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The development of aerial and ground-based hyperspectral and multispectral imaging equipment has been a major breakthrough in the expansion and practical application of precision agriculture techniques. This technology has made possible the assessment of crop stresses, characterization of soils and vegetative cover and yield estimation, in addition to its predictive capabilities. Spectral reflectance, measured by hyperspectral imaging equipment, is the amount of reflected light from a surface. Hyperspectral imaging is the process by which images are taken and numerical values spectral radiance assigned to each pixel, utilizing a range of wavelengths across the electromagnetic spectrum, including visible and infrared regions.

Through the use of specialized software and statistical analysis, these pixels are sorted and characterized to distinguish between groups of pixels or in the case of precision agriculture, plant characteristics and environmental conditions. Earlier remote sensing technology, in particular multispectral imaging, collects data at a few widely-spaced wavelengths. Each layer of the cube represents data at a specific wavelength. Ideal for lab, microscopy, and field applications.

The ability of hyperspectral imaging to provide valuable data on the condition and health of crops is predicated on the interaction and relationship between electromagnetic radiation EMR and foliage. EMR may be absorbed, transmitted or reflected and although the internal and external physical structure of vegetation affects this, the primary influences on EMR are the various photosynthetic pigments.

In the red and blue parts of the visible spectrum, reflectance is primarily a result of absorption by the photosynthetic pigments. Water content is the primary influence on reflectance in the mid-infrared MIR while reflectance in the near-infrared area NIR is influenced by the shape and condition of air spaces in the spongy mesophyll. One of the most powerful techniques for the measurement of overall photosynthetic efficiency and thus of plant productivity, is the fluorescence of chlorophyll a in photosystem II.

Hyperspectral Imaging Gets Stamp of Approval for Food Processing

The indexes produced give a good measure, however they are limited in their use by the need for the active excitation of photosynthesis by, for example, a saturating light pulse. Besides the photosynthetic pigments, reflectance is also influenced by the presence of zeaxanthin. This pigment is produced by plants to safely remove excess photons when light intensity exceeds the ability of photosystem II to absorb photons without becoming over-energized.

Zeaxanthin accumulation can therefore be used as a quantitative indicator of non-photochemical energy dissipation and therefore of light-use efficiency. By measuring changes at waveband nm, which is affected by the production of zeaxanthin and comparing it with waveband nm, which is not affected, a standard Photochemical Reflectance Index PRI has been developed which serves as a measure of photosynthetic light use efficiency. This index can be readily generated from hyperspectral imaging data. Of particular importance is a comparison with traditional spectrometric equipment to ensure the ability of hyperspectral imaging to deliver equivalent data to traditional equipment.

A study by Rascher et al. In this study it was demonstrated that PRI could provide measurements of both the biochemical adaptions to high light intensity and the gradual de-activation of photosynthesis during drying, making PRI monitoring by remote sensing a valuable methodology for drought investigations.

Hyperspectral remote sensing and its applications

The Rascher study relied on detached leaves, but the same methodology and instrumentation has been shown in the controlled conditions of Biosphere 2 to give effective data on whole vegetation-canopies. Above is an example of the picture processing and Photochemical Reflectance Index of four tropical leaves during the drying process as seen in the Rascher et al. The mask from C was used to show the leaf PRI values only. Drought is a significant factor in predicting crop yields and the final success of a crop.

Early detection of water related stresses in field crops can allow producers to identify specific areas for irrigation, saving water, energy, and time. Early detection might also allow producers to deliver water to crops before drought stress results in yield losses.

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Colombo et al. Using hyperspectral imaging they tested various models for estimating EWT at the canopy level in Italian poplar plantations and found suitable models giving error levels of only 2. They concluded that hyperspectral regression indices derived from hyperspectral imaging were strong tools for estimating water content at both leaf and landscape level.

Multispectral Imaging for Plant Food Quality Analysis and Visualization

Higher levels of stress do ultimately manifest themselves in changes in photosynthetic pigments. These changes lead to the familiar symptom of chlorosis when the reflectance of red wavelengths increases to equal that of green, producing the typical yellow colour. These changes are detected much earlier by hyperspectral imaging well before any change is visible to the human eye.

As discussed above, Rascher et al. That this stress-detection methodology could be applied to grain was demonstrated in trials of corn subjected to different water and nutrient regimes in field plots. Though the traits of leaf and canopy water stress were subtle, hyperspectral imaging technology could distinguish between treatments in both the controlled and field experiments. Under the field conditions, with variability of light and plot differences, the imaging technology could correctly characterize three out of the four treatment groups.

This demonstrated the suitability of hyperspectral imaging for early detection of drought-stress and nutrient-stress in practical agricultural conditions. Additionally, Rossini et al. They conducted a comparison of three irrigation conditions with airborne remote sensing equipment and found that they were able to accurately map irrigation deficits even before water stress affected the canopy structure. Field crops are not the only application of hyperspectral imaging, this technology can be used to assess water stress and scheduling of irrigation in turf grasses.

Jiang and Carrow examined the correlation of spectral reflectance and drought stress on turf grass turf quality and leaf firing. They screened 12 grasses and found that the reflectance models varied by cultivar, suggesting that species differences should be taken into account when using indexes. They also conclude that hyperspectral imaging might be useful in screening grasses for drought tolerance. Fungal pathogens cause serious losses to yields and quality of agricultural crops globally.

