Recent Progress of Hyperspectral Imaging on Quality and Safety Inspection of Fruits and Vegetables: A Review

A novel method for vegetable and fruit classification based on using diffusion maps and machine learning

Vegetable and fruit classification can help all links of agricultural product circulation to better carry out inventory management, logistics planning and supply chain coordination, and improve the efficiency and response speed of the supply chain. However, the current classification of vegetables and fruits mainly relies on manual classification, which inevitably introduces the influence of human subjective factors, resulting in errors and misjudgments in the classification of vegetables and fruits. In response to this serious problem, this research proposes an efficient and reproducible novel model to classify multiple vegetables and fruits using handcrafted features. In the proposed model, preprocessing operations such as Gaussian filtering, grayscale and binarization are performed on the pictures of vegetables and fruits to improve the quality of the pictures; statistical texture features representing vegetable and fruit categories, wavelet transform features and shape features are extracted from the preprocessed images; the feature dimension reduction method of diffusion maps is used to reduce the redundant information of the combined features composed of statistical texture features, wavelet transform features and shape features; five effective machine learning methods were used to classify the types of vegetables and fruits. In this research, the proposed method was rigorously verified experimentally and the results show that the SVM classifier achieves 96.25% classification accuracy of vegetables and fruits, which proves that the proposed method is helpful to improve the quality and management level of vegetables and fruits, and provide strong support for agricultural production and supply chain.

Computer vision based deep learning approach for toxic and harmful substances detection in fruits

Formaldehyde (CH₂O) is one of the significant chemicals mixed with different perishable fruits in Bangladesh. The fruits are artificially preserved for extended periods by dishonest vendors using this dangerous chemical. Such substances are complicated to detect in appearance. Hence, a reliable and robust detection technique is required. To overcome this challenge and address the issue, we introduce comprehensive deep learning-based techniques for detecting toxic substances. Four different types of fruits, both in fresh and chemically mixed conditions, are used in this experiment. We have applied diverse data augmentation techniques to enlarge the dataset. The performance of four different pre-trained deep learning models was then assessed, and a brand-new model named "DurbeenNet," created especially for this task, was presented. The primary objective was to gauge the efficacy of our proposed model compared to well-established deep learning architectures. Our assessment centered on the models' accuracy in detecting toxic substances. According to our research, GoogleNet detected toxic substances with an accuracy rate of 85.53 %, VGG-16 with an accuracy rate of 87.44 %, DenseNet with an impressive accuracy rate of 90.37 %, and ResNet50 with an accuracy rate of 91.66 %. Notably, the proposed model, DurbeenNet, outshone all other models, boasting an impressive accuracy rate of 96.71 % in detecting toxic substances among the sample fruits.

Gray mold and anthracnose disease detection on strawberry leaves using hyperspectral imaging

BACKGROUND

Gray mold and anthracnose are the main factors affecting strawberry quality and yield. Accurate and rapid early disease identification is of great significance to achieve precise targeted spraying to avoid large-scale spread of diseases and improve strawberry yield and quality. However, the characteristics between early disease infected and healthy leaves are very similar, making the early identification of strawberry gray mold and anthracnose still a challenge.

RESULTS

Based on hyperspectral imaging technology, this study explored the potential of combining spectral fingerprint features and vegetation indices (VIs) for early detection (24-h infected) of strawberry leaves diseases. The competitive adaptive reweighted sampling (CARS) algorithm and ReliefF algorithm were used for the extraction of spectral fingerprint features and VIs, respectively. Three machine learning models, Backpropagation Neural Network (BPNN), Support Vector Machine (SVM) and Random Forest (RF), were developed for the early identification of strawberry gray mold and anthracnose, using spectral fingerprint, VIs and their combined features as inputs respectively. The results showed that the combination of spectral fingerprint features and VIs had better recognition accuracy compared with individual features as inputs, and the accuracies of the three classifiers (BPNN, SVM and RF) were 97.78%, 94.44%, and 93.33%, respectively, which indicate that the fusion features approach proposed in this study can effectively improve the early detection performance of strawberry leaves diseases.

CONCLUSIONS

This study provided an accurate, rapid, and nondestructive recognition of strawberry gray mold and anthracnose disease in early stage.

Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review

The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables.

