Skip to main content Accessibility help
×
Home

Information:

  • Access
  • Cited by 9

Actions:

      • Send article to Kindle

        To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

        Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

        Find out more about the Kindle Personal Document Service.

        Assessment of seed quality using non-destructive measurement techniques: a review
        Available formats
        ×

        Send article to Dropbox

        To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

        Assessment of seed quality using non-destructive measurement techniques: a review
        Available formats
        ×

        Send article to Google Drive

        To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

        Assessment of seed quality using non-destructive measurement techniques: a review
        Available formats
        ×
Export citation

Abstract

Seed quality is of great importance in optimizing the cost of crop establishment. Rapid and non-destructive seed quality detection methods must therefore be developed for agriculture and the seed production industry. This review focuses primarily on non-destructive techniques, namely machine vision, spectroscopy, hyperspectral imaging, soft X-ray imaging, thermal imaging and electronic nose techniques, for assessing the quality of agricultural seeds. The fundamentals of these techniques are introduced. Seed quality, including chemical composition, variety identification and classification, insect damage and disease assessment as well as seed viability and germinability of various seeds are discussed. We conclude that non-destructive techniques are accurate detection methods with great potential for seed quality assessment.

Nomenclature

ADF

acid detergent fiber

ANNR

artificial neural network regression

ANN

artificial neural network

BPNN

back-propagation neural network

DA

discriminant analysis

DM

dry matter

ECVA

extended canonical variates analysis

FDA

factorial discriminant analysis

ICA

independent component analysis

iECVA

interval extended canonical variates analysis

iPLS-DA

interval partial least-squares discriminant analysis

iPLSR

interval partial least-squares regression

KNN

k-nearest neighbor

KPCA

kernel principal component analysis

KS

Kennard and Stone

LDA

linear discriminant analysis

LOD

limit of detection

LSD

least significance difference

LS-SVM

least-squares support vector machine

LS-SVMR

least-squares support vector machine regression

LW-PCA

locally weighted principal component analysis

MD

Mahalanobis distance

MDC

Mahalanobis distance classifier

MLMR

maximum likelihood multinomial regression

MLP

multilayer perceptron

MLR

multiple linear regression

MPLS

modified partial least-squares

MPLSR

modified partial least-squares regression

MSE

mean squared error

NDA

non-linear discriminant analysis

NNN

non-linear neural networks

OMD

organic matter digestibility

PCA

principal component analysis

PCR

principal component regression

PLS

partial least-squares

PLS-DA

partial least-squares discriminant analysis

PLSR

partial least-squares regression

QDA

quadratic discriminant analysis

RF

random forest

SAM

spectral angle mapper

SIMCA

soft independent modeling class analogy

SSC

soluble sugar content

SWI

single waveband image

SVDD

support vector machine description

RMSEP

root mean square error of prediction

R p

correlation coefficient of prediction

R

coefficient of correlation

R 2

coefficient of determination

R p 2

determination coefficient of prediction

R c 2

determination coefficient of calibration

SEP

standard error of prediction

RPD

ratio prediction to deviation

Introduction

Seed is a living product and must be grown, harvested and processed correctly to maximize its viability and subsequent crop productivity. Seed quality has a profound effect on the development and yield of a crop (Bradbeer, 1988). Good seed quality can increase yield significantly. Seed quality depends on the health, physiology, germinability and physical attributes of seeds, including the presence or absence of disease, chemical composition, insect infestation, and the presence or absence of weed seeds or other plant varieties. Quality of seeds and their products is directly or indirectly related to human health; nevertheless, the evaluation of seed quality parameters is a time-consuming process. For example, calculation of the germination percentage commonly requires manual counting and grading of germinating seedlings by experienced technicians. Therefore rapid, simple and accurate detection techniques must be developed for farmers and the agro-industry to ensure quality seed during seeding, growth, harvesting, storage and transport to consumers (Huang et al., 2015).

The sowing quality of seed is associated with the germination and growth conditions after sowing and depends on seed composition, kernel maturity, insect infestation, diseases, cleanliness and germination ability (Copeland and McDonald, 1999). The genetic purity of seeds may be detected by molecular identification, DNA analysis, isotope fingerprinting and mineral element analysis (Bradbeer, 1988). Protein electrophoresis, gas chromatography, high-performance liquid chromatography, tetrazolium tests, accelerated ageing and conductivity tests have been employed to evaluate the vigour and germination quality of seeds (Huang et al., 2015). Most of these chemical and physical techniques exhibit high accuracy and good reliability but have certain limitations, such as their high cost, long time requirements and high operator requirements. With the increasing demand for rapid, non-destructive and reliable techniques for evaluation of seed quality in the modern agro-industry, high-performance techniques must be developed for the evaluation of seed quality. A number of non-destructive testing technologies have been developed for evaluation of seed quality (Huang et al., 2015). These non-destructive testing technologies are rapid, accurate, reliable and simple methods for assessing the quality of seeds. This review focuses primarily on non-destructive techniques, namely, machine vision, spectroscopy, hyperspectral imaging, electronic nose, soft X-ray imaging and thermal imaging techniques, which have been used to assess seed quality parameters such as chemical composition, genetic purity and classification, disease and insect infestation, as well as vigour and germinability. The emphasis in this review is also placed on insights into the methods and techniques that have been investigated for evaluating seed qualities.

Non-destructive techniques for seed quality assessment

Machine vision

Machine vision, also known as ‘computer vision’ or ‘computer image processing’, is an artificial intelligence technique that simulates human vision (Huang et al., 2015). This technique is non-destructive, reliable and rapid and has been proven to be an effective and powerful technique for quality evaluation of food and agricultural products, particularly seeds (Hornberg, 2007). A typical machine vision system consists of four basic components: an illumination system, a sensor or camera, a lens and a computer with frame grabber/digitizer (Fig. 1). Most applications of machine vision address the visible spectrum (380–780 nm) (Gunasekaran et al., 1985). A machine vision system should be capable of identifying and grading seeds based on image external features, such as size, shape, colour and texture. The superiority, disadvantages and feasibility of different image external features should be simultaneously considered to select the most suitable feature for specific applications. Machine vision has already been used, with varying success, to assess seeds of a range of crop and non-crop species. This review focuses mainly on machine vision techniques that can be used to classify seed varieties, disease detection, identification of seed varieties, etc.

Figure 1. A typical machine vision system

Spectroscopy

Spectroscopy is used to investigate and measure the spectra produced when matter interacts with, or emits, electromagnetic radiation (Huang et al., 2015). A range of spectroscopic techniques, such as near-infrared- (NIR), mid-infrared- (MIR), fluorescence-, Fourier transform-infrared- (FT-IR) and Raman spectroscopy have been widely and successfully used as sensitive and fast analytical techniques for authentication and quality analysis of a variety of agricultural seeds (Fig. 2). NIR and MIR spectroscopy are based on molecular overtones and combined vibrations. FT-IR spectroscopy is a technique used to record infrared spectra and detect radiation in the MIR region. FT-IR spectroscopy is an information-rich analytical technique, as it provides a greater amount of chemical information regarding the scanned sample than NIR spectroscopy (Lohumi et al., 2015). Raman spectroscopy is another form of analytical spectroscopy that is suitable for quality and authenticity analysis of agro-food products. This technique can provide specific information needed for identification of sample matrices based on model compounds, such as lipids, proteins and carbohydrates, and is sensitive to minor components (Seo et al., 2016). This review focuses mainly on spectroscopic techniques that can be used to detect seed quality attributes, such as chemical composition, viability and damage by insects and other causes.

Figure 2. NIR, MIR or FT-IR spectroscopy (left panel) and Raman spectroscopy (right panel). From Seo et al. (2016).

Hyperspectral imaging

Hyperspectral imaging has recently emerged as a powerful analytical technique for food quality and authenticity analysis. This technique is used to acquire both spectral and spatial information from an object (Wu and Sun, 2013). A hyperspectral imaging system includes light sources, wavelength dispersion devices and detectors. As the centre of a hyperspectral imaging system, wavelength dispersion devices are used to disperse broadband light into different wavelengths (Fig. 3). The detector collects light, which carries useful information from the wavelength dispersion device and measures the intensity of the light by converting radiation energy into electrical signals (Huang et al., 2015). Using hyperspectral imaging, sample analysis is convenient and comparatively fast because a large number of samples are analysed at the same time, whereas spectroscopic methods analyse only one sample at a time (Lohumi et al., 2015). Machine vision and spectroscopy can only provide spatial or spectral information, whereas hyperspectral imaging, which integrates machine vision and spectroscopy advantages, can simultaneously obtain spatial and spectral information by using only one system. In this regard, hyperspectral imaging has been widely used by researchers to evaluate the exterior quality of seeds and predict their internal composition (Mahesh et al., 2011a; Zhu et al., 2011; Huang et al., 2014).

