Search

• X-ray CT scans were taken from batches of sugar beet fruit. • Pericarp, true seed and air were manually annotated in the CT volumes. • A 3D U-Net model was trained to segment pericarp, true seed and air in CT data. • A CNN network was trained to detect bigerm fruit. • The methods can be used for nondestructive and high-throughput phenotyping of seeds. Sugar beet (Beta vulgaris L. ssp. vulgaris) accounts for roughly 20 % of the global sugar production, with the remainder derived from sugar cane (Saccharum officinarum L.). To maximize sugar yield, high performing sugar beet varieties are needed, in combination with good agronomical practices. Delivering vigorous seeds to the market and meeting the highest quality standards is, therefore, essential. Seed vigor is highly determined by fruit morphology, with the main characteristics of interest being fruit and true seed size, pericarp morphology and fruit filling. Current methods for evaluating fruit morphology mostly rely on labor-intensive and destructive analyses. Here we present a high-throughput nondestructive method to quantitatively phenotype sugar beet fruit and true seeds using X-ray micro-CT imaging and deep learning. A 3D convolutional neural network was trained for the semantic segmentation of the pericarp, true seed and air in X-ray micro-CT scans. High average Dice scores of 0.996, 0.971 and 0.930 were found for the pericarp, true seed and air, respectively. Additionally, since farmers target single plants after emergence in the field, we present a method to identify whether sugar beet fruit are monogerm or contain more than one seed (bigerm). An excellent overall classification accuracy, false positive and false negative rate of respectively 98.6, 1.0 and 1.8 % were achieved. The presented methods have a high potential for integration into tools for breeding programs and the sample-wise monitoring and adjustment of production processes. [ABSTRACT FROM AUTHOR]

• A dataset of X-ray radiographs of pear fruit was simulated from CT scans. • Deep anomaly detection models were trained on data of healthy pears and tested on data of healthy and defect pears. • Synthetic anomalies in training improved performance over the unsupervised case. • High AUC scores (up to 0.96) and classification accuracy (up to 95%) were obtained. • Defect pears with a cavity percentage > 1.0% could be detected 100% accurate. To preserve the quality of fresh fruit after harvest and to meet the year-round demand for high-quality fruit, pears are stored under a controlled atmosphere. However, due to preharvest events or suboptimal storage conditions, internal disorders might develop resulting in severe quality loss. Examples include internal browning and cavities, which are invisible externally. Here, X-ray radiography is investigated as a technique for internal quality inspection. The detection of defect fruit is approached as an anomaly detection (AD) problem, in which a model is constructed using nominal data and an anomaly score is used to identify defect fruit. In this work, multiple deep AD methods are shown to be effective to detect pears with internal cavity and browning disorders using X-ray radiographs (mean area under the receiver operating characteristic curve (AUC) up to 0.962). The best performing methods were found on par with a state-of-the-art multisensor disorder detection method (mean AUC up to 0.966). By investigating AD performance in function of internal disorder severity, it was shown that defect fruit with a cavity volume percentage > 1.0% could be detected 100% accurate using inline X-ray imaging. For lower cavity area percentages, the accuracy depended on the internal browning severity. Additionally, the explainability of the deep AD methods, i.e., how well human interpretable insight can be provided from each method's predictions, were qualitatively evaluated using anomaly heatmaps, which provided useful insight in the execution of the deep learning algorithms. [ABSTRACT FROM AUTHOR]

To preserve the quality of fresh pear fruit after harvest and deliver quality fruit year-round a controlled supply chain and long-term storage are applied. During storage, however, internal disorders can develop due to suboptimal storage conditions that may not cause externally visible symptoms. This makes them impossible to be detected by current commercial quality grading systems in a reliable and non-destructive way. A combination of a Support Vector Machine coupled with a feature extraction algorithm and X-ray Computed Tomography is proposed to successfully detect internal disorders in 'Conference' and 'Cepuna' pear fruit nondestructively. Classifiers were able to distinguish defective from sound fruit with classification accuracies ranging between 90.2 and 95.1% depending on the cultivar and number of used features. Moreover, low false positive and negative rates were obtained, respectively ranging between 0.0 and 6.7%, and 5.7 and 13.3%. Classifiers trained on 'Conference' data were transferred effectively to the 'Cepuna' cultivar, suggesting generalizability to other cultivars as well. With continuing developments in both hardware and software to increase inspection speed and reduce equipment costs, the method can be implemented in industrial applications, e.g., inline translational X-ray CT. • Healthy and defect Conference and Cepuna pears resulted from storage experiments. • Fast X-ray CT scans were obtained of all fruit and relevant features were extracted. • SVMs achieved high accuracy in detecting defect fruit regardless of the cultivar. • Feature selection could limit the used features to two without a drop in performance. • Method can be implemented for postharvest fruit sorting using fast X-ray CT. [ABSTRACT FROM AUTHOR]