In the United States alone, plant pathogens are reported to cause economic losses of 33 billion annually. Conventional methods of detection rely on scouting and visual examination and often result in detection after the optimum time for control has passed. In addition to preventing individual producer losses, early detection will allow for the prevention of spread to neighboring fields or crops. Using diagnostic symptoms of pathogens such as changes in leaf pigments, leaf structure and moisture content, hyperspectral and multispectral imaging can aid in mapping fields for plant disease management.

For example, the fungal pathogen, Puccinia recondite, causes wheat rust characterized by small brown pustules on the leaf surface. In a greenhouse study, wheat plants were inoculated and then hyperspectral and multispectral imaging technologies were compared for their accuracy in distinguishing treated plants from non-treated plants. Franke et al. Compared to multispectral imaging, the higher spectral sensitivity of hyperspectral imaging produced superior detection at an earlier stage of development of the pathogen, when only slight visual symptoms were apparent.

Hyperspectral imaging

Tools Request permission Export citation Add to favorites Track citation. Share Give access Share full text access. Share full text access. Please review our Terms and Conditions of Use and check box below to share full-text version of article. Abstract The multispectral imaging technique is considered a reformation of hyperspectral imaging. Figure 1 Open in figure viewer PowerPoint. Chemometric Analysis Due to the large overlap and the complex nature of continuous data, it is not easy to clearly locate the positions of characteristic bands that represent the different components in plant foods.

Plant foods Quality parameters Optimal variable selection method Adopted wavelengths nm No. Physical attributes The food physical attributes normally include textural property, water binding capacity, and specific gravity. Figure 2 Open in figure viewer PowerPoint. Visualization of textural properties in terms of A hardness, B springiness, and C resilience of maize seeds Wang and others a.

Chemical attributes Soluble solids content SSC The total soluble solid TSS , including carbohydrates, organic acids, proteins, fats, and minerals, is known as the soluble solids content SSC that is a composite index of sweetness. Figure 3 Open in figure viewer PowerPoint. Visualization of A glucose, B fructose, and C sucrose contents in kiwi slices from unripe 1 d to ripe 16 d Hu and others Moisture content MC As a very basic and significant quality indicator, moisture content MC has great impacts on the overall quality of plant food products.

Antioxidants Antioxidants are substances that can inhibit the oxidation of other substances and delay some types of cell damage Lobo and others Figure 4 Open in figure viewer PowerPoint. Visualization of A insoluble dietary fiber and B soluble dietary fiber in celeries Yan and others Figure 5 Open in figure viewer PowerPoint.

Varietal authentication Variety identification plays a key role in plant breeding and plant food production. Gradation aspects Multispectral imaging was exploited for determining ripeness and storage periods of plant foods. Figure 6 Open in figure viewer PowerPoint. Visualization of browning levels of lychee fruits from low red color to high blue color Yang and others c. Contamination aspects The potential of multispectral imaging to identify contaminants of plant foods has also been investigated. Defect aspects With the multispectral imaging technology, defective plant foods could be rapidly separated from intact food products.


Figure 7 Open in figure viewer PowerPoint. Summary In general, the feasibility of multispectral imaging for the quality detection of various plant foods has been illustrated by empirical studies. Figure 8 Open in figure viewer PowerPoint. Frequency of using multispectral imaging for evaluations of different quality parameters in plant foods from to Figure 9 Open in figure viewer PowerPoint. Frequency of using different wavelength selection methods for quality evaluations of plant foods from to Figure 10 Open in figure viewer PowerPoint. Frequency of using different modeling methods for quality determinations of plant foods from to Conclusions This review article covers the current research status and prospects of multispectral imaging MSI technology for the rapid and noninvasive assessment of plant food quality.

Food Bioproc Technol 4 : — 6.

Hyperspectral imaging - Wikipedia

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How Does This Reduce Food Waste

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Su and Sun b. Liu and others Yan and others Zhang and others Yu and others Jin and others Su and Sun c. Pu and others Yang and others a. Xiong and others Hu and others Li and others a.

Guo and others Zhao and others Fan and others Dong and Guo Liu and others a. Sun and others Especially in recent years, hyperspectral imaging has attracted much research and development attention, as a result rapid scientific and technological advances have increasingly taken place in food and agriculture, especially on safety and quality inspection, classification and evaluation of a wide range of food products, illustrating the great advantages of using the technology for objective, rapid, non-destructive and automated safety inspection as well as quality control.

Therefore, as the first reference book in the area, Hyperspectral Imaging Technology in Food and Agriculture focuses on these recent advances. The book is divided into three parts, which begins with an outline of the fundamentals of the technology, followed by full covering of the application in the most researched areas of meats, fruits, vegetables, grains and other foods, which mostly covers food safety and quality as well as remote sensing applicable for crop production.

Hyperspectral Imaging Technology in Food and Agriculture is written by international peers who have both academic and professional credentials, with each chapter addressing in detail one aspect of the relevant technology, thus highlighting the truly international nature of the work. Therefore the book should provide the engineer and technologist working in research, development, and operations in the food and agricultural industry with critical, comprehensive and readily accessible information on the art and science of hyperspectral imaging technology.

It should also serve as an essential reference source to undergraduate and postgraduate students and researchers in universities and research institutions. JavaScript is currently disabled, this site works much better if you enable JavaScript in your browser. Food Engineering Series Free Preview.