Figure 3. A typical hyperspectral reflectance/fluorescence imaging system. From Qin et al. (2013).

Thermal imaging

Thermal imaging is a technique for converting the invisible radiation pattern of an object into visible images for feature extraction and analysis without establishing contact with the object. Using this method, the surface temperature of any object can be mapped at a high resolution in two dimensions. The thermal data produced may be used directly or indirectly in many ways (Manickavasagan et al., 2008). The application of thermal imaging has gained popularity in the agro-food industry in recent years (Vadivambal and Jayas, 2011). The major advantage of thermal imaging is that it is a non-contact, non-invasive and rapid technique that can be used in online applications (Fig. 4). Thermal cameras are easy to handle and highly accurate temperature measurements are possible (Vadivambal and Jayas, 2011). Using thermal imaging, it is possible to obtain temperature mapping of any particular region of interest with fast response times, which is not possible with thermocouples or other temperature sensors that can only measure spot data. The repeatability of temperature measurements in thermal imaging is high (Ishimwe et al., 2014). In addition, thermal imaging does not require an illumination source, unlike other imaging systems. Nowadays, thermal imaging has a potential application in many operations involved in agriculture, starting from assessing seed quality, especially in detection of diseases, insects and seedling viability, estimating soil water status, estimating crop water stress, scheduling irrigation, determining disease and pathogen affected plants, estimating fruit yield and evaluating maturity of fruits and vegetables (Chelladurai et al., 2010; Manickavasagan et al., 2010; Vadivambal and Jayas, 2011). In spite of the fact that it could be used as a non-contact, non-destructive technique, it has some drawbacks in comparison with other imaging techniques because high resolution thermal imaging is costly and accurate thermal measurements depend on environmental and weather conditions. Thus it may not be possible to develop a universal methodology for its application in seed quality assessment.

Figure 4. A typical thermal imaging system. From Manickavasagan et al. (2010).

Soft X-ray imaging

Electromagnetic waves with wavelengths ranging from 1 to 100 nm (and energies of approximately 0.12 to 12 keV) are called soft X-rays. The low penetration power of these waves and their ability to reveal internal density changes make soft X-rays suitable for use in evaluating agricultural products (Neethirajan et al., 2007). Soft X-ray imaging is a well-known technique that takes a few seconds (3–5 s) to produce an X-ray image. Soft X-ray imaging has begun to be used in the seed industry to detect internal voids, defects, insect infestation and insect damage (Karunakaran et al., 2004; Neethirajan et al., 2006; Mathanker et al., 2013).

Electronic nose

An electronic nose is an instrument consisting of an array of electronic and chemical sensors with partial specificity and a pattern recognition system that is capable of recognizing simple or complex odours (Wilson and Baietto, 2009). These devices typically have arrays of sensors used to detect and distinguish odours precisely in complex samples and at low cost (Zhou et al., 2012). Electronic nose devices have been employed in a wide variety of applications, including classification of kernels and microbial pathogen detection.

Quality detection of seeds using non-destructive techniques

Quality assessment of seeds: chemical composition

In recent years, non-destructive sensing techniques, mainly spectroscopy and hyperspectral imaging, have been widely used to determine the internal composition of seeds (Table 1). Previous studies have shown that spectroscopy systems can be applied successfully to determine the protein contents of corn (Armstrong et al., 2011), maize (Baye et al., 2006), common beans (Hacisalihoglu et al., 2010), rice (Wu and Shi 2004), soybean (Ferreira et al., 2014), peanuts (Wang et al., 2012), jatropha (Vaknin et al., 2011), rapeseed (Velasco and Möllers, 2002), sunflower (Fassio and Cozzolino, 2004), canola (Daun et al., 1994), cotton (Huang et al., 2013), foxtail millet (Yang et al., 2013), flax, safflower, sesame and palm (Pandord et al., 1988). Previous studies have shown that spectroscopy is highly accurate in protein prediction. The coefficients of determination for prediction (R p 2) of a partial least-squares regression (PLSR) model have been found to be 0.98 for corn (Chen et al., 2014), 0.99 for rapeseed (Pandord et al., 1988), 0.96 for cottonseed (Huang et al., 2013), 0.98 for peanut (Pandord et al., 1988) and 0.91 for soybeans (Ferreira et al., 2014). Spectroscopy has also been used to estimate the fibre content of soybean, corn (Armstrong et al., 2011) and rapeseed (Wittkop et al., 2012; Bala and Singh, 2013;), and the sucrose content of soybean (Choung, 2010). However, unsatisfactory results have been reported for carbohydrate determination in maize (Baye and Becker 2004; Tallada et al., 2009), rice (Wu and Shi 2004), foxtail millet (Chen et al., 2013) and soybean (Choung 2010; Ferreira et al., 2013) and made the same conclusions in their study that any changes in the compositional amount among the sample are not translated into differences within the spectra. In recent research, hyperspectral imaging has been used to predict crude protein and crude fat fractions in soybean (Zhu et al., 2011), protein in wheat (Mahesh et al., 2011a) and alpha-amylase activity in wheat (Xing et al., 2009, 2011). Unsatisfactory prediction results have been obtained in some cases using hyperspectral imaging because of the difficulty of extracting the most important object features for assessing the physical structure and chemical composition of samples. The oil content is an important parameter in the internal quality evaluation of most oilseed crops. Spectroscopy within the range of 400–2500 nm has been widely used to determine oil content in peanuts (Sundaram et al., 2010), maize (Tallada et al., 2009), safflower (Rudolphi et al., 2012), rapeseed (Velasco and Becker, 1998; Velasco et al., 1999; Petisco et al., 2010), sunflower (Pandord et al., 1988; Pérez-Vich et al., 1998; Fassio and Cozzolino, 2004), jatropha (Vaknin et al., 2011), canola (Daun et al., 1994), cotton (Huang et al., 2013), corn and soybean (Armstrong et al., 2011). The coefficients of determination of the oil prediction model were 0.99, 0.91, 0.98, 0.92, 0.95, 0.98, 0.95, 0.87 and 0.84 for peanut, safflower, rapeseed, sunflower, jatropha, canola, cotton, corn and soybean, respectively. Hyperspectral imaging has also been used to predict the oil and oleic acid concentrations in corn (Weinstock et al., 2006). An NIR hyperspectral imaging system (750–1090 nm) was used to predict the oil content in maize and the determination coefficient of the PLSR model for the determination of oil content was found to be 0.75 (Cogdill et al., 2004). The results indicated outstanding performance of the non-destructive technique in the prediction of the internal composition of the seed. Spectroscopy has also been used to determine the fatty acid content of peanuts (Sundaram et al., 2010), soybean (Patil et al., 2010), safflower (Rudolphi et al., 2012), rapeseed (Kim et al., 2007), sunflower (Cantarelli et al., 2009), jatropha (Vaknin et al., 2011), canola and flax (Siemens and Daun, 2005) with high accuracy. The amino acid composition of seeds is also a concern in their quality assessment since high protein content and a rational amino acid composition of seed are a major concern to the plant breeder (Chen et al., 2011). Studies have shown that near-infrared spectroscopy (NIRS) and FT-NIRS can be used successfully in the assessment of amino acid composition in rapeseed (Pandord et al., 1988; Chen et al., 2011), peanuts (Wang et al., 2012), rice (Zhang et al., 2011) and foxtail millet (Yang et al., 2013). An experiment in high-resolution hyperspectral reflectance imagery in the near-infrared region (960–1700 nm) was conducted to predict the amino acid content of fresh soybeans and showed that the best predictions (MSE = 0.305, R = 0.611) were obtained using a non-linear artificial neural network (ANN)-based regression model based on the second-derivative spectra data produced for the nitrogen concentration (Monteiro et al., 2007). Spectroscopy has also been used to determine the moisture content of soybean (Pandord et al., 1988; Ferreira et al., 2013; Ferreira et al., 2014), sunflower (Pandord et al., 1988; Fassio and Cozzolino, 2004), peanuts (Sundaram et al., 2010), flax, safflower and cotton (Pandord et al., 1988), as well as the pH of cocoa beans (Sunoj et al., 2016), the mineral contents (K, Mg, Ca and P) of peanuts (Phan-Thien et al., 2011), the seed weight of rapeseed (Velasco et al., 1999), the grain weight of rice and brown rice (Wu and Shi, 2004), the ethanol content of maize (Hao et al., 2012), the phenol content of rapeseed (Bala and Singh, 2013) and the polyphenol content of cocoa beans (Sunoj et al., 2016). In recent years, hyperspectral imaging has been used to predict the moisture content of corn (Cogdill et al., 2004; Mahesh et al., 2011b) and soybean during drying (Huang et al., 2014), the sweetness (sucrose, glucose and fructose contents) of soybean (Monteiro et al., 2007) and the colour of soybeans during drying (Huang et al., 2014).

