Hyperspectral imaging (HSI) is an emerging platform technology that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Although HSI was originally developed for remote sensing, it has recently emerged as a powerful process analytical tool for non-destructive food analysis. This paper provides an introduction to hyperspectral imaging: HSI equipment, image acquisition and processing are described; current limitations and likely future applications are discussed. In addition, recent advances in the application of HSI to food safety and quality assessment are reviewed, such as contaminant detection, defect identification, constituent analysis and quality evaluation.

Hyperspectral imaging – an emerging process analytical tool for food quality and safety control

Image processing techniques have been applied increasingly for food quality evaluation in recent years. This paper reviews recent advances in image processing techniques for food quality evaluation, which include charge coupled device camera, ultrasound, magnetic resonance imaging, computed tomography, and electrical tomography for image acquisition; pixel and local pre-processing approaches for image pre-processing; thresholding-based, gradient-based, region-based, and classification-based methods for image segmentation; size, shape, colour, and texture features for object measurement; and statistical, fuzzy logic, and neural network methods for classification. The promise of image processing techniques for food quality evaluation is demonstrated, and some issues which need to be resolved or investigated further to expedite the application of image processing technologies for food quality evaluation are also discussed.

Recent developments in the applications of image processing techniques for food quality evaluation

Priming applied to commercial seed lots is widely used by seed technologists to enhance seed vigour in terms of germination potential and increased stress tolerance. Priming can be also valuable to seed bank operators who need improved protocols of ex situ conservation of germplasm collections (crop and native species). Depending on plant species, seed morphology and physiology, different priming treatments can be applied, all of them triggering the so-called ‘pre-germinative metabolism’. This physiological process takes place during early seed imbibition and includes the seed repair response (activation of DNA repair pathways and antioxidant mechanisms), essential to preserve genome integrity, ensuring proper germination and seedling development. The review provides an overview of priming technology, describing the range of physical–chemical and biological treatments currently available. Optimised priming protocols can be designed using the ‘hydrotime concept’ analysis which provides the theoretical bases for assessing the relationship between water potential and germination rate. Despite the efforts so far reported to further improve seed priming, novel ideas and cutting-edge investigations need to be brought into this technological sector of agri-seed industry. Multidisciplinary translational research combining digital, bioinformatic and molecular tools will significantly contribute to expand the range of priming applications to other relevant commercial sectors, e.g. the native seed market.

Seed priming: state of the art and new perspectives.

Hyperspectral imaging systems are starting to be used as a scientific tool for food quality assessment. A typical hyperspectral image is composed of a set of a relatively wide range of monochromatic images corresponding to continuous wavelengths that normally contain redundant information or may exhibit a high degree of correlation. In addition, computation of the classifiers used to deal with the data obtained from the images can become excessively complex and time-consuming for such high-dimensional datasets, and this makes it difficult to incorporate such systems into an industry that demands standard protocols or high-speed processes. Therefore, recent works have focused on the development of new systems based on this technology that are capable of analysing quality features that cannot be inspected using visible imaging. Many of those studies have also centred on finding new statistical techniques to reduce the hyperspectral images to multispectral ones, which are easier to implement in automatic, non-destructive systems. This article reviews recent works that use hyperspectral imaging for the inspection of fruit and vegetables. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of the internal and external features of these products. Particular attention is paid to the works aimed at reducing the dimensionality of the images, with details of the statistical techniques most commonly used for this task.

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Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment

This review article analyzes state-of-the-art and future perspectives for harvesting robots in high-value crops. The objectives were to characterize the crop environment relevant for robotic harvesting, to perform a literature review on the state-of-the-art of harvesting robots using quantitative measures, and to reflect on the crop environment and literature review to formulate challenges and directions for future research and development. Harvesting robots were reviewed regarding the crop harvested in a production environment, performance indicators, design process techniques used, hardware design decisions, and algorithm characteristics. On average, localization success was 85%, detachment success was 75%, harvest success was 66%, fruit damage was 5%, peduncle damage was 45%, and cycle time was 33 s. A kiwi harvesting robot achieved the shortest cycle time of 1 s. Moreover, the performance of harvesting robots did not improve in the past three decades, and none of these 50 robots was commercialized. Four future challenges with R&D directions were identified to realize a positive trend in performance and to successfully implement harvesting robots in practice: 1 simplifying the task, 2 enhancing the robot, 3 defining requirements and measuring performance, and 4 considering additional requirements for successful implementation. This review article may provide new directions for future automation projects in high-value crops.