A comprehensive review on acquisition of phenotypic information of Prunoideae fruits: Image technology

Fruit phenotypic information reflects all the physical, physiological, biochemical characteristics and traits of fruit. Accurate access to phenotypic information is very necessary and meaningful for post-harvest storage, sales and deep processing. The methods of obtaining phenotypic information include traditional manual measurement and damage detection, which are inefficient and destructive. In the field of fruit phenotype research, image technology is increasingly mature, which greatly improves the efficiency of fruit phenotype information acquisition. This review paper mainly reviews the research on phenotypic information of Prunoideae fruit based on three imaging techniques (RGB imaging, hyperspectral imaging, multispectral imaging). Firstly, the classification was carried out according to the image type. On this basis, the review and summary of previous studies were completed from the perspectives of fruit maturity detection, fruit quality classification and fruit disease damage identification. Analysis of the advantages and disadvantages of various types of images in the study, and try to give the next research direction for improvement.

Convolutional neural network for apple bruise detection based on hyperspectral

The timely detection of apple bruises caused by collision and squeeze is of great significance to reduce the economic losses of the apple industry. This study proposed a spectral analysis model (SpectralCNN) based on a one-dimensional convolutional neural network to detect apple bruises. The influences of six spectral preprocessing methods on the SpectralCNN model were firstly analyzed in this paper. Compared with traditional chemometric models, the SpectralCNN model had a better accuracy, which was demonstrated not depend on the spectral preprocessing method by experiment results. Then, 20 characteristic wavelengths could be extracted by successive projection algorithm. The SpectralCNN model could achieve an accuracy of 95.79% on the test set of characteristic wavelengths, indicating that the extracted characteristic wavelengths contain most of the features of bruised and healthy pixels.

A Novel Hyperspectral Method to Detect Moldy Core in Apple Fruits

An innovative low-cost device based on hyperspectral spectroscopy in the near infrared (NIR) spectral region is proposed for the non-invasive detection of moldy core (MC) in apples. The system, based on light collection by an integrating sphere, was tested on 70 apples cultivar (cv) Golden Delicious infected by Alternaria alternata, one of the main pathogens responsible for MC disease. Apples were sampled in vertical and horizontal positions during five measurement rounds in 13 days’ time, and 700 spectral signatures were collected. Spectral correlation together with transmittance temporal patterns and ANOVA showed that the spectral region from 863.38 to 877.69 nm was most linked to MC presence. Then, two binary classification models based on Artificial Neural Network Pattern Recognition (ANN-AP) and Bagging Classifier (BC) with decision trees were developed, revealing a better detection capability by ANN-AP, especially in the early stage of infection, where the predictive accuracy was 100% at round 1 and 97.15% at round 2. In subsequent rounds, the classification results were similar in ANN-AP and BC models. The system proposed surpassed previous MC detection methods, needing only one measurement per fruit, while further research is needed to extend it to different cultivars or fruits.

Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine

Apples are one of the most widely planted fruits in the world, with an extremely high annual production. Several issues should be addressed to avoid the damaging of samples during the quality grading process of apples (e.g., the long detection period and the inability to detect the internal quality of apples). In this study, an electronic nose (e-nose) detection system for apple quality grading based on the K-nearest neighbor support vector machine (KNN-SVM) was designed, and the nasal cavity structure of the e-nose was optimized by computational fluid dynamics (CFD) simulation. A KNN-SVM classifier was also proposed to overcome the shortcomings of the traditional SVMs. The performance of the developed device was experimentally verified in the following steps. The apples were divided into three groups according to their external and internal quality. The e-nose data were pre-processed before features extraction, and then Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to reduce the dimension of the datasets. The recognition accuracy of the PCA–KNN-SVM classifier was 96.45%, and the LDA–KNN-SVM classifier achieved 97.78%. Compared with other commonly used classifiers, (traditional KNN, SVM, Decision Tree, and Random Forest), KNN-SVM is more efficient in terms of training time and accuracy of classification. Generally, the apple grading system can be used to evaluate the quality of apples during storage.