Table 1. Assessment of chemical composition in seeds using different non-destructive techniques

Quality assessment of seeds: insect damage and diseases

Seed damage by insects, fungi or natural causes, such as germination, are an important factor in seed quality during storage and processing. Seed damage is therefore taken seriously by consumers and the food industry. Various non-destructive techniques such as machine vision, spectroscopy, hyperspectral imaging, soft X-ray imaging, electronic nose and thermal imaging have been widely used in the detection of insect damage, insect infestation and diseases in seeds (Table 2). Machine vision has been used together with back-propagation neural networks based on colour features to detect external defects in rice seeds, such as germs, diseases and incompletely closed glumes, with an accuracy of 98.6–99.2% (Cheng et al., 2006). A machine vision system developed for the detection of damaged wheat kernels based on morphological and textural properties was shown to have a classification accuracy of 91–94% (Delwiche et al., 2013). A machine vision system was also used to detect damaged soybeans based on colour features with an accuracy of 99.6% (Shatadal and Tan, 2003). Recently, spectroscopy has been used to identify defects in corn (Esteve Agelet et al., 2012) and soybean (Sirisomboon et al., 2009). Hyperspectral imaging has been used to detect sprout damage in wheat (Singh et al., 2009a; Xing et al., 2010) and to detect sprouting in barley (Arngren et al., 2011). In a recent study, a machine vision system was used to detect diseases and insects for the purpose of quality sorting of areca nuts with an accuracy of 90.9% (Huang, 2012). Spectroscopy-based methods have also been used to detect and classify fungus-infected maize (Giacomo and Stefania, 2013), wheat (Soto-Cámara et al., 2012) and soybeans (Wang et al., 2004), to determine the percentage of fungal infection in rice (Sirisomboon et al., 2013) and to identify the green mottle mosaic virus in cucumber (Lee et al., 2016). However, this technique has yielded unsatisfactory results for fungal infection determination in rice because the moisture and starch contents in rice affect the overall extent of fungal infection (Sirisomboon et al., 2013). Numerous studies have been conducted using hyperspectral imaging to detect fungal-infected wheat (Singh et al., 2012) and maize (Del Fiore et al., 2010; Williams et al., 2012; Yao et al., 2013) and to detect bacteria-infected watermelon seeds (Lee et al., 2016). One study showed that the electronic nose is a powerful tool for the detection of fungal contamination in wheat; the accuracy obtained using partial least-squares discriminant analysis (PLS-DA) was found to be 85.3% (Paolesse et al., 2006). Recently, chlorophyll fluorescence has been used to sort white cabbage seeds, resulting in 97% germination by removing 13.2% of the seeds with very high chlorophyll fluorescence signal from the seed lot (Jalink et al., 1998). Similar studies have been conducted to evaluate the seed maturity in cabbage (Dell'Aquila et al., 2002), tomato (Jalink et al., 1999), barley (Konstantinova et al., 2002), carrot (Groot et al., 2006) and pepper (Kenanoglu et al., 2013) using chlorophyll fluorescence. Thermal imaging has been used to detect fungal infestations in stored wheat using linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA), with an accuracy of 100% for healthy samples and 96–97% for infected samples (Chelladurai et al., 2010). In a study in which a hyperspectral imaging system (1100–1700 nm) was used to detect aflatoxin B1 (AFB1) contaminants on corn kernels, a PLS-DA was performed, and a minimum classification accuracy of 96.9% was achieved (Kandpal et al., 2015). Similar studies have been performed to detect AFB1 contaminants on the surfaces of healthy maize kernels using a short wavelength infrared (SWIR) hyperspectral imaging system (Wang et al., 2014). The feasibility of short-wave near-infrared hyperspectral (700–1100 nm wavelength range) and digital colour imaging with different statistical discriminant classifiers was investigated for use in the detection of wheat damaged by four different insect species: the rice weevil (Sitophilus oryzae), the lesser grain borer (Rhyzopertha dominica), the rusty grain beetle (Cryptolestes ferrugineus) and the red flour beetle (Tribolium castaneum). Accuracies of 96% were achieved for healthy wheat kernels and 91–100% for insect-damaged wheat kernels (Singh et al., 2010a). Similarly, numerous studies have been performed to detect insect-damaged (Singh et al., 2009a, 2009b, 2010a, 2010b; Serranti et al., 2013) and mildew-damaged (Shahin et al., 2014) wheat using hyperspectral imaging. Hyperspectral imaging has also been used to detect insect-damaged mung bean (Kaliramesh et al., 2013) and insect fragments in semolina (Bhuvaneswari et al., 2011) and soybean (Huang et al., 2013; Chelladurai et al., 2014). Soft X-ray imaging technology has been used to detect red flour beetle infestation in wheat. An accuracy of 86% was achieved using textural features with a back-propagation neural network (BPNN) classifier (Karunakaran et al., 2004b). Soft X-ray imaging has also been used to detect internal wheat seed infestation by insects (Karunakaran et al., 2004a) and bug damage in soybean seeds (Pinto et al., 2009). In a recent study, thermal imaging was used to detect insect infestation in wheat with an accuracy of 77.6% for infested seeds and 83% for healthy seeds (Manickavasagan et al., 2008). A recent study has shown that multispectral imaging can be used for spinach seeds to discriminate uninfected seeds from infected seeds with 80–100% classification rate (Olesen et al., 2011).

Table 2. Assessment of insect damages and diseases in seeds using different non-destructive techniques

Quality assessment of seeds: variety identification and classification

Variety identification and classification of seed species using non-destructive techniques has been extensively investigated by researchers worldwide (Table 3). Machine vision has been used to identify four wheat varieties using morphological features and colour features with an accuracy of 95.86%, which suggests that morphological features are more effective than colour features in recognizing wheat varieties (Arefi et al., 2011). Machine vision has also been used to classify seeds of various species using morphological, colour, textural and wavelet features and to distinguish among species of wheat, barley, oats and rye (Choudhary et al., 2008) and between wheat and barley (Guevara-Hernandez and Gomez-Gil, 2011). Similarly, machine vision has been used to identify nine Iranian wheat seeds based on their varieties, using textural features, with an accuracy of 98.15% (Pourreza et al., 2012) and to recognize five Chinese corn varieties based on their external features (Chen et al., 2010). Machine vision has also been used to identify bean varieties (Venora et al., 2009), discriminate among wheat grain varieties (Zapotoczny, 2011a, 2011b), identify wheat varieties (Zayas et al., 1986; Dubey et al., 2006), classify corn (Jingtao et al., 2012; Pazoki et al., 2013), discriminate among rapeseed varieties (Li et al., 2007; Kurtulmuş and Ünal 2015), classify pepper seeds (Kurtulmuş et al., 2016) and classify rice varieties (Rad et al., 2011; Hong et al., 2015). Accuracy is an important evaluation parameter in variety identification; most of these studies have reported highly accurate results, in the range of 85–100%. In addition, machine vision has been shown to exhibit an overall accuracy of greater than 80% in grading maize (Yi et al., 2007; Wu et al., 2013) and soybean (Kılıç et al., 2007). Recently, an electronic nose was used to distinguish among varieties of wheat seeds with an accuracy of 100% (Zhou et al., 2012). Thermal imaging was used in a recent study to identify eight western Canadian wheat varieties. The overall classification accuracies of eight-class model, red-class model (four classes), white-class model (four classes), and pairwise (two-class) model comparisons obtained using a quadratic discriminant method were 76, 87, 79 and 95%, respectively, and those obtained using bootstrap and leave-one-out validation methods were 64, 87, 77 and 91%, respectively (Manickavasagan et al., 2010). Hyperspectral imaging systems have been used for accurate and reliable discrimination among varieties of maize seeds (Zhang et al., 2012), for classification of four varieties of maize seeds in different years (Huang et al., 2016), for identification of wheat varieties (Choudhary et al., 2009; Zhu et al., 2012), for differentiation of wheat classes grown in western Canada (Mahesh et al., 2008) and for differentiation among varieties of rice (Kong et al., 2013). Some of these applications have achieved a classification accuracy of 100%. Hyperspectral imaging has also been used by several researchers for hardness classification of maize (Williams et al., 2009; McGoverin et al., 2011). Recently, hyperspectral imaging has been used to distinguish among transgenic soybeans (Esteve Agelet et al., 2012) and rice (Liu et al., 2014). Similarly, a NIRS technique has been used to distinguish among herbicide-resistant genetically modified soybean seeds (Lee and Choung, 2011). It has also been demonstrated that multispectral imaging technique can be used to distinguish transgenic- from non-transgenic rice seeds (Liu et al., 2014).