[Display omitted] • Deep learning was able to label defects in X-ray radiographs of pear fruit. • A large amount of ground truth data were obtained by simulations from X-ray CT data. • Synthetic training data improved segmentation of browning and cavity defects. A particular challenge for food quality inspection remains the quantification of internal defects. This work addresses the challenge of internal defects segmentation in pear fruit (cv. Conference) based on high-throughput inline X-ray radiography images in combination with deep learning. Unlike previous approaches, which focused on healthy vs defect classification, the current segmentation approach is able to determine the type of defect, its dimensions, and location. To this end, a novel simulation method was designed to obtain input-target image pairs of radiography data, and more diverse defect pears were generated using a conditioned generator model. The obtained data contributed to the design of a segmentation model that labeled every pixel in the X-ray radiographs of pear fruit as 'external air', 'healthy tissue', 'core', 'browning', or 'cavity'. We demonstrated that additional synthetic data in the learning process drastically improved the model performance. For instance, the mean IoU increased from 0.781 ± 0.112 to 0.883 ± 0.088 for consumable pears with minor cavities and browning. In terms of utility, our segmentation maps provide more detailed information about the type, size, and location of the disorders compared to the heatmaps produced by existing benchmark classifiers. [ABSTRACT FROM AUTHOR]

During respiratory quotient based dynamic controlled atmosphere (RQ-DCA) storage, the oxygen concentration in the room is reduced until the RQ measured in the air of the room (R Q a i r ) increases above a predefined threshold. This increase in RQ indicates an increased extent of fermentation within the fruit. Because of internal gas gradients, the oxygen concentration inside the fruit is lower than that of the storage atmosphere and, hence, the RQ value in the fruit (R Q f r u i t , m a x ) is larger than R Q a i r . Moreover, due to biological variability and fluctuations in the storage conditions, both R Q f r u i t , m a x and R Q a i r vary. Currently, no information exists about how the RQ measured in the atmosphere correlates with the RQ value inside the fruit, and how representative this R Q a i r is for all fruit inside the room. In this article, we investigated how variability affects the critical R Q a i r threshold in RQ-DCA. Hereto we used a multiscale stochastic reaction-diffusion model to compute gas transport within pears of three different cultivars, 'Cepuna', 'Conference' and 'Queen's Forelle'. Biological variability in diffusion and respiration parameters and fluctuations in cool room conditions were taken into account by performing Monte-Carlo simulations. The results showed large variations in both R Q f r u i t , m a x and R Q a i r . The effect of cool room filling degree on both RQ values was negligible, while variations in fruit material properties (pear size, tissue gas diffusivity and respiration kinetics) showed to be of high importance. However, as R Q a i r increased before fermentation started to dominate, the underestimation and variability of R Q a i r did not inhibit a proper detection of hypoxia inside the fruit within the room. This finding indicates that R Q a i r is a proper parameter to control DCA storage of various cultivars. Due to differences in fruit properties, the RQ-response varied between the cultivars. 'Cepuna' requires the highest O 2 (0.61 kPa) to prevent fermentation, while 'Conference' and 'Queen's Forelle' can be stored at lower O 2 (0.33 and 0.22 kPa, respectively). The results obtained in this research can be used to optimize RQ-DCA storage of each cultivar. • Gas exchange properties of pear vary with cultivar. • Stochastic simulations quantified the variability in RQ during RQ-DCA storage. • Large variations in RQ were predicted during RQ-DCA storage. • Room RQ measurements allowed to properly detect fruit fermentation. [ABSTRACT FROM AUTHOR]