Phenotyping Local Eggplant Varieties: Commitment to Biodiversity and Nutritional Quality Preservation

Given the little variability among commercialised eggplants mainly in developed countries, exploring, and structuring of traditional varieties germplasm collections have become a key element for extending ecotypes and promoting biodiversity preservation and consumption. Thirty-one eggplant landraces from Spain were characterised with 22 quantitative and 14 qualitative conventional morphological descriptors. Landraces were grouped based on their fruit skin colour (black-purple, striped, white, and reddish). Landraces B7, B20, and B24 were left out for their distinctive fruit characteristics. Wide variation for plant, leaf, flower, and fruit phenology traits was observed across the local landraces, and fruit descriptors were considered the most important ones. In a second experiment, landraces, B14, B16, and B17 were selected to determine fruit quality. By contemplating the benefits provided by antioxidants and sugars for human health, pulp antioxidant capacity, total phenolic, ascorbic acid, carotenoid, flavonoid, and total sugar content were determined. Significant differences were observed across these three landraces, and B14 was highlighted for its antioxidant properties, while B17 stood out for its high sugar content. B16 did not stand out for any traits. The results indicate the wide variability in eggplants for their phenotypic and nutritional characteristics, which emphasises the importance of traditional varieties as the main source of agricultural biodiversity.

Hyperspectral Imaging to Characterize Table Grapes

Table grape quality is of importance for consumers and thus for producers. Its objective quality is usually determined by destructive methods mainly based on sugar content. This study proposed to evaluate the possibility of hyperspectral imaging to characterize table grapes quality through its sugar (TSS), total flavonoid (TF), and total anthocyanin (TA) contents. Different data pre-treatments (WD, SNV, and 1st and 2nd derivative) and different methods were tested to get the best prediction models: PLS with full spectra and then Multiple Linear Regression (MLR) were realized after selecting the optimal wavelengths thanks to the regression coefficients (β-coefficients) and the Variable Importance in Projection (VIP) scores. All models were good at showing that hyperspectral imaging is a relevant method to predict sugar, total flavonoid, and total anthocyanin contents. The best predictions were obtained from optimal wavelength selection based on β-coefficients for TSS and from VIPs optimal wavelength windows using SNV pre-treatment for total flavonoid and total anthocyanin content. Thus, good prediction models were proposed in order to characterize grapes while reducing the data sets and limit the data storage to enable an industrial use.

Detecting Bacterial Biofilms Using Fluorescence Hyperspectral Imaging and Various Discriminant Analyses

Biofilms formed on the surface of agro-food processing facilities can cause food poisoning by providing an environment in which bacteria can be cultured. Therefore, hygiene management through initial detection is important. This study aimed to assess the feasibility of detecting Escherichia coli (E. coli) and Salmonella typhimurium (S. typhimurium) on the surface of food processing facilities by using fluorescence hyperspectral imaging. E. coli and S. typhimurium were cultured on high-density polyethylene and stainless steel coupons, which are the main materials used in food processing facilities. We obtained fluorescence hyperspectral images for the range of 420–730 nm by emitting UV light from a 365 nm UV light source. The images were used to perform discriminant analyses (linear discriminant analysis, k-nearest neighbor analysis, and partial-least squares discriminant analysis) to identify and classify coupons on which bacteria could be cultured. The discriminant performances of specificity and sensitivity for E. coli (1–4 log CFU·cm⁻²) and S. typhimurium (1–6 log CFU·cm⁻²) were over 90% for most machine learning models used, and the highest performances were generally obtained from the k-nearest neighbor (k-NN) model. The application of the learning model to the hyperspectral image confirmed that the biofilm detection was well performed. This result indicates the possibility of rapidly inspecting biofilms using fluorescence hyperspectral images.

State-of-the-Art of Analytical Techniques to Determine Food Fraud in Olive Oils

The benefits of the food industry compared to other sectors are much lower, which is why producers are tempted to commit fraud. Although it is a bad practice committed with a wide variety of foods, it is worth noting the case of olive oil because it is a product of great value and with a high percentage of fraud. It is for all these reasons that the authenticity of olive oil has become a major problem for producers, consumers, and legislators. To avoid such fraud, it is necessary to develop analytical techniques to detect them. In this review, we performed a complete analysis about the available instrumentation used in olive fraud which comprised spectroscopic and spectrometric methodology and analyte separation techniques such as liquid chromatography and gas chromatography. Additionally, other methodology including protein-based biomolecular techniques and analytical approaches like metabolomic, hhyperspectral imaging and chemometrics are discussed.

Sensing Technology for Fish Freshness and Safety: A Review

Standard analytical methods for fish freshness assessment are based on the measurement of chemical and physical attributes related to fish appearance, color, meat elasticity or texture, odor, and taste. These methods have plenty of disadvantages, such as being destructive, expensive, and time consuming. All these techniques require highly skilled operators. In the last decade, rapid advances in the development of novel techniques for evaluating food quality attributes have led to the development of non-invasive and non-destructive instrumental techniques, such as biosensors, e-sensors, and spectroscopic methods. The available scientific reports demonstrate that all these new techniques provide a great deal of information with only one test, making them suitable for on-line and/or at-line process control. Moreover, these techniques often require little or no sample preparation and allow sample destruction to be avoided.

