Hyperspectral and multispectral imaging for evaluating food safety and quality

Qin, Jianwei; Chao, Kuanglin; Kim, Moon S.; Lu, Renfu; Burks, Thomas F.

doi:10.1016/j.jfoodeng.2013.04.001

Cited by 312 publications

(214 citation statements)

References 67 publications

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“…In spite of the large number of successful works that have been applied to agroforestry and related areas using low-cost passive imagery sensors-such as visible (RGB) and near infrared (NIR)-many applications require higher spectral fidelity that only multispectral and hyperspectral [10][11][12][13] sensors can offer. Both of the referred spectral-based methods consist of the acquisition of images where, for each of the image's spatially distributed elements, a spectrum of the energy reaching the respective sensor is measured.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, both multi-and hyperspectral imagery have the potential to take data mining to a whole new exploration level in many areas, including food quality assessment [11] and agriculture [12]. For instance, productivity and stress indicators in both agricultural and forest ecosystems can be assessed through photosynthetic light use efficiency quantification, which can be obtained by measuring the photochemical reflectance index (PRI) relying on narrowband absorbance of xanthophyll pigments at 531 and 570 nm [15].…”

Section: Introductionmentioning

confidence: 99%

“…On the one hand, it covers spectral detail that might pass unnoticeable in multispectral data due to its discrete and sparse nature. For example, in Figure 2, since red-edge (RE, 670-780 nm) is not accessible through the broadband sensor, leaf chlorophyll content, phenological state and vegetation stress-which are parameters that manifest in that spectral range- In fact, both multi-and hyperspectral imagery have the potential to take data mining to a whole new exploration level in many areas, including food quality assessment [11] and agriculture [12]. For instance, productivity and stress indicators in both agricultural and forest ecosystems can be assessed through photosynthetic light use efficiency quantification, which can be obtained by measuring the photochemical reflectance index (PRI) relying on narrowband absorbance of xanthophyll pigments at 531 and 570 nm [15].…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

et al. 2017

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Traditional imagery-provided, for example, by RGB and/or NIR sensors-has proven to be useful in many agroforestry applications. However, it lacks the spectral range and precision to profile materials and organisms that only hyperspectral sensors can provide. This kind of high-resolution spectroscopy was firstly used in satellites and later in manned aircraft, which are significantly expensive platforms and extremely restrictive due to availability limitations and/or complex logistics. More recently, UAS have emerged as a very popular and cost-effective remote sensing technology, composed of aerial platforms capable of carrying small-sized and lightweight sensors. Meanwhile, hyperspectral technology developments have been consistently resulting in smaller and lighter sensors that can currently be integrated in UAS for either scientific or commercial purposes. The hyperspectral sensors' ability for measuring hundreds of bands raises complexity when considering the sheer quantity of acquired data, whose usefulness depends on both calibration and corrective tasks occurring in pre-and post-flight stages. Further steps regarding hyperspectral data processing must be performed towards the retrieval of relevant information, which provides the true benefits for assertive interventions in agricultural crops and forested areas. Considering the aforementioned topics and the goal of providing a global view focused on hyperspectral-based remote sensing supported by UAV platforms, a survey including hyperspectral sensors, inherent data processing and applications focusing both on agriculture and forestry-wherein the combination of UAV and hyperspectral sensors plays a center role-is presented in this paper. Firstly, the advantages of hyperspectral data over RGB imagery and multispectral data are highlighted. Then, hyperspectral acquisition devices are addressed, including sensor types, acquisition modes and UAV-compatible sensors that can be used for both research and commercial purposes. Pre-flight operations and post-flight pre-processing are pointed out as necessary to ensure the usefulness of hyperspectral data for further processing towards the retrieval of conclusive information. With the goal of simplifying hyperspectral data processing-by isolating the common user from the processes' mathematical complexity-several available toolboxes that allow a direct access to level-one hyperspectral data are presented. Moreover, research works focusing the symbiosis between UAV-hyperspectral for agriculture and forestry applications are reviewed, just before the paper's conclusions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

et al. 2017

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“…Several review papers have been published ( Davies, [25], some of these papers are only based on traditional computer vision system. The others are focused on hyperspectral and multispectral imaging in quality (external, internal and safety) evaluation.…”

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confidence: 99%

Quality Inspection of Fruits and Vegetables using Colour Sorting in Machine Vision System: A review

Anish¹

2017

IJETED

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ABSTRACT:Quality is an important aspect in the trading and business of vegetable and fruits. The customer satisfaction and acceptance depends greatly on the external appearance of these food items. The qualities assessed include the size, shape, colour and texture. At present only manual inspection is being done to brand the quality of the product. Manual inspection is challenging in terms of the speed of inspection and the reliability of the output. Machine vision systems may be used as an object of quality evaluation. Various methods such as colour sorting and pattern recognition can be used to evaluate the quality of the fruit or vegetable. Machine vision systems provide a nondestructive, reliable, accurate and fast results regarding the external appearance of the fruit or vegetable. The application of lighting, cameras and image processing are being discussed here with an eye on the future developments possible.

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“…Although biosensors can provide rapid detection with satisfactory accuracy, they cannot satisfy the meat industry's need because of their destructive nature. Meanwhile, optical technologies have received wide attention in the research community because of their rapid, nondestructive, real-time, and precise detection ability (Gomez et al, 2006;Jamshidi et al, 2012;Lu and Peng, 2006;Pan et al, 2015;Peng and Lu, 2007;Qin et al, 2013;Xue et al, 2012;Zhang et al, 2014). Consequently, optical technologies are gaining importance in industrial automation (Li et al, 2011a;Mehta and Burks, 2014;Tong et al, 2013).…”

mentioning

confidence: 99%