Harvesting Robots for High-value Crops: State-of-the-art Review and Challenges Ahead

In this study, spectral images of five ripeness stages of tomatoes have been recorded and analyzed. The
electromagnetic spectrum between 396 and 736 nm was recorded in 257 bands (every 1.3 nm). Results show that spectral
images offer more discriminating power than standard RGB images for measuring ripeness stages of tomatoes. The
classification error of individual pixels was reduced from 51% to 19%. Using a gray reference, the reflectance can be made
invariant to the light source and even object geometry, which makes it possible to have comparable classification results over
a large range of illumination conditions. Experimental results show that, although the error rate increases from 19% to 35%
when using different light sources, it is still considerably below the 51% for RGB under a single light source.

Spectral Image Analysis for Measuring Ripeness of Tomatoes

https://ucanr.edu/datastoreFiles/608-1033.pdf

Measuring surface distribution of carotenes and chlorophyll in ripening tomatoes using imaging spectrometry

Fusarium head blight (FHB) is a harmful fungal disease that occurs in small grains. Non-destructive detection of this disease is traditionally done using spectroscopy or image processing. In this paper the combination of these two in the form of spectral imaging is evaluated. Transmission spectral images are recorded, both in the visible and near-infrared range from FHB infected wheat kernels. These images are analyzed, using light absorption, the relation between two wavelength bands, unsupervised fuzzy c-means clustering and supervised partial least squares regression. The reference method for training and validation is TaqMan real-time PCR. Results show that nearinfrared spectral images perform much better than spectral images in the visible range. Kernels with more than 6000 pg Fusarium DNA could clearly be identified. Above 100 pg it was possible to predict the amount of Fusarium with a Q2 of 0.8. This was both for Partial Least Squares regression (PLS) and a simple wavelength ratio. Also fuzzy c-means clustering shows a relation between amount of Fusarium and spectra. Fusarium head blight (FHB) is a harmful fungal disease that occurs in small grains. Non-destructive detection of this disease is traditionally done using spectroscopy or image processing. In this paper the combination of these two in the form of spectral imaging is evaluated. Transmission spectral images are recorded, both in the visible and near-infrared range from FHB infected wheat kernels. These images are analyzed, using light absorption, the relation between two wavelength bands, unsupervised fuzzy c-means clustering and supervised partial least squares regression. The reference method for training and validation is TaqMan real-time PCR. Results show that near-infrared spectral images perform much better than spectral images in the visible range. Kernels with more than 6000 pg Fusarium DNA could clearly be identified. Above 100 pg it was possible to predict the amount of Fusarium with a Q(2) of 0.8. This was both for Partial Least Squares regression (PLS) and a simple wavelength ratio. Also fuzzy c-means clustering shows a relation between amount of Fusarium and spectra.

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Detection of Fusarium in single wheat kernels using spectral Imaging

Summary: The latest developments in optics and sensors allow imaging spectrometry: the creation of hyperspectral images, i.e. images with wavelength-specific measurements over a large part of the spectrum. In this study, hyperspectral images of several stages of ripeness of tomatoes were recorded and analyzed. The electromagnetic spectrum between 450 and 850 nm was recorded in 80 bands (every 5 nm). Results show that hyperspectral images offer more discriminating power than standard RGB-images for discriminating ripeness stages of tomatoes, reducing the classification error of individual pixels from 40% to 7%. Using a reference tile, the images can be made invariant to the light source and even object geometry without losing discriminating power. This makes it possible to have comparable classification results under a large range of conditions.

Hyperspectral image analysis for measuring ripeness of tomatoes.

An image analysis system is described to study the process of imbibition in white cabbage (Brassica oleracea L.) seeds. The system consists of two components: 1) an environmental system where seeds germinate; 2) a computer imaging system, composed of a Charged Coupled Device (CCD)-imaging sensor, an image frame grabber, a computer and a video monitor. The measurement software can determine area, perimeter, width, length and eccentricity as descriptors of changes in seed size during swelling. The captured images of imbibing seeds allowed estimation of timing of ‘visible germination’ occurrence without any subjective evaluation. Using the area parameter, the imbibition process can be described graphically by a series of curves similar to the triphasic pattern of water uptake, with extension and rate depending on the degree of seed viability and germination medium conditions. Imbibitional osmotic stress, provided by -1.5 MPa mannitol, induced a large delay in germination and, upon stress removal, a more uniform radicle emergence. The versatility and the sensitivity of the method, together with the feasibility to investigate germination rate in individual seeds within a population, suggest that image analysis techniques have high potential in seed biology studies.

G.W.A.M. van der Heijden

Papers

Spectral Image Analysis for Measuring Ripeness of Tomatoes

Measuring surface distribution of carotenes and chlorophyll in ripening tomatoes using imaging spectrometry

Detection of Fusarium in single wheat kernels using spectral Imaging

Hyperspectral image analysis for measuring ripeness of tomatoes.

The application of image analysis in monitoring the imbibition process of white cabbage ( Brassica oleracea L.) seeds