Prediction of dry matter content of recently harvested 'Hass' avocado fruits using hyperspectral imaging

BACKGROUND

'Hass' avocado consumption is increasing due to its organoleptic properties, so it is necessary to develop new technologies to guarantee export quality. Avocado fruits do not ripen on the tree, and the visual classification of its maturity is not accurate. The most commonly used fruit maturity indicator is the percentage of dry matter (DM). The aim of this research was to investigate a non-destructive method with hyperspectral images to predict the percentage of DM of fruits across the spectral range of 400-1000 nm.

RESULTS

No correlation between fruit weight and color with the percentage of DM was found in the study area. Cross-validation efficiency of different data sources, including the spectrum extraction zone (the center, a line from the peduncle to the base, and the whole fruit) and the average of one or two fruit faces, was compared. Four linear regression models were compared. Data of the whole fruit and average of both sides per fruit using a support vector machine regression were selected for the prediction test. Following the cross-validation concept, five sets of calibration and test data were selected and optimized for calibration. The best test prediction set comprised an R² = 0.9, a root-mean-square error of 2.6 g kg^-1 DM, a Pearson correlation of 0.95, and a ratio of prediction to deviation of 3.2.

CONCLUSIONS

The results of the study indicate that hyperspectral images allow classifying export fruits and making harvesting decisions. © 2020 Society of Chemical Industry.

A dataset for evaluating blood detection in hyperspectral images

The sensitivity of imaging spectroscopy to haemoglobin derivatives makes it a promising tool for detecting blood. However, due to complexity and high dimensionality of hyperspectral images, the development of hyperspectral blood detection algorithms is challenging. To facilitate their development, we present a new hyperspectral blood detection dataset. This dataset, published under an open access license, consists of multiple detection scenarios with varying levels of complexity. It allows to test the performance of Machine Learning methods in relation to different acquisition environments, types of background, age of blood and presence of other blood-like substances. We have explored the dataset with blood detection experiments, for which we have used a hyperspectral target detection algorithm based on the well-known Matched Filter detector. Our results and their discussion highlight the challenges of blood detection in hyperspectral data and form a reference for further works.

Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review

In the last two decades, food scientists have attempted to develop new technologies that can improve the detection of insect infestation in fruits and vegetables under postharvest conditions using a multitude of non-destructive technologies. While consumers' expectations for higher nutritive and sensorial value of fresh produce has increased over time, they have also become more critical on using insecticides or synthetic chemicals to preserve food quality from insects' attacks or enhance the quality attributes of minimally processed fresh produce. In addition, the increasingly stringent quarantine measures by regulatory agencies for commercial import-export of fresh produce needs more reliable technologies for quickly detecting insect infestation in fruits and vegetables before their commercialization. For these reasons, the food industry investigates alternative and non-destructive means to improve food quality. Several studies have been conducted on the development of rapid, accurate, and reliable insect infestation monitoring systems to replace invasive and subjective methods that are often inefficient. There are still major limitations to the effective in-field, as well as postharvest on-line, monitoring applications. This review presents a general overview of current non-destructive techniques for the detection of insect damage in fruits and vegetables and discusses basic principles and applications. The paper also elaborates on the specific post-harvest fruit infestation detection methods, which include principles, protocols, specific application examples, merits, and limitations. The methods reviewed include those based on spectroscopy, imaging, acoustic sensing, and chemical interactions, with greater emphasis on the noninvasive methods. This review also discusses the current research gaps as well as the future research directions for non-destructive methods' application in the detection and classification of insect infestation in fruits and vegetables.

Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects

A novel object rotation hyperspectral imaging system with the wavelength range of 468–950 nm for investigating round-shaped fruits was developed. This system was used to obtain the reflection spectra of jujubes for the application of surface defect detection. Compared to the traditional linear scan system, which can scan about 49% of jujube surface in one scan pass, this novel object rotation scan system can scan 95% of jujube surface in one scan pass. Six types of jujube skin condition, including rusty spots, decay, white fungus, black fungus, cracks, and glare, were classified by using hyperspectral data. Support vector machine (SVM) and artificial neural network (ANN) models were used to differentiate the six jujube skin conditions. Classification effectiveness of models was evaluated based on confusion matrices. The percentage of classification accuracy of SVM and ANN models were 97.3% and 97.4%, respectively. The object rotation scan method developed for this study could be used for other round-shaped fruits and integrated into online hyperspectral investigation systems.