Table 3. Assessment of variety identification and classification in seeds using different non-destructive techniques

Quality assessment of seeds: seed viability

A good-quality seed is one that is capable of germination under various conditions. A non-viable seed is one that fails to germinate even under optimal conditions (Bradbeer, 1988). In recent years, non-destructive techniques, mainly spectroscopy and hyperspectral imaging, have been widely used to predict seed viability (Table 4). A machine vision system was used to predict alfalfa and sativa seed germinability using the RGB (red, green, blue) density value with correlation coefficients of 0.982 and 0.984 for alfalfa and sativa, respectively (Behtari et al., 2014). Researchers have also studied soybean and snap bean seed germinability using electric impedance spectroscopy in the frequency range of 60 Hz to 8 MHz (Vozáry et al., 2007). Recently, spectroscopy has been used to distinguish viable gourd (Min and Kang, 2003), cucumber (Mo et al., 2012), patula pine (Tigabu and Odén, 2003), watermelon and pepper seeds (Lohumi et al., 2013; Seo et al., 2016) from their non-viable counterparts, to assess corn seed viability (Ambrose et al., 2016) and to predict the viability of cabbage and radish seeds (Shetty et al., 2011). Most of these studies have reported accuracies of more than 90% in viable seed identification. Hyperspectral imaging systems have also been used for accurate and reliable discrimination of viable and non-viable seeds of corn (Ambrose et al., 2016), radish (Ahn et al., 2012), watermelon (Bae et al., 2016) and pepper (Mo et al., 2014) with accuracies of 95.6, 95, 84.2 and 99.4%, respectively. Recently, a hyperspectral fluorescence imaging technique was used to extract the fluorescence spectra of cucumber seeds in the 425–700 nm range to discriminate between viable and non-viable cucumber seeds using four types of algorithms. The discrimination accuracies achieved based on the subtraction image, the ratio image and the ratio-subtraction image were 100 and 99.0% for viable and non-viable seeds, respectively (Mo et al., 2015). Hyperspectral imaging has also been used to classify muskmelon seeds based on germination ability with an accuracy of 94.6%, using a PLS-DA classification algorithm (Kandpal et al., 2016). Hyperspectral imaging in the range of 1000–2498 nm was able to predict the viability of barley, wheat and sorghum seed with correlation coefficients of 0.85, 0.92 and 0.87, respectively (McGoverin et al., 2011). Recently, multispectral imaging has been demonstrated to be a potential technique to evaluate castor seed viability with 96% correct classification rate at 19 different wavelengths ranging from 375 to 970 nm (Olesen et al., 2015). Other studies have been conducted, using multispectral imaging to examine germination ability and germ length in spinach seeds; with the use of PLS-DA of images of spinach seeds it was possible to classify large spinach seeds from small-sized and medium-sized seeds (Shetty et al., 2012). Infrared thermography has also been used to predict whether a quiescent seed will germinate or die upon water uptake, and the technique was reported to be able to detect imbibition- and germination-associated biophysical and biochemical changes (Kranner et al., 2010). A similar technique has been used for viability evaluation of lettuce seeds (Kim et al., 2013) and to evaluate germination capacity of leguminous plant seeds (Baranowski et al., 2003).

Table 4. Assessment of seed viability using different non-destructive techniques

Summary and future trends

This paper provided an overview of previous studies on seed quality assessment using non-destructive measurement techniques, namely chemical composition (Table 1), insect damage and diseases (Table 2), variety identification and classification (Table 3) and viability (Table 4). Machine vision, spectroscopy, hyperspectral imaging, thermal imaging, electronic nose and soft X-ray imaging are the main techniques to determine seed quality. Among them, spectroscopy and hyperspectral imaging techniques for chemical composition, machine vision, hyperspectral imaging, spectroscopy and soft X-ray imaging for insect and diseases detection, machine vision, thermal imaging and hyperspectral imaging for seed variety identification and classification, and spectroscopy and hyperspectral imaging for viability of seeds has been widely used in research, quality assessment, and for industrial purposes. For this, numerous spectroscopy instruments are commercially available. However, most of the instruments are too expensive to be widely used in practical production. Therefore, one of the main concerns of current researchers is how to decrease the cost while maintaining accuracy of analysis. In contrast, hyperspectral imaging provides both spatial and spectral information and is suitable for both external quality classification and for prediction of internal chemical composition. However, current hyperspectral imaging technology is not widely used compared with spectroscopy. This limitation may be due to the time-consuming process of hyperspectral imaging to generate a hypercube and the large amount of hyperspectral data. As a new technology that has only been studied for over a decade, hyperspectral imaging has a long way to go before it can be moved from laboratories to practical application. Recently, machine vision techniques have been placed as in-line detection and grading systems in actual production. Generally, a complete detection process for machine vision technique includes image acquisition, image processing and analysis, and formulation of decisions. These steps can be accomplished with only one smart camera, considering the increasing development of electronics and microprocessors. Thermal imaging and soft X-ray imaging are of very limited use in seed quality assessment due to high cost, the requirement of a controlled environment as the precision of this instrument fluctuates with environmental condition. The electronic nose technique is commonly used to determine seed quality during storage because it detects chemical interactions between the substrates over the gas sensors and the aromatic compounds. Electronic noses today generally suffer from significant weaknesses which limit their widespread application in seed quality assessment. Their sensing ability is profoundly influenced by ambient factors that are very critical in seed quality assessment. We should address the rapid development of instruments coupled with the improvement of analysis algorithms to help to promote efficient technologies for the seed quality assessment field.

Conclusions

This paper presents an overview of studies that have shown that non-destructive techniques can be used effectively as reliable and accurate tools for the composition prediction, variety identification and classification, quality grading, damage detection, insect infestation detection and viability and germinability prediction of agricultural seeds. These non-destructive techniques are rapid, accurate, reliable and simple tools for quality assessment of seeds. Given the urgent need of the industry for advanced testing methods and rapid development of suitable technologies and instruments, non-destructive techniques exhibit great potential to be dominant methods for quality assessment of seeds.

Acknowledgements

None.

Financial support

This research was partially supported by the Export Strategy Technology Development Program, Ministry of Agriculture, Food and Rural Affairs (MAFRA) and by Golden Seed Project, MAFRA, Ministry of Oceans and Fisheries (MOF), Rural Development Administration (RDA) and Korea Forest Service (KFS), Republic of Korea.

Conflicts of interest

None.

