Spatial development of transport structures in apple (Malus x domestica Borkh.) fruit

The void network and vascular system are important pathways for the transport of gases, water and solutes in apple fruit (Malus x domestica Borkh). Here we used X-ray micro-tomography at various spatial resolutions to investigate the growth of these transport structures in 3D during fruit development of ‘Jonagold’ apple. The size of the void space and porosity in the cortex tissue increased considerably. In the core tissue, the porosity was consistently lower, and seemed to decrease towards the end of the maturation period. The voids in the core were more narrow and fragmented than the voids in the cortex. Both the void network in the core and in the cortex changed significantly in terms of void morphology. An automated segmentation protocol underestimated the total vasculature length by 9 to 12% in comparison to manually processed images. Vascular networks increased in length from a total of 5 meter at 9 weeks after full bloom, to more than 20 meter corresponding to 5 cm of vascular tissue per cubic centimeter of apple tissue. A high degree of branching in both the void network and vascular system and a complex three-dimensional pattern was observed across the whole fruit. The 3D visualisations of the transport structures may be useful for numerical modeling of organ growth and transport processes in fruit

Neural network Hilbert transform based filtered backprojection for fast inline x-ray inspection

X-ray imaging is an important tool for quality control since it allows to inspect the interior of products in a non-destructive way. Conventional x-ray imaging, however, is slow and expensive. Inline x-ray inspection, on the other hand, can pave the way towards fast and individual quality control, provided that a sufficiently high throughput can be achieved at a minimal cost. To meet these criteria, an inline inspection acquisition geometry is proposed where the object moves and rotates on a conveyor belt while it passes a fixed source and detector. Moreover, for this acquisition geometry, a new neural-network-based reconstruction algorithm is introduced: the neural network Hilbert transform based filtered backprojection. The proposed algorithm is evaluated both on simulated and real inline x-ray data and has shown to generate high quality reconstructions of 400 x 400 reconstruction pixels within 200 ms, thereby meeting the high throughput criteria

Online quality control of fruit and vegetables using X-ray imaging

Fruits and vegetables may develop internal disorders. Disorders greatly reduce consumer appreciation and commercial value. Nowadays, the most used method to detect these internal disorders is to manually and destructively inspect a number of randomly selected samples from each batch. This has several disadvantages: not all fruit are inspected, a number of fruit are destroyed and entire batches containing a significant amount of unaffected samples are discarded, leading to high quantities of waste. Recently, non-destructive inspection based on X-ray imaging has been suggested for internal quality evaluation of fresh produce, and while results were promising, the feasibility of the technique was only demonstrated offline with relatively slow imaging techniques. The aim of this PhD was, therefore, to develop novel methods that enable online inspection of internal quality of fruit using X-ray imaging. To reach this goal, in a first stage the performance of existing online X-ray radiography technology was investigated. Novel image processing algorithms were developed to detect granulation and endoxerosis in orange and lemon using X-ray radiography. This was achieved by adaptive thresholding of the affected tissue, and quantifying the sphericity of the segmented region. Up to 95.7 % of samples were correctly classified using this approach while taking randomly oriented samples into account. While this approach worked well for this type of defects, it needs a sufficiently large amount of contrast to be present in the radiographs. Additionally it is very time-consuming to develop specific detection algorithms for every possible type of defect. We then assess the application of conventional Computed Tomography (CT) to online systems. In so-called translational tomography, tomographic reconstructions are generated from a limited number of projections obtained by combining the sample translation on the conveyor belt with a limited but accurately controlled rotation while taking projection images. The 3-dimensional reconstructions are then analyzed using conventional image processing techniques to evaluate them for the presence of defects. While this method was shown to be useful for detection of internal browning in apple, it requires extensive hardware equipment and very specific data processing. To counter these disadvantages a new inspection method was proposed, combining X-ray radiography with 3D sensors and deformable shape models which are used to remove the contrast generated by the overall object shape from the radiographs. The approach was validated on a dataset of torus shapes with spherical defects of varying size and density, and a set of pears with internal cavities, and was shown to significantly improve detection of small defects with limited density deviations compared to visual inspection and conventional image processing of the radiographs. The proposed method could reliably detect more defects with identical density and size than visual inspection, with respective detection rates of 100 and 76 % for defect sizes of 3.5 mm. No significant improvement relative to conventional image processing was found, but this could be contributed to the fact the investigated defects were rather large cavities and thus provide sufficient image contrast. Additionally, the proposed method was extended to cope with samples with a spatially varying density. When compared with translational CT, both methods were shown to accurately detect internal browning in apples, both having their advantages and disadvantages related to a trade-off between hardware complexity and required prior sample knowledge. ROC analysis showed that the proposed method misclassified 5 % of samples (false positives) while this was 15 \% for the translational tomography approach at a detection rate of 90 %. While validation on hardware setups is still required, the proposed method shows much promise for further development.status: publishe