Detecting Green Mold Pathogens on Lemons Using Hyperspectral Images

Hyperspectral images in the spectral wavelength range of 500 nm to 650 nm are used to detect green mold pathogens, which are parasitic on the surface of lemons. The images reveal that the spectral range of 500 nm to 560 nm is appropriate for detecting the early stage of development of the pathogen in the lemon, because the spectral intensity is proportional to the infection degree. Within the range, it was found that the dominant spectral wavelengths of the fresh lemon and the green mold pathogen are 580 nm and 550 nm, respectively, with the 550 nm being the most sensitive in detecting the pathogen with spectral imaging. The spectral intensity ratio of the infected lemon to the fresh one in the spectral range of 500 nm to 560 nm increases with the increasing degree of the infection. Therefore, the ratio can be used to effectively estimate the degree of lemons infecting by the green mold pathogens. It also shows that the sudden decrease of the spectral intensity corresponding to the dominant spectral wavelength of the fresh lemon, together with the neighboring spectral wavelengths can be used to classify fresh and contaminated lemons. The spectral intensity ratio of discriminating the fresh lemon from the infected one is calculated as 1.15.

Convolutional neural network for hyperspectral data analysis and effective wavelengths selection

Fusion of spectral and spatial information has been proved to be an effective approach to improve model performance in near-infrared hyperspectral data analysis. Regardless, most of the existing spectral-spatial classification methods require fairly complex pipelines and exact selection of parameters, which mainly depend on the investigator's experience and the object under test. Convolutional neural network (CNN) is a powerful tool for representing complicated data and usually works with few "hand-engineering", making it an appropriate candidate for developing a general and automatic approach. In this paper, a two-branch convolutional neural network (2B-CNN) was developed for spectral-spatial classification and effective wavelengths (EWs) selection. The proposed network was evaluated by three classification data sets, including herbal medicine, coffee bean and strawberry. The results showed that the 2B-CNN obtained the best classification accuracies (96.72% in average) when compared with support vector machine (92.60% in average), one dimensional CNN (92.58% in average), and grey level co-occurrence matrix based support vector machine (93.83% in average). Furthermore, the learned weights of the two-dimensional branch in 2B-CNN were adopted as the indicator of EWs and compared with the successive projections algorithm. The 2B-CNN models built with wavelengths selected by the weight indicator achieved the best accuracies (96.02% in average) among all the examined EWs models. Different from the conventional EWs selection method, the proposed algorithm works without any additional retraining and has the ability to comprehensively consider the discriminative power in spectral domain and spatial domain.

Non-destructive classification of apple bruising time based on visible and near-infrared hyperspectral imaging

BACKGROUND

Bruising time of apple is one of the most important factors for internal quality assessment. The present study aimed to establish a non-destructive method for the classification of apple bruising time using visible and near-infrared (VNIR) hyperspectral imaging. In this study, VNIR hyperspectral images were obtained and analyzed at seven bruising periods. Moreover, regions of interest (ROIs) were chosen to construct the bruised region classification model, and spectra of bruised regions were collected and resampled based on four different methods. Subsequently, machine learning algorithms were employed and used for dealing with the time classification model of apples. In order to reduce data redundancy and improve the accuracy of the classification model, a tree-based assembling learning model was used to select feature wavelengths, and linear discriminant analysis (LDA) was used to improve the discernibility of data.

RESULTS

The results revealed that the random forest (RF) model can precisely locate bruised regions, while the gradient boosting decision tree (GBDT) model can validly classify apple bruising times with 70.59% accuracy. Data of 128 wavebands were compressed to 13 wavebands, providing a high accuracy of 92.86%.

CONCLUSION

The results prove that the hyperspectral technique can be used for predicting apple bruising time, which will help to assess the internal quality and safety of apples. © 2018 Society of Chemical Industry.