References

Ahn, C.K., Mo, C.Y., Kang, J.-S. and Cho, B.-K. (2012) Non-destructive classification of viable and non-viable radish (Raphanus sativus L.) seeds using hyperspectral reflectance imaging. Journal of Biosystems Engineering 37, 411419.
Ambrose, A., Lohumi, S., Lee, W.-H.H. and Cho, B.K. (2016a) Comparative non-destructive measurement of corn seed viability using Fourier transform near-infrared (FT-NIR) and Raman spectroscopy. Sensors and Actuators B: Chemical 224, 500506.
Ambrose, A., Kandpal, L.M., Kim, M.S., Lee, W.-H. and Cho, B.-K. (2016b) High speed measurement of corn seed viability using hyperspectral imaging. Infrared Physics & Technology 75, 173179.
Arefi, A., Motlagh, A.M. and Teimourlou, R.F. (2011) Wheat class identification using computer vision system and artificial neural networks. International Agrophysics 25, 319323.
Armstrong, P.R., Tallada, J.G., Hurburgh, C.R., Hildebrand, D.F. and Specht, J.E. (2011) Development of single-seed near-infrared spectroscopic predictions of corn and soybean constituents using bulk reference values and mean spectra. Transactions of the ASABE 54, 15291535.
Arngren, M., Hansen, P.W., Eriksen, B., Larsen, J. and Larsen, R. (2011) Analysis of pregerminated barley using hyperspectral image analysis. Journal of Agricultural and Food Chemistry 59, 1138511394.
Bae, H., Seo, Y.-W., Kim, D.-Y., Lohumi, S., Park, E. and Cho, B.-K. (2016) Development of non-destructive sorting technique for viability of watermelon seed by using hyperspectral image processing. Journal of the Korean Society for Non-destructive Testing 36, 3544.
Bala, M. and Singh, M. (2013) Non-destructive estimation of total phenol and crude fiber content in intact seeds of rapeseed–mustard using FTNIR. Industrial Crops and Products 42, 357362.
Baranowski, P., Mazurek, W. and Walczak, R.T. (2003) The use of thermography for pre-sowing evaluation of seed germination capacity. Acta Horticulturae 604, 459465.
Bauriegel, E., Giebel, A., Geyer, M., Schmidt, U. and Herppich, W.B.B. (2011) Early detection of Fusarium infection in wheat using hyper-spectral imaging. Computers and Electronics in Agriculture 75, 304312.
Baye, T. and Becker, H.C. (2004) Analyzing seed weight, fatty acid composition, oil, and protein contents in Vernonia galamensis germplasm by near-infrared reflectance spectroscopy. Journal of the American Oil Chemists’ Society 81, 641645.
Baye, T.M., Pearson, T.C. and Settles, A.M. (2006) Development of a calibration to predict maize seed composition using single kernel near infrared spectroscopy. Journal of Cereal Science 43, 236243.
Behtari, B., De Luis, M. and Dabbagh Mohammadi Nasab, A. (2014) Predicting germination of Medicago sativa and Onobrychis viciifolia seeds by using image analysis. Turkish Journal of Agriculture and Forestry 38, 615623.
Bhuvaneswari, K., Fields, P.G., White, N.D.G., Sarkar, A.K., Singh, C.B. and Jayas, D.S. (2011) Image analysis for detecting insect fragments in semolina. Journal of Stored Products Research 47, 2024.
Bradbeer, J.W. (1988) Seed Dormancy and Germination. Boston, MA, Springer US).
Cantarelli, M.A., Funes, I.G., Marchevsky, E.J. and Camiña, J.M. (2009) Determination of oleic acid in sunflower seeds by infrared spectroscopy and multivariate calibration method. Talanta 80, 489492.
Chelladurai, V., Jayas, D.S. and White, N.D.G. (2010) Thermal imaging for detecting fungal infection in stored wheat. Journal of Stored Products Research 46, 174179.
Chelladurai, V., Karuppiah, K., Jayas, D.S., Fields, P.G. and White, N.D.G. (2014) Detection of Callosobruchus maculatus (F.) infestation in soybean using soft X-ray and NIR hyperspectral imaging techniques. Journal of Stored Products Research 57, 4348.
Chen, G.L., Zhang, B., Wu, J.G. and Shi, C.H. (2011) Non-destructive assessment of amino acid composition in rapeseed meal based on intact seeds by near-infrared reflectance spectroscopy. Animal Feed Science and Technology 165, 111119.
Chen, H., Ai, W., Feng, Q., Jia, Z. and Song, Q. (2014) FT-NIR spectroscopy and Whittaker smoother applied to joint analysis of duel-components for corn. Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy 118, 752759.
Chen, J., Ren, X., Zhang, Q., Diao, X. and Shen, Q. (2013) Determination of protein, total carbohydrates and crude fat contents of foxtail millet using effective wavelengths in NIR spectroscopy. Journal of Cereal Science 58, 241247.
Chen, X., Xun, Y., Li, W. and Zhang, J. (2010) Combining discriminant analysis and neural networks for corn variety identification. Computers and Electronics in Agriculture 71, S48S53.
Cheng, F., Ying, Y.B. and Li, Y.B. (2006) Detection of defects in rice seeds using machine vision. Transactions of the ASABE 49, 19291934.
Choudhary, R., Paliwal, J. and Jayas, D.S. (2008) Classification of cereal grains using wavelet, morphological, colour, and textural features of non-touching kernel images. Biosystems Engineering 99, 330337.
Choudhary, R., Mahesh, S., Paliwal, J. and Jayas, D.S. (2009) Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples. Biosystems Engineering 102, 115127.
Choung, M.-G. (2010) Determination of sucrose content in soybean using near-infrared reflectance spectroscopy. Journal of the Korean Society for Applied Biological Chemistry 53, 478484.
Cogdill, R.P., Hurburgh, C.R., Rippke, G.R., Bajic, S.J., Jones, R.W., McClelland, J.F., Jensen, T.C. and Liu, J. (2004) Single-kernel maize analysis by near-infrared hyperspectral imaging. Transactions of the ASAE 47, 311320.
Copeland, L.O. and McDonald, M.B. (1999) Principles of Seed Science and Technology. Boston, MA, Springer US).
Daun, J.K., Clear, K.M. and Williams, P. (1994) Comparison of three whole seed near-infrared analyzers for measuring quality components of canola seed. Journal of the American Oil Chemists’ Society 71, 10631068.
Dell'Aquila, A., van der Schoor, R. and Jalink, H. (2002) Application of chlorophyll fluorescence in sorting controlled deteriorated white cabbage (Brassica oleracea L.) seeds. Seed Science and Technology 30, 689695.
Delwiche, S.R., Kim, M.S. and Dong, Y. (2011) Fusarium damage assessment in wheat kernels by Vis/NIR hyperspectral imaging. Sensing and Instrumentation for Food Quality and Safety 5, 6371.
Delwiche, S.R., Yang, I.-C. and Graybosch, R.A. (2013) Multiple view image analysis of freefalling U.S. wheat grains for damage assessment. Computers and Electronics in Agriculture 98, 6273.
Dubey, B.P., Bhagwat, S.G., Shouche, S.P. and Sainis, J.K. (2006) Potential of artificial neural networks in varietal identification using morphometry of wheat grains. Biosystems Engineering 95, 6167.
Esteve Agelet, L., Ellis, D.D., Duvick, S., Goggi, A.S., Hurburgh, C.R. and Gardner, C.A. (2012a) Feasibility of near infrared spectroscopy for analyzing corn kernel damage and viability of soybean and corn kernels. Journal of Cereal Science 55, 160165.
Esteve Agelet, L., Gowen, A.A., Hurburgh, C.R. and O'Donell, C.P. (2012b) Feasibility of conventional and Roundup Ready® soybeans discrimination by different near infrared reflectance technologies. Food Chemistry 134, 11651172.
Fassio, A. and Cozzolino, D. (2004) Non-destructive prediction of chemical composition in sunflower seeds by near infrared spectroscopy. Industrial Crops and Products 20, 321329.
Fassio, A., Fernández, E.G., Restaino, E.A., La Manna, A. and Cozzolino, D. (2009) Predicting the nutritive value of high moisture grain corn by near infrared reflectance spectroscopy. Computers and Electronics in Agriculture 67, 5963.
Ferreira, D.S., Pallone, J.A.L. and Poppi, R.J. (2013) Fourier transform near-infrared spectroscopy (FT-NIRS) application to estimate Brazilian soybean [Glycine max (L.) Merril] composition. Food Research International 51, 5358.
