Non-destructive technologies for fruit and vegetable size determination - a review

Here, we review different methods for non-destructive horticultural produce size determination, focusing on electronic technologies capable of measuring fruit volume. The usefulness of produce size estimation is justified and a comprehensive classification system of the existing electronic techniques to determine dimensional size is proposed. The different systems identified are compared in terms of their versatility, precision and throughput. There is general agreement in considering that online measurement of axes, perimeter and projected area has now been achieved. Nevertheless, rapid and accurate volume determination of irregular-shaped produce, as needed for density sorting, has only become available in the past few years. An important application of density measurement is soluble solids content (SSC) sorting. If the range of SSC in the batch is narrow and a large number of classes are desired, accurate volume determination becomes important. A good alternative for fruit three-dimensional surface reconstruction, from which volume and surface area can be computed, is the combination of height profiles from a range sensor with a two-dimensional object image boundary from a solid-state camera (brightness image) or from the range sensor itself (intensity image). However, one of the most promising technologies in this field is 3-D multispectral scanning, which combines multispectral data with 3-D surface reconstructio

Examining Soil Based Construction Materials through X-Ray Computed Tomography

X-ray computed tomography (XRCT) enables the non-destructive analysis of samples internal structures down to a sub-micron resolution and has been used to examine the macrostructure of unstabilized soil based construction materials (SBCMs) alongside experiments on the materials unconfined compressive strength. SBCMs are manufactured mixtures of clay, sand and gravel which should be considered as highly unsaturated compacted soil where suction is the key source of strength. The use of XRCT in geotechnical literature is comprehensively reviewed before three laboratory investigations are described. Firstly crack propagation in SBCMs following unconfined compression is investigated and key lessons about XRCT scanning highlighted. Secondly the impact of altering sample size to match optimum XRCT scanning conditions is explored through experiments on void size distribution and unconfined compressive strength. Finally the effects of adding expansive clay to SBCM mixes on macrostructure are investigated and insights on how the unconfined compressive strength develops as SBCM dries are given. Conclusions from this thesis have applicability to both the SBCM industry, as the insights into the fundamental behaviour of SBCM can be used to inform building practice, and geotechnical researchers where the extensive use and development of XRCT can be applied to investigate the internal structure of a wide range of geotechnical materials

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Third-generation site characterization: cryogenic core collection, nuclear magnetic resonance, and electrical resistivity

2016 Fall.Includes bibliographical references.To view the abstract, please see the full text of the document

Debris-flow erosion and deposition dynamics

Debris flows are a major natural hazard in mountains world wide, because of their destructive potential. Prediction of occurrence, magnitude and travel distance is still a scientific challenge, and thus research into the mechanics of debris flows is still needed. Poor understanding of the processes of erosion and deposition are partly responsible for the difficulties in predicting debrisflow magnitude and travel distance. Even less is known about the long-term evolution of debrisflow fans because the sequential effects of debris-flow erosion and deposition in thousands of flows are poorly documented and hence models to simulate debris-flow fans do not exist. Here I address the specific issues of the dynamics of erosion and deposition in single flows and over multiple flows on debris-flow fans by terrain analysis, channel monitoring and fan evolution modeling. I documented erosion and deposition dynamics of debris flows at fan scale using the Illgraben debris-flow fan, Switzerland, as an example. Debris flow activity over the past three millenia in the Illgraben catchment in south-western Switzerland was documented by geomorphic mapping, radiocarbon dating of wood and cosmogenic exposure dating of deposits. In this specific case I also documented the disturbance induced by two rock avalanches in the catchment resulting in distinct patterns of deposition on the fan surface. Implications of human intervention and the significance of autogenic forcing of the fan system are also discussed. Quantification and understanding of erosion and deposition dynamics in debris flows at channel scale hinges on the ability to detect surface change. But change detection is a fundamental task in geomorphology in general. Terrestrial laser scanners are increasingly used for monitoring down to centimeter scale of surface change resulting from a variety of geomorphic processes, as they allow the rapid generation of high resolution digital elevation models. In this thesis procedures were developed to measure surface change in complex topography such as a debris-flow channel. From this data high-resolution digital elevation models were generated. But data from laser scanning contains ambiguous elevation information originating from point cloud matching, surface roughness and erroneous measurments. This affects the ability to detect change, and results in spatially variable uncertainties. I hence developed techniques to visualize and quantify these uncertainties for the specific application of change detection. I demonstrated that use of data filters (e.g. minimum height filter) on laser scanner data introduces systematic bias in change detection. Measurement of debris-flow erosion and deposition in single events was performed at Illgraben, where multiple debris flows are recorded every year. I applied terrestrial laser scanning and flow hydrograph analysis to quantify erosion and deposition in a series of debris flows. Flow depth was identified as an important control on the pattern and magnitude of erosion, whereas deposition is governed more by the geometry of flow margins. The relationship between flow depth and erosion is visible both at the reach scale and at the scale of the entire fan. Maximum flow depth is a function of debris flow front discharge and pre-flow channel cross section geometry, and this dual control gives rise to complex interactions with implications for long-term channel stability, the use of fan stratigraphy for reconstruction of past debris flow regimes, and the predictability of debris flow hazards. Debris-flow fan evolution on time scales of decades up to ten thousands of years is poorly understood because the cumulative effects of erosion and deposition in subsequent events are rarely well documented and suitable numerical models are lacking. Enhancing this understanding is crucial to assess the role of autogenic (internal) and allogenic (external) forcing mechanisms on building debris-flow fans over long time scales. On short time scales understanding fan evolution is important for debris-flow hazard assessment. I propose a 2D reduced-complexity model to assess debris-flow fan evolution. The model is built on a broad range of qualitative and empirical observations on debris-flow behaviour as well as on monitoring data acquired at Illgraben as part of this thesis. I have formulated a framework of rules that govern debris-flow behaviour, and that allows efficient implementation in a numerical simulation. The model is shown to replicate the general behaviour of alluvial fans in nature and in flume experiments. In three applications it is demonstrated how fan evolution modeling may improve understanding of inundation patterns, surface age distribution and surface morphology

