity given by normal bution with the mathematical expectation Q and the variance LT .Let the distances between neighbouring leaves be equal, then the height of the j-th leaf is zj = z l + (j-1) H / N L , d = 1,. . . ,NL. The azimuth angle of the first leaf is uniformly distributed on the '' interval [ o * 23r] , the azimuth of the d-th leaf is F j = $ , + ( j -l ) f s v where \9 is the azimuth angle between succeseive leaves on the genetic spiral. The leaf inclination angle 6' is described by the probability dlstridensity function* of leaf angle distribution (LAD) yL (e,), 0 d e L < n / 2 , 3i/2 0 according to one of the three rules: 1. all leaves have a constant and equal inclinat ion angle @* : $ ( Q~I = & ( e L -e * ) , where 8 is a Dirac delta function; 2. trigonometrical orientation l i 3 3. beta-distribution 131 ,-p,) = ~3 -+ 6c0S28, + c ~0 3 4 6~; where 6 ( p , v ) = r ( p ) r(v)/r (p+v> is the beta function. The plant canopy is simulated as an infinite field of model plants grown in rows. The row effect is characterized by the parameter Let LL be the given leaf area index. tien the distance between plants in one row A is uniquely defined by the equality L, = n N,d,f d L 2 / 4 A 2 p and Apis the distance between the rows. If A is known, we can find the stem area index L = d,t.TH/2AT> and the total area index LA1 2 L,+ L,. The following simple optical leaf model is proposed: isotropic transmission is characterized by the spectral coefficient t L ; the reflection consists of two components : the diffuse component by the spectral coefficient r , and the specular component. The lea...