Ferreira, D.S., Galão, O.F., Pallone, J.A.L. and Poppi, R.J. (2014) Comparison and application of near-infrared (NIR) and mid-infrared (MIR) spectroscopy for determination of quality parameters in soybean samples. Food Control 35, 227232.
Del Fiore, A., Reverberi, M., Ricelli, A., Pinzari, F., Serranti, S., Fabbri, A.A., Bonifazi, G. and Fanelli, C. (2010) Early detection of toxigenic fungi on maize by hyperspectral imaging analysis. International Journal of Food Microbiology 144, 6471.
Giacomo, D.R. and Stefania, D.Z. (2013) A multivariate regression model for detection of fumonisins content in maize from near infrared spectra. Food Chemistry 141, 42894294.
Groot, S.P.C., Birnbaum, Y., Rop, N., Jalink, H., Forsberg, G., Kromphardt, C., Werner, S. and Koch, E. (2006) Effect of seed maturity on sensitivity of seeds towards physical sanitation treatments. Seed Science & Technology 34, 403413.
Guevara-Hernandez, F. and Gomez-Gil, J. (2011) A machine vision system for classification of wheat and barley grain kernels. Spanish Journal of Agricultural Research 9, 672.
Gunasekaran, S., Paulsen, M.R. and Shove, G.C. (1985) Optical methods for non-destructive quality evaluation of agricultural and biological materials. Journal of Agricultural Engineering Research 32, 209241.
Hacisalihoglu, G., Larbi, B. and Settles, A.M. (2010) Near-infrared reflectance spectroscopy predicts protein, starch, and seed weight in intact seeds of common bean (Phaseolus vulgaris L.). Journal of Agricultural and Food Chemistry 58, 702706.
Hao, X., Thelen, K. and Gao, J. (2012) Prediction of the ethanol yield of dry-grind maize grain using near infrared spectroscopy. Biosystems Engineering 112, 161170.
Hong, P.T.T., Hai, T.T.T., Lan, L.T., Hoang, V.T., Hai, V. and Nguyen, T.T. (2015) Comparative study on vision based rice seed varieties identification, pp. 377–382 in Proceedings of the Seventh International Conference on Knowledge and Systems Engineering, IEEE-CPS.
Hornberg, A. (2007) Handbook of Machine Vision. Wiley-VCH Verlag GmbH & Co KGaA.
Huang, K.-Y. (2012) Detection and classification of areca nuts with machine vision. Computers & Mathematics with Applications 64, 739746.
Huang, Z., Sha, S., Rong, Z., Chen, J., He, Q., Khan, D.M. and Zhu, S. (2013b) Feasibility study of near infrared spectroscopy with variable selection for non-destructive determination of quality parameters in shell-intact cottonseed. Industrial Crops and Products 43, 654660.
Huang, M., Tang, J., Yang, B. and Zhu, Q. (2016) Classification of maize seeds of different years based on hyperspectral imaging and model updating. Computers and Electronics in Agriculture 122, 139145.
Huang, M., Wan, X., Zhang, M. and Zhu, Q. (2013a) Detection of insect-damaged vegetable soybeans using hyperspectral transmittance image. Journal of Food Engineering 116, 4549.
Huang, M., Wang, Q., Zhang, M. and Zhu, Q. (2014) Prediction of color and moisture content for vegetable soybean during drying using hyperspectral imaging technology. Journal of Food Engineering 128, 2430.
Huang, M., Wang, Q.G., Zhu, Q.B., Qin, J.W. and Huang, G. (2015) Review of seed quality and safety tests using optical sensing technologies. Seed Science & Technology 43, 337366.
Hurburgh, C.R. (2007) Measurement of fatty acids in whole soybeans with near infrared spectroscopy. Lipid Technology 19, 8890.
Igne, B., Rippke, G.R. and Hurburgh, C.R. Jr (2008) Measurement of whole soybean fatty acids by near infrared spectroscopy. Journal of the American Oil Chemists' Society 85, 11051113.
Ishimwe, R., Abutaleb, K. and Ahmed, F. (2014) Applications of thermal imaging in agriculture—a review. Advances in Remote Sensing 3, 128140.
Jalink, H., Frandas, A., Schoor, R. van der and Bino, J.B. (1998) Chlorophyll fluorescence of the testa of Brassica oleracea seeds as an indicator of seed maturity and seed quality. Scientia Agricola 55, 8893.
Jalink, H., van der Schoor, R., Birnbaum, Y.E. and Bino, R.J. (1999) Seed chlorophyll content as an indicator for seed maturity and seed quality. Acta Horticulturae 504, 219228.
Jingtao, J., Yanyao, W., Ranbing, Y. and Shuli, M. (2012) Variety identification of corn seed based on Bregman Split method. Transactions from the Chinese Society of Agricultural Engineering 28, 248252.
Kaliramesh, S., Chelladurai, V., Jayas, D.S., Alagusundaram, K., White, N.D.G. and Fields, P.G. (2013) Detection of infestation by Callosobruchus maculatus in mung bean using near-infrared hyperspectral imaging. Journal of Stored Products Research 52, 107111.
Kandpal, L.M., Lee, S., Kim, M.S., Bae, H. and Cho, B.-K. (2015) Short wave infrared (SWIR) hyperspectral imaging technique for examination of aflatoxin B1 (AFB1) on corn kernels. Food Control 51, 171176.
Kandpal, L.M., Lohumi, S., Kim, M.S., Kang, J.-S. and Cho, B.-K. (2016) Near-Infrared hyperspectral imaging system coupled with multivariate methods to predict viability and vigor in muskmelon seeds. Sensors and Actuators B: Chemical 229, 534544.
Karunakaran, C., Jayas, D. and White, N.D. (2004a) Detection of internal wheat seed infestation by Rhyzopertha dominica using X-ray imaging. Journal of Stored Products Research 40, 507516.
Karunakaran, C., Jayas, D.S. and White, N.D.G. (2004b) Identification of wheat kernels damaged by the red flour beetle using x-ray images. Biosystems Engineering 87, 267274.
Kenanoglu, B.B., Demir, I. and Jalink, H. (2013) Chlorophyll fluorescence sorting method to improve quality of capsicum pepper seed lots produced from different maturity fruits. Hortscience 48, 965968.
Kılıç, K., Boyacı, İ.H., Köksel, H. and Küsmenoğlu, İ. (2007) A classification system for beans using computer vision system and artificial neural networks. Journal of Food Engineering 78, 897904.
Kim, G., Kim, G., Ahn, C.-K., Yoo, Y. and Cho, B.-K. (2013) Mid-infrared lifetime imaging for viability evaluation of lettuce seeds based on time-dependent thermal decay characterization. Sensors 13, 29862996.
Kim, K.S., Park, S.H., Choung, M.G. and Jang, Y.S. (2007) Use of near-infrared spectroscopy for estimating fatty acid composition in intact seeds of rapeseed. Journal of Crop Science and Biotechnology 10, 1520.
Kong, W., Zhang, C., Liu, F., Nie, P. and He, Y. (2013) Rice seed cultivar identification using near-infrared hyperspectral imaging and multivariate data analysis. Sensors 13, 89168927.
Konstantinova, P., Van Der Schoor, R., Van Den Bulk, R. and Jalink, H. (2002) Chlorophyll fluorescence sorting as a method for improvement of barley (Hordeum vulgare L.) seed health and germination. Seed Science & Technology 30, 411421.
Kovalenko, I. V., Rippke, G.R. and Hurburgh, C.R. (2006) Measurement of soybean fatty acids by near-infrared spectroscopy: Linear and nonlinear calibration methods. Journal of the American Oil ChemistsSociety 83, 421427.
Kranner, I., Kastberger, G., Hartbauer, M. and Pritchard, H.W. (2010) Non-invasive diagnosis of seed viability using infrared thermography. Proceedings of the National Academy of Sciences of the USA 107, 39123917.
Kurtulmuş, F. and Ünal, H. (2015) Discriminating rapeseed varieties using computer vision and machine learning. Expert Systems with Applications 42, 18801891.
Kurtulmuş, F., Alibaş, İ. and Kavdir, I. (2016) Classification of pepper seeds using machine vision based on neural network. International Journal of Agricultural and Biological Engineering 9, 5162.
Lee, J.H. and Choung, M.-G. (2011) Non-destructive determination of herbicide-resistant genetically modified soybean seeds using near-infrared reflectance spectroscopy. Food Chemistry 126, 368373.