The potential use of non destructive optical-based techniques for early detection of chilling injury and freshness in horticultural commodities

The increasing concern and awareness of the modern consumer regarding food including fruits and vegetables, has been oriented the research in the food industry to develop rapid, reliable and cost effective methods for the evaluation of food products including the traceability of the product history in terms of storage conditions. Since the conventional destructive analysis methods are time consuming, expensive, targeted and labor intensive, non-destructive methods are gaining significant popularity. These methods are being utilized by the food industry for the early detection of fruits defects, for the classification of fruits and vegetables on the basis of variety, maturity stage, storage history and origin and for the prediction of main internal constituents. Since chilling injury (CI) occurrence is a major problem for chilling sensitive products, as tropical and sub-tropical fruit and vegetables, prompt detection of CI is still a challenge to be addressed. The incorrect management of the temperature during storage and distribution causes significant losses and wastes in the horticultural food chain, which can be prevented if the product is promptly reported to the correct temperature, before that damages become irreversible. For this reason, rapid and fast methods for early detection of CI are needed. In the first work of this thesis, non-destructive optical techniques were applied for the early detection of chilling injury in eggplants. Eggplant fruit is a chilling sensitive vegetable that should be stored at temperatures above 12°C. For the estimation of CI, fruit were stored at 2°C (chilling temperature) and at 12°C (safe storage temperature) for a time span of 10 days. CIE L*a*b* measurements, reflectance data in the wavelength range 360–740 nm, Fourier Transformed (FT)-NIR spectra (800–2777 nm) and hyperspectral images in the visible (400–1000 nm) and near infrared (900–1700 nm) spectral range were acquired for each fruit. Partial least square discriminant analysis (PLSDA), supervised vector machine (SVM) and k-nearest neighbor (kNN) were applied to classify fruit according to the storage temperature. According to the results, although CI symptoms started being evident only after the 4th day of storage at 2°C, it was possible to discriminate fruit earlier using FT-NIR spectral data with the SVM classifier (100 and 92% non-error-rate (NER) in calibration and cross validation, respectively, in the whole data set. Color data and PLSDA classification possessed relatively lower accuracy as compared to SVM. These results depicted a good potential of for the non-destructive techniques for the early detection of CI in eggplants. Similarly, in the second experimental part of the thesis, hyperspectral imaging in Vis-NIR and SWIR regions combined with chemometric techniques were used for the early estimation of chilling injury in bell peppers. PLSDA models accompanied by wavelength selection algorithms were used for this purpose, with accuracies ranging from 81% and 87% non-error-rate (NER) based on the wavelength ranges used and variables selected. PLSR models were developed for the prediction of days of cold storage resulting in R²CV = 0.92 for full range and R²CV = 0.79 using selected variables. Based on the results, it was concluded, that Vis-NIR hyperspectral imaging is a reliable option for on-line classification of fresh versus refrigerated fruit and for identifying early incidence of CI. Inspired by the results obtained from previous studies a third study regarded the use of nondestructive techniques for the estimation of freshness of eggplants using color, spectral and hyperspectral measurements. To this aim, fruit were stored at 12°C for 10 days. Fruit were left at room temperature (20°C) for 1 day after sampling which was done with a 2-day interval, simulating one-day of shelf life in the market. PLSR models were developed using the spectral and hyperspectral data and the storage days, allowing safe assessment of the freshness of the fruits along with the utilization of SPA for variable reduction. The results depicted strong correlation between storage days, FT-NIR spectra and the hyperspectral data in the Vis-NIR range with accuracies as high as RC> 0.98, RCV> 0.94, RMSEC < 0.4 and RMSECV< 0.8, followed by lower accuracies using color data. The results of this study may set the basis to develop a protocol allowing a rapid screening and sorting of eggplants according to their postharvest freshness either upon handling in a distribution center or even upon the reception in the retail market. In the last work, as a deeper investigation, the effect of temperature and storage time on the FTNIR spectra was statistically investigated using ANOVA-simultaneous component analysis (ASCA) on eggplant fruit as a crop model. Also in this case, fruit were stored at 2 and 12 °C, for 10 days. Sensorial analysis, electrolyte leakage (EL), weight loss and firmness were used, as the reference measurements for CI. ASCA model proved that both temperature, duration of storage, and their interaction had a significant effect on the spectral changes over time of eggplant fruit. Followed by ASCA, PLSDA was conducted on the data to discriminate fruit based on the storage temperature. In this case, only the WL significant in the ASCA approach for temperature were considered, allowing to reach 87.4±2.7% as estimated by a repeated double-cross-validation procedure. The outcomes of all these studied manifested a promising, non-invasive, and fast tool for the control of CI and the prevention of food losses due to the incorrect management of the temperature in the horticultural food chain