Mattias van Dael

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

The void network and vascular system are important pathways for the transport of gases, water and solutes in apple fruit (Malus × 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 toward 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 9–12% in comparison to manually processed images. Vascular networks increased in length from a total of 5 m at 9 weeks after full bloom, to more than 20 m 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 visualizations of the transport structures may be useful for numerical modeling of organ growth and transport processes in fruit.

Building a Statistical Shape Model of the Apple from Corresponded Surfaces

In this paper, a method for building a 3D statistical shape model of the apple is described. The framework consists of two parts. First, a reference surface is registered to each apple surface, derived from 3D CT scans of apples, of the population to obtain meaningful correspondences between the shapes. In the second part, the corresponded surfaces are used to build a statistical shape model from the population of apples. This model maps out the variability within the population and by adapting the shape model parameters, new, realistic surfaces can be obtained. By parameterizing the surface, an apple can be described with a compact set of basis functions, which has applications in surface fitting description, recognition, or meshing, e.g. for storage simulation. The constructed apple shape model is tested on performance and has proven to be a good representation of the population and can be used in many applications.

Building 3D Statistical Shape Models of Horticultural Products

A method to build a 3D statistical shape model of horticultural products is described. The framework consists of two parts. First, the surfaces of the horticultural products, which are extracted from X-ray CT scans, are registered to obtain meaningful correspondences between the surfaces. In the second part, a statistical shape model is built from these corresponded surfaces, which maps out the variability of the surfaces and allows to generate new, realistic surfaces. The proposed shape modelling method is applied to 30 Jonagold apples, 30 bell peppers, and 52 zucchini. The average geometric registration error between the original instance and the deformed reference instance is 0.015 ± 0.011 mm for the apple dataset, 0.106 ± 0.026 mm for the bell pepper dataset, and 0.027 ± 0.007 mm for the Zucchini dataset. All shape models are shown to be an excellent representation of their specific population, as they are compact and able to generalize to an unseen sample of the population.

Inline discrete tomography system

It is proposed an inline X-ray Computed Tomography imaging setup.Discrete Tomography is well suited for the proposed scanning setup.The use of a conveyor belt with object rotation allows a full angular sampling.The use of prior knowledge about the external object shape leads to better results. X-ray Computed Tomography (CT) has been applied in agriculture engineering for quality and defect control in food products. However, conventional CT systems are neither cost effective nor flexible, making the deployment of such technology unfeasible for many industrial environments. In this work, we propose a simple and cost effective X-ray imaging setup that comprises a linear translation of the object in a conveyor belt with a fixed X-ray source and detector, with which a small number of X-ray projections can be acquired within a limited angular range. Due to the limitations of such geometry, conventional reconstruction techniques lead to misshapen images. Therefore, we apply a Discrete Tomography reconstruction technique that incorporates prior knowledge of the density of the objects materials. Moreover, we further improve the reconstruction results with the following strategies: (i) an image acquisition involving object rotation during a linear translation in the conveyor-belt; and (ii) an image reconstruction incorporating prior knowledge of the object support (e.g., obtained from optic sensors). Experiments based on simulation as well as real data demonstrate substantial improvement of the reconstruction quality compared to conventional reconstruction methods.

To disinfect or not to disinfect in postharvest research on the fungal decay of apple

Postharvest losses of fruit and vegetables can reach up to 30%, the main cause being microbial decay. For apple fruit, mostly fungal pathogens, such as Penicillium expansum, Colletotrichum spp., Neofabraea spp. and Botrytis cinerea, are important. As such losses are unsustainable in many ways, it is necessary that research is conducted to prevent them. Generally, for plants and fruit grown under non-sterile field conditions, disinfection is carried out prior to the start of a phytopathological experiment. The motivation for this practice is the removal of background contamination so that it will not affect the experimental outcome and its interpretation. In literature, a plethora of disinfection methods exists, differing in disinfectant, strength and duration. The following two disinfectants are commonly used: sodium hypochlorite (NaOCl) and ethanol. This article presents a targeted investigation into the effects of these two disinfectants on apple fruit surface and physiology. The results clearly demonstrate that both were affected by both disinfectants. NaOCl caused oxidative damage to the apples wax layer, causing it to crack. Ethanol affected a redistribution of the wax on the fruit surface and altered the wax composition and/or metabolism. Both NaOCl and ethanol treatment resulted in an increased respiration rate. Therefore, apple and possibly other fruit should not be disinfected in phytopathological studies. A negative control, as is typically used, is not solving this issue, as we clearly demonstrate that the living tissue shows metabolic effects following disinfection, and hence the study objects are changed, hampering a clear interpretation of the experimental outcomes. Moreover, fungal inoculation during experiments is typically taking place at rather large levels in wounded tissue (as infection success is the exception), outnumbering the variable levels of background population, if present.

Neural netwok based X-ray tomography for fast inspection of apples on a conveyor belt system

The throughput of an inline computed tomography (CT) based inspection system depends on the speed of its image reconstruction algorithm. Filtered back projection (FBP) provides fast reconstructions, but requires many high quality radiographs from all angles to obtain accurate reconstructions. This is not achievable in an inline environment. Iterative reconstruction methods yield adequate reconstructions from limited, but they are slow. Recently a new reconstruction algorithm was introduced [1] that can handle limited data and is very fast: the neural network FBP (NN-FBP). In this work, we introduce a neural network (NN) based Hilbert transform FBP (NN-hFBP) for inline inspection. This method reconstructs images with a filter-based Hilbert transform FBP method. The filters are application specific and trained by a neural network. Comparison of the NN-hFBP and conventional reconstruction methods applied to inline fan-beam X-ray data of apples shows that the NN-hFBP yields high quality images in a short reconstruction time.

Online Tomato Inspection Using X-ray Radiographies and 3-dimensional Shape Models

A method is proposed that fits a 3-dimensional shape model (SM) on X-ray radiographies of tomatoes on a conveyor belt to allow for inspection of internal tomato quality using X-ray. For the training of the SM a set of computed tomography (CT) scans is used. From these scans, the surfaces of the fruits are extracted. Corresponding points on all these surfaces are located after which the variation in position of every point can be determined using principal component analysis (PCA). The result of this process is a mean shape with various modes of variation, which represent the variability of the shape. Any shape can then be reconstructed through a linear combination of the mean shape and its modes of variation. During runtime, the contour of every tomato is extracted onto which the SM is fitted. This allows us to accurately estimate tomato volume and 3-dimensional shape, and assess the presence of defects and other unwanted properties from X-ray radiographies in an online application. Results are promising, but show that improvement can be made by simulating radiographs from the shape model and fitting these directly to the measured radiograph.