Lee, H., Cho, B.-K., Kim, M.S., Lee, W.-H., Tewari, J., Bae, H., Sohn, S.-I. and Chi, H.-Y. (2013) Prediction of crude protein and oil content of soybeans using Raman spectroscopy. Sensors and Actuators B: Chemical 185, 694700.
Lee, H., Lim, H.-S. and Cho, B.-K. (2016a) Classification of cucumber green mottle mosaic virus (CGMMV) infected watermelon seeds using Raman spectroscopy, p. 98640D in Proceedings SPIE 9864, Sensing for Agriculture and Food Quality and Safety VIII (International Society for Optics and Photonics). doi:10.1117/12.2228264
Lee, H., Kim, M.S., Song, Y.-R., Oh, C., Lim, H.-S., Lee, W.-H., Kang, J.-S. and Cho, B.-K. (2016b) Non-destructive evaluation of bacteria-infected watermelon seeds using Vis/NIR hyperspectral imaging. Journal of the Science of Food and Agriculture. doi: 10.1002/jsfa.7832
Li, J., Liao, G., Ou, Z. and Jin, J. (2007) Rapeseed seeds classification by machine vision, pp. 222–226 in Workshop on Intelligent Information Technology Application (IITA 2007) IEEE.
Liu, C., Liu, W., Lu, X., Chen, W., Yang, J. and Zheng, L. (2014) Non-destructive determination of transgenic Bacillus thuringiensis rice seeds (Oryza sativa L.) using multispectral imaging and chemometric methods. Food Chemistry 153, 8793.
Lohumi, S., Mo, C., Kang, J.-S., Hong, S.-J. and Cho, B.-K. (2013) Non-destructive evaluation for the viability of watermelon (Citrullus lanatus) seeds using fourier transform near infrared spectroscopy. Journal of Biosystems Engineering 38, 312317.
Lohumi, S., Lee, S., Lee, H. and Cho, B.-K. (2015) A review of vibrational spectroscopic techniques for the detection of food authenticity and adulteration. Trends in Food Science and Technology 46, 8598.
Mahesh, S., Manickavasagan, A., Jayas, D.S., Paliwal, J. and White, N.D.G. (2008) Feasibility of near-infrared hyperspectral imaging to differentiate Canadian wheat classes. Biosystems Engineering 101, 5057.
Mahesh, S., Jayas, D.S., Paliwal, J., and White, N.D.G. (2011a) Near-infrared hyperspectral imaging for protein and hardness predictions of bulk samples of western canadian wheat from different locations and crop years using multivariate regression models. In CSBE/SCGAB Annual Conference (Winnipeg, Manitoba).
Mahesh, S., Jayas, D.S., Paliwal, J. and White, N.D.G. (2011b) Identification of wheat classes at different moisture levels using near-infrared hyperspectral images of bulk samples. Sensing and Instrumentation for Food Quality and Safety 5, 19.
Manickavasagan, A., Jayas, D.S. and White, N.D.G. (2008) Thermal imaging to detect infestation by Cryptolestes ferrugineus inside wheat kernels. Journal of Stored Products Research 44, 186192.
Manickavasagan, A., Jayas, D.S., White, N.D.G. and Paliwal, J. (2010) Wheat class identification using thermal imaging. Food Bioprocessing and Technology 3, 450460.
Mathanker, S.K., Weckler, P.R. and Bowser, T.J. (2013) X-ray applications in food and agriculture: a review. Transactions of the ASABE 56, 12271239.
McGoverin, C.M., Engelbrecht, P., Geladi, P. and Manley, M. (2011) Characterisation of non-viable whole barley, wheat and sorghum grains using near-infrared hyperspectral data and chemometrics. Analytical and Bioanalytical Chemistry 401, 22832289.
Min, T.G. and Kang, W.S. (2003) Non-destructive separation of viable and non-viable gourd (Lagenaria siceraria) seeds using single seed near infrared spectroscopy. Journal of the Korean Society of Horticultural Science 44, 545548.
Mo, C., Kang, S., Lee, K., Kim, G., Cho, B.-K., Lim, J.-G., Lee, H.-S. and Park, J. (2012) Germination prediction of cucumber (Cucumis sativus) seed using Raman spectroscopy. Journal of Biosystems Engineering 37, 404410.
Mo, C., Kim, G., Lee, K., Kim, M., Cho, B.-K., Lim, J. and Kang, S. (2014) Non-destructive quality evaluation of pepper (Capsicum annuum L.) seeds using led-induced hyperspectral reflectance imaging. Sensors 14, 74897504.
Mo, C., Kim, M.S., Lim, J., Lee, K., Kim, G. and Cho, B.-K. (2015) Multispectral fluorescence imaging technique for discrimination of cucumber seed viability. Transactions of the ASABE 58, 959968.
Monteiro, S.T., Minekawa, Y., Kosugi, Y., Akazawa, T. and Oda, K. (2007) Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery. ISPRS Journal of Photogrammetry and Remote Sensing 62, 212.
Moschner, C.R. and Biskupek-Korell, B. (2006) Estimating the content of free fatty acids in high-oleic sunflower seeds by near-infrared spectroscopy. European Journal of Lipid Science and Technology 108, 606613.
Neethirajan, S., Karunakaran, C., Symons, S. and Jayas, D.S. (2006) Classification of vitreousness in durum wheat using soft X-rays and transmitted light images. Computers and Electronics in Agriculture 53, 7178.
Neethirajan, S., Jayas, D.S. and White, N.D.G. (2007) Detection of sprouted wheat kernels using soft X-ray image analysis. Journal of Food Engineering 81, 509513.
Olesen, M., Nikneshan, P., Shrestha, S., Tadayyon, A., Deleuran, L., Boelt, B. and Gislum, R. (2015) Viability prediction of Ricinus cummunis L. seeds using multispectral imaging. Sensors 15, 45924604.
Olesen, M.H., Carstensen, J.M.M. and Boelt, B. (2011) Multispectral imaging as a potential tool for seed health testing of spinach (Spinacia oleracea L.). Seed Science & Technology 39, 140150.
Pandord, J.A., Williams, P.C. and DeMan, J.M. (1988) Analysis of oilseeds for protein, oil, fiber and moisture by near-infrared reflectance spectroscopy. Journal of the American Oil ChemistsSociety 65, 16271634.
Paolesse, R., Alimelli, A., Martinelli, E., Natale, C. Di, D'Amico, A., D'Egidio, M.G., Aureli, G., Ricelli, A. and Fanelli, C. (2006) Detection of fungal contamination of cereal grain samples by an electronic nose. Sensors and Actuators B: Chemical 119, 425430.
Patil, A.G., Oak, M.D., Taware, S.P., Tamhankar, S.A. and Rao, V.S. (2010) Non-destructive estimation of fatty acid composition in soybean [Glycine max (L.) Merrill] seeds using near-infrared transmittance spectroscopy. Food Chemistry 120, 12101217.
Pazoki, A., Farokhi, F. and Pazoki, Z. (2013) Corn seed varieties classification based on mixed morphological and color features using artificial neural networks. Research Journal of Applied Sciences, Engineering and Technology 6, 35063513.
Pearson, T.C. and Wicklow, D.T. (2006) Detection of corn kernels infected by fungi. Transactions of the ASABE 49, 12351245.
Pérez-Vich, B., Velasco, L. and Fernández-Martínez, J.M. (1998) Determination of seed oil content and fatty acid composition in sunflower through the analysis of intact seeds, husked seeds, meal and oil by near-infrared reflectance spectroscopy. Journal of the American Oil Chemists’ Society 75, 547555.
Petisco, C., García-Criado, B., Vázquez-de-Aldana, B.R., de Haro, A. and García-Ciudad, A. (2010) Measurement of quality parameters in intact seeds of Brassica species using visible and near-infrared spectroscopy. Industrial Crops and Products 32, 139146.
Phan-Thien, K.-Y., Golic, M., Wright, G.C. and Lee, N.A. (2011) Feasibility of estimating peanut essential minerals by near infrared reflectance spectroscopy. Sensing and Instrumentation for Food Quality and Safety 5, 4349.
Pinto, T.L.F., Cicero, S.M., França-Neto, J.B. and Forti, V.A. (2009) An assessment of mechanical and stink bug damage in soybean seed using X-ray analysis test. Seed Science & Technology 37, 110120.
Plans, M., Simó, J., Casañas, F., Sabaté, J. and Rodriguez-Saona, L. (2013) Characterization of common beans (Phaseolus vulgaris L.) by infrared spectroscopy: Comparison of MIR, FT-NIR and dispersive NIR using portable and benchtop instruments. Food Research International 54, 16431651.
