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Author: Pflanz, Michael × End date: 2019 × Start date: 2019 × WGL Institute: ATB ×

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Now showing 1 - 3 of 3
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    Spectral shift as advanced index for fruit chlorophyll breakdown
    (Heidelberg : Springer, 2013) Seifert, Birgit; Pflanz, Michael; Zude, Manuela
    The decline of fruit chlorophyll is a valuable indicator of fruit ripeness. Fruit chlorophyll content can be nondestructively estimated by UV/VIS spectroscopy at fixed wavelengths. However, this approach cannot explain the complex changes in chlorophyll catabolism during fruit ripening. We introduce the apparent peak position of the red band chlorophyll absorption as a new qualitative spectral indicator. Climacteric fruit (apple: n = 24, mango: n = 38, tomato: n = 48) were analysed at different ripeness stages. The peak position and corresponding intensity values were determined between 650 and 690 nm of nondestructively measured fruit spectra as well as of corresponding spectra of fruit extracts. In the extracts, individual contents of chlorophyll a, chlorophyll b, pheophytin a and carotenoids were analysed photometrically, using an established iterative multiple linear regression approach. Nondestructively measured peak positions shifted unimodal in all three fruit species with significant shifts between fruit ripeness classes of maximal 2.00 ± 0.27 nm (mean ± standard error) in tomato and 0.57 ± 0.11 nm in apple. Peak positions in extract spectra were related to varying pigment ratios (Rmax = −0.91), considering individual pigments in the pool. The peak intensities in both spectral readings, nondestructive and fruit extracts, were correlated with absolute chlorophyll contents with Rmax = −0.84 and Rmax = 1.00, respectively. The introduced spectral marker of the apparent peak position of chlorophyll absorbance bears the potential for an advanced information gain from nondestructive spectra for the determination of fruit ripeness.
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    An automated field phenotyping pipeline for application in grapevine research
    (Basel : MDPI, 2015) Kicherer, Anna; Herzog, Katja; Pflanz, Michael; Wieland, Markus; Rüger, Philipp; Kecke, Steffen; Kuhlmann, Heiner; Töpfer, Reinhard
    Due to its perennial nature and size, the acquisition of phenotypic data in grapevine research is almost exclusively restricted to the field and done by visual estimation. This kind of evaluation procedure is limited by time, cost and the subjectivity of records. As a consequence, objectivity, automation and more precision of phenotypic data evaluation are needed to increase the number of samples, manage grapevine repositories, enable genetic research of new phenotypic traits and, therefore, increase the efficiency in plant research. In the present study, an automated field phenotyping pipeline was setup and applied in a plot of genetic resources. The application of the PHENObot allows image acquisition from at least 250 individual grapevines per hour directly in the field without user interaction. Data management is handled by a database (IMAGEdata). The automatic image analysis tool BIVcolor (Berries in Vineyards-color) permitted the collection of precise phenotypic data of two important fruit traits, berry size and color, within a large set of plants. The application of the PHENObot represents an automated tool for high-throughput sampling of image data in the field. The automated analysis of these images facilitates the generation of objective and precise phenotypic data on a larger scale.
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    Regression kriging for improving crop height models fusing ultra-sonic sensing with UAV imagery
    (Basel : MDPI, 2017) Schirrmann, Michael; Hamdorf, André; Giebel, Antje; Gleiniger, Franziska; Pflanz, Michael; Dammer, Karl-Heinz
    A crop height model (CHM) can be an important element of the decision making process in agriculture, because it relates well with many agronomic parameters, e.g., crop height, plant biomass or crop yield. Today, CHMs can be inexpensively obtained from overlapping imagery captured from unmanned aerial vehicle (UAV) platforms or from proximal sensors attached to ground-based vehicles used for regular management. Both approaches have their limitations and combining them with a data fusion may overcome some of these limitations. Therefore, the objective of this study was to investigate if regression kriging, as a geostatistical data fusion approach, can be used to improve the interpolation of ground-based ultrasonic measurements with UAV imagery as covariate. Regression kriging might be suitable because we have a sparse data set (ultrasound) and an exhaustive data set (UAV) and both data sets have favorable properties for geostatistical analysis. To confirm this, we conducted four missions in two different fields in total, where we collected UAV imagery and ultrasonic data alongside. From the overlapping UAV images, surface models and ortho-images were generated with photogrammetric processing. The maps generated by regression kriging were of much higher detail than the smooth maps generated by ordinary kriging, because regression kriging ensures that for each prediction point information from the UAV, imagery is given. The relationship with crop height, fresh biomass and, to a lesser extent, with crop yield, was stronger using CHMs generated by regression kriging than by ordinary kriging. The use of UAV data from the prior mission was also of benefit and could improve map accuracy and quality. Thus, regression kriging is a flexible approach for the integration of UAV imagery with ground-based sensor data, with benefits for precision agriculture-oriented farmers and agricultural service providers.