Get in shape for smart farming

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PicknPack: flexibele robots voor het verpakken van verse en kant-en-klare voedingsproducten

Terwijl in veel belangrijke industrieën, zoals de auto- en elektronica-industrie, automatisatie en robots al lang ingeburgerd zijn geraakt, vraagt het sorteren en verpakken van groenten en fruit nog zeer veel handenarbeid. Het Europese project PicknPack tracht de problemen van de voedselverpakking door robots op een slimme manier aan te pakken. De afdeling MeBioS van KU Leuven werkt hieraan mee.nrpages: 14-15status: publishe

3D Pore Structure Maps of Whole Apple using High Resolution X-Ray CT

‘Braeburn’ apples (Malus × domestica Borkh.) are highly susceptible to develop storage disorders when kept in long term CA storage. Due to limited gas transport through the fruit tissue, local oxygen deficiencies may occur, leading to internal brown spots (“‘Braeburn’ browning disorder”). The pore structure of the tissues has been shown to be a major contributing factor to the gas transport properties of fruit tissues. However, this was based on analysis of relatively small samples of fruit tissue. This research aimed to visualize the complete porous microstructure of whole ‘Braeburn’ apples as a means to better understand how the pore network will affect the internal gas concentrations and the development of storage defects. High resolution X-ray CT images of 5 ‘Braeburn’ apples were made at a voxel resolution of 45 micron for a non-destructive pore structure analysis. These images were segmented into cell and pore volumes and based on image analysis of the pore structure, a 3D geometrical description of the apple anatomy was formulated. It is shown that the pore space of the ovary and the hypanthium of the fruit were completely disconnected. The hypanthium was divided into a high and low porosity zone. The average porosity of the apples was 21.81 ± 2.78%. The highly porous zone of the hypanthium had an average porosity of 26.10 ± 2.46% with a well-connected pore network, while the less porous zone only had 4.30 ± 0.67% of air spaces. Furthermore, the high porosity zone of the hypanthium, which represents on average 77.90 ± 5.45% of the apple volume, contained 93.39 ± 3.12% of the pore volume. The ovary had the highest porosity with a value of 43.66 ± 4.85%, seed lobes included. Radial porosity profiles were calculated showing a highly varying porosity from skin to ovary, with minimal values in the region around the ovary. It is suggested that the low porosity of certain parts of the ‘Braeburn’ apple is causing a barrier for gas transport and is therefore directly related to the occurrence of brown spots during the long term CA storage of these apples.status: publishe

Multisensor X-ray inspection of internal defects in horticultural products

A combination of 3-D vision and X-ray radiography is proposed to enable low-cost, generally applicable online inspection of internal quality of horticultural and potentially other products. The underlying concept assumes that the shape of the product is known beforehand through a deformable shape model. A 3-D vision system is used in combination with the shape model to accurately determine the complete outer surface shape of the sample. This shape is voxelized to generate a reference product from which a X-ray radiograph is simulated to be compared with a measured radiograph, hence revealing the presence of any defects or disorders. Advantages of this method are that small deviations in internal density are detected easily since the cumulative information of the bulk object shape is removed. Furthermore, no specific detection algorithms have to be developed for different types of defects, since the method will directly identify deviations from the ideal. Validation on two datasets and comparison with two reference detections methods (classical image processing and a human operator) shows that the proposed method reliably (accuracy > 99 %) detect defects larger than 3.5 mm radius with densities differences between sample and defects as small as 10 %. Voids are reliably (accuracy > 99 %) detected down to a radius of 1.5 mm, corresponding to a volume of less than 0.02 cm3.publisher: Elsevier articletitle: Multisensor X-ray inspection of internal defects in horticultural products journaltitle: Postharvest Biology and Technology articlelink: http://dx.doi.org/10.1016/j.postharvbio.2017.02.002 content_type: article copyright: © 2017 Elsevier B.V. All rights reserved.status: publishe