Pourreza, A., Pourreza, H., Abbaspour-Fard, M.-H. and Sadrnia, H. (2012) Identification of nine Iranian wheat seed varieties by textural analysis with image processing. Computers and Electronics in Agriculture 83, 102108.
Qin, J., Chao, K., Kim, M.S. and Burks, T.F. (2013) Hyperspectral and multispectral imaging for evaluating food safety and quality. Journal of Food Engineering 118(2), 157171.
Rad, S.J.M., Tab, F.A. and Mollazade, K. (2011) Classification of rice varieties using optimal color and texture features and bp neural networks, pp. 1–5 in 7th Iranian Conference on Machine Vision and Image Processing (IEEE).
Rosales, A., Galicia, L., Oviedo, E., Islas, C. and Palacios-Rojas, N. (2011) Near-infrared reflectance spectroscopy (NIRS) for protein, tryptophan, and lysine evaluation in quality protein maize (QPM) breeding programs. Journal of Agricultural and Food Chemistry 59, 1078110786.
Rudolphi, S., Becker, H.C., Schierholt, A. and von Witzke-Ehbrecht, S. (2012) Improved estimation of oil, linoleic and oleic acid and seed hull fractions in safflower by NIRS. Journal of the American Oil ChemistsSociety 89, 363369.
Seo, Y.-W., Ahn, C.K., Lee, H., Park, E., Mo, C. and Cho, B. (2016) Non-destructive sorting techniques for viable pepper (Capsicum annuum L.) seeds using fourier transform near-infrared and Raman spectroscopy. Journal of Biosystems Engineering 41, 5159.
Serranti, S., Cesare, D. and Bonifazi, G. (2013) The development of a hyperspectral imaging method for the detection of Fusarium-damaged, yellow berry and vitreous Italian durum wheat kernels. Biosystems Engineering 115, 2030.
Shahin, M.A. and Symons, S.J. (2011) Detection of fusarium damaged kernels in Canada western red spring wheat using visible/near-infrared hyperspectral imaging and principal component analysis. Computers and Electronics in Agriculture 75, 107112.
Shahin, M.A., Symons, S.J. and Hatcher, D.W. (2014) Quantification of mildew damage in soft red winter wheat based on spectral characteristics of bulk samples: a comparison of visible-near-infrared imaging and near-infrared spectroscopy. Food and Bioprocess Technology 7, 224234.
Shao, Y., Zhao, C., He, Y. and Bao, Y. (2009) Application of infrared spectroscopy technique and chemometrics for measurement of components in rice after radiation. Transactions of the ASABE 52, 187192.
Shao, Y., Cen, Y., He, Y. and Liu, F. (2011) Infrared spectroscopy and chemometrics for the starch and protein prediction in irradiated rice. Food Chemistry 126, 18561861.
Shatadal, P. and Tan, J. (2003) Identifying damaged soybeans by color image analysis. Applied Engineering In Agriculture 19, 6569.
Shetty, N., Min, T.-G., Gislum, R., Olesen, M. and Boelt, B. (2011) Optimal sample size for predicting viability of cabbage and radish seeds based on near infrared spectra of single seeds. Journal of Near Infrared Spectroscopy 19, 451.
Shetty, N., Olesen, M.H., Gislum, R., Deleuran, L.C. and Boelt, B. (2012) Use of partial least squares discriminant analysis on visible-near infrared multispectral image data to examine germination ability and germ length in spinach seeds. Journal of Chemometrics 26, 462466.
Siemens, B.J. and Daun, J.K. (2005) Determination of the fatty acid composition of canola, flax, and solin by near-infrared spectroscopy. Journal of the American Oil Chemists’ Society 82, 153157.
Singh, C.B., Jayas, D.S., Paliwal, J. and White, N.D.G. (2009a) Detection of sprouted and midge-damaged wheat kernels using near-infrared hyperspectral imaging. Cereal Chemistry 86, 256260.
Singh, C.B., Jayas, D.S., Paliwal, J., and White, N.D.G. (2009b) Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging. Journal of Stored Products Research 45, 151158.
Singh, C.B., Jayas, D.S., Paliwal, J. and White, N.G. (2010a) Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging. Computers and Electronics in Agriculture 73, 118125.
Singh, C.B., Jayas, D.S., Paliwal, J. and White, N.D.G. (2010b) Detection of midge-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging. Biosystems Engineering 105, 380387.
Singh, C.B., Jayas, D.S., Paliwal, J. and White, N.D.G. (2012) Fungal damage detection in wheat using shortwave near-infrared hyperspectral and digital colour imaging. International Journal of Food Properties 15, 1124.
Sirisomboon, C.D., Putthang, R. and Sirisomboon, P. (2013) Application of near infrared spectroscopy to detect aflatoxigenic fungal contamination in rice. Food Control 33, 207214.
Sirisomboon, P., Hashimoto, Y. and Tanaka, M. (2009) Study on non-destructive evaluation methods for defect pods for green soybean processing by near-infrared spectroscopy. Journal of Food Engineering 93, 502512.
Soto-Cámara, M., Gaitán-Jurado, A.J. and Domínguez, J. (2012) Application of near infrared spectroscopy technology for the detection of fungicide treatment on durum wheat samples. Talanta 97, 298302.
Sundaram, J., Kandala, C. V., Holser, R.A., Butts, C.L. and Windham, W.R. (2010a) Determination of in-shell peanut oil and fatty acid composition using near-infrared reflectance spectroscopy. Journal of the American Oil Chemists’ Society 87, 11031114.
Sundaram, J., Kandala, C.V.K., Butts, C.L. and Windham, W.R. (2010b) Application of NIR reflectance spectroscopy on determination of moisture content of peanuts: a non destructive analysis method. Transactions of the ASABE 53, 183189.
Sunoj, S., Igathinathane, C. and Visvanathan, R. (2016) Non-destructive determination of cocoa bean quality using FT-NIR spectroscopy. Computers and Electronics in Agriculture 124, 234242.
Tallada, J.G., Palacios-Rojas, N. and Armstrong, P.R. (2009) Prediction of maize seed attributes using a rapid single kernel near infrared instrument. Journal of Cereal Science 50, 381387.
Tallada, J.G., Wicklow, D.T., Pearson, T.C. and Armstrong, P.R. (2011) Detection of fungus-infected corn kernels using near-infrared reflectance spectroscopy and color imaging. Transactions of the ASABE 54, 11511158.
Tigabu, M. and Odén, P.C. (2003) Discrimination of viable and empty seeds of Pinus patula Schiede & Deppe with near-infrared spectroscopy. New Forestry 25, 163176.
Vadivambal, R. and Jayas, D.S. (2011) Applications of thermal imaging in agriculture and food industry – a review. Food and Bioprocess Technology 4, 186199.
Vaknin, Y., Ghanim, M., Samra, S., Dvash, L., Hendelsman, E., Eisikowitch, D. and Samocha, Y. (2011) Predicting Jatropha curcas seed-oil content, oil composition and protein content using near-infrared spectroscopy – a quick and non-destructive method. Industrial Crops and Products 34, 10291034.
Velasco, L. and Becker, H.C. (1998) Estimating the fatty acid composition of the oil in intact-seed rapeseed (Brassica napus L.) by near-infrared reflectance spectroscopy. Euphytica 101, 221230.
Velasco, L. and Möllers, C. (2002) Non-destructive assessment of protein content in single seeds of rapeseed (Brassica napus L.) by near-infrared reflectance spectroscopy. Euphytica 123, 8993.
Velasco, L., Möllers, C. and Becker, H.C. (1999) Estimation of seed weight, oil content and fatty acid composition in intact single seeds of rapeseed (Brassica napus L.) by near-infrared reflectance spectroscopy. Euphytica 106, 7985.
Venora, G., Grillo, O., Ravalli, C. and Cremonini, R. (2009) Identification of Italian landraces of bean (Phaseolus vulgaris L.) using an image analysis system. Scientia Horticulturae 121, 410418.
Vozáry, E., Paine, D.H., Kwiatkowski, J., and Taylor, A.G. (2007) Prediction of soybean and snap bean seed germinability by electrical impedance spectroscopy. Seed Science & Technology 35, 4864.
Wang, D., Dowell, F.E., Ram, M.S. and Schