Background In India, raw peanuts are obtained by aggregators from smallholder farms in the form of whole pods and the price is based on a manual estimation of basic peanut pod and kernel characteristics. These methods of raw produce evaluation are slow and can result in procurement irregularities. The procurement delays combined with the lack of storage facilities lead to fungal contaminations and pose a serious threat to food safety in many regions. To address this gap, we investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation. Results We generated 1752 individual peanut pod 2D X-ray projections using a computed tomography (CT) system (CTportable160.90). Out of these projections we predicted the kernel weight and shell weight, which are important indicators of the produce price. Two methods for the feature prediction were tested: (i) X-ray image transformation (XRT) and (ii) a trained convolutional neural network (CNN). The prediction power of these methods was tested against the gravimetric measurements of kernel weight and shell weight in diverse peanut pod varieties1. Both methods predicted the kernel mass with R2 > 0.93 (XRT: R2 = 0.93 and mean error estimate (MAE) = 0.17, CNN: R2 = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN (R2 = 0.91, MAE = 0.09) compared to XRT (R2 = 0.78; MAE = 0.08). Conclusion Our study demonstrated that the X-ray based system is a relevant technology option for the estimation of key peanut produce indicators (Figure 1). The obtained results justify further research to adapt the existing X-ray system for the rapid, accurate and objective peanut procurement process. Fast and accurate estimates of produce value are a necessary pre-requisite to avoid post-harvest losses due to fungal contamination and, at the same time, allow the fair payment to farmers. Additionally, the same technology could also assist crop improvement programs in selecting and developing peanut cultivars with enhanced economic value in a high-throughput manner by skipping the shelling of the pods completely. This study demonstrated the technical feasibility of the approach and is a first step to realize a technology-driven peanut production system transformation of the future.

The detection of threat items and prohibited items inside closed containers is an active field of research and an even more important real-world application. While conventional systems use X-ray projections for inspection, they lack true three-dimensional information which is crucial for a reliable detection. In this work, we introduce a demonstrator setup for three-dimensional threat detection within a 3D printed miniature container. We created a data processing pipeline that automatically integrates CT scanning, volumetric reconstruction and the actual detection of threat objects by means of a neural network. The achieved results are very promising and could be obtained using very few annotated training samples which is a realistic assumption in this application.

Motivation: Within the field of bio-implants one topic deals with the in-vitro production of 3D tissues from cell cultures. A major challenge in the cultivation of biopreparations is the imitation of the vascular system with its surrounding tissue. Additiv manufacturing of such vascularisation could be supported with an adequate template from high-resolution 3D radiographs. Goals: Evaluation of highresolution 3D-imaging methods and 3D-analysis approaches to extract the vascularisation from tissue samples. Methods: To provide such template, vascular structure of small tissues samples (partially treated with contrast medium) were recorded and compared using high-resolution CT and MRI imaging modalities. Optimal measurement parameters were selected for the acquisition of very small vessels. Interactive and semi-automated segmentation of the vessel system were investigated and compared. Results: MRI scans yield a higher contrast than CT scans, but are much slower. Duration of interactive segmentation ranges between 1 to 12 hours. Runtime of the (semi) automatic method was between 5 and 20 minutes, not counting manual adjustment of the parameters. Correlation between manual and automatic segmentation yield Hausdorff distances of 0.024 (CT) and 0.74 (MRI) and Dice coefficients of 0.7 (CT) and 0.39 (MRI). Conclusion: Both imaging methods are appropriate for high-resolution vessel detection and segmentation, nevertheless, MRI with no contrast agent seems preferable if the imaging time can be reduced.