Organizational and Technological Aspects of a Platform for Collective Food Awareness

Can Internet-of-food technologies foster collective food awareness within a food consumer community? The paper contributes to answer this question in a fourfold aspect. Firstly, we model a cooperative process for generating and sharing reliable food information that is derived from food instrumental measurements performed by consumers via smart food things. Secondly, we outline the functional architecture of a platform capable to support such a process and to let a consumer community share reliable food information. Thirdly, we identify main entities and their attributes necessary to model the contextualized interaction between a consumer and the platform. Lastly, we review articles reviewing technologies capable of acquiring and quantifying food characteristics for food performances assessment. The purpose is to give an insight into current research directions on technologies employable in a platform for collective food awareness.

Hyperspectral imaging analysis of a photonic crystal bead array for multiplex bioassays

For multiplex bioassays, one effective strategy is to employ microfluidic chips based on an array of photonic crystal beads (PCBs) that are encoded by their characteristic reflection spectrum (CRS). In this paper, we report a hyperspectral imaging system and algorithms for the high throughput decoding of a PCB array and subsequent detection. The results showed that the decoding accuracy of up to ∼500 PCBs is 98.56% with an excellent ability to extract low-intensity fluorescence intensities. The results also demonstrated hyperspectral imaging techniques which can simultaneously obtain both spatial and spectral information as powerful tools in the analysis of multiplex bioassays or microfluidic chips.

Hyperspectral imaging: a novel approach for plant root phenotyping

BACKGROUND

Root phenotyping aims to characterize root system architecture because of its functional role in resource acquisition. RGB imaging and analysis procedures measure root system traits via colour contrasts between roots and growth media or artificial backgrounds. In the case of plants grown in soil-filled rhizoboxes, where the colour contrast can be poor, it is hypothesized that root imaging based on spectral signatures improves segmentation and provides additional knowledge on physico-chemical root properties.

RESULTS

Root systems of Triticum durum grown in soil-filled rhizoboxes were scanned in a spectral range of 1000-1700 nm with 222 narrow bands and a spatial resolution of 0.1 mm. A data processing pipeline was developed for automatic root segmentation and analysis of spectral root signatures. Spectral- and RGB-based root segmentation did not significantly differ in accuracy even for a bright soil background. Best spectral segmentation was obtained from log-linearized and asymptotic least squares corrected images via fuzzy clustering and multilevel thresholding. Root axes revealed major spectral distinction between center and border regions. Root decay was captured by an exponential function of the difference spectra between water and structural carbon absorption regions.

CONCLUSIONS

Fundamentals for root phenotyping using hyperspectral imaging have been established by means of an image processing pipeline for automated segmentation of soil-grown plant roots at a high spatial resolution and for the exploration of spectral signatures encoding physico-chemical root zone properties.

Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection

Hyperspectral imaging sensors are promising tools for monitoring crop plants or vegetation in different environments. Information on physiology, architecture or biochemistry of plants can be assessed non-invasively and on different scales. For instance, hyperspectral sensors are implemented for stress detection in plant phenotyping processes or in precision agriculture. Up to date, a variety of non-imaging and imaging hyperspectral sensors is available. The measuring process and the handling of most of these sensors is rather complex. Thus, during the last years the demand for sensors with easy user operability arose. The present study introduces the novel hyperspectral camera Specim IQ from Specim (Oulu, Finland). The Specim IQ is a handheld push broom system with integrated operating system and controls. Basic data handling and data analysis processes, such as pre-processing and classification routines are implemented within the camera software. This study provides an introduction into the measurement pipeline of the Specim IQ as well as a radiometric performance comparison with a well-established hyperspectral imager. Case studies for the detection of powdery mildew on barley at the canopy scale and the spectral characterization of Arabidopsis thaliana mutants grown under stressed and non-stressed conditions are presented.

Smart storage technologies applied to fresh foods: A review

Fresh foods are perishable, seasonal and regional in nature and their storage, transportation, and preservation of freshness are quite challenging. Smart storage technologies can online detection and monitor the changes of quality parameters and storage environment of fresh foods during storage, so that operators can make timely adjustments to reduce the loss. This article reviews the smart storage technologies from two aspects: online detection technologies and smartly monitoring technologies for fresh foods. Online detection technologies include electronic nose, nuclear magnetic resonance (NMR), near infrared spectroscopy (NIRS), hyperspectral imaging and computer vision. Smartly monitoring technologies mainly include some intelligent indicators for monitoring the change of storage environment. Smart storage technologies applied to fresh foods need to be highly efficient and nondestructive and need to be competitively priced. In this work, we have critically reviewed the principles, applications, and development trends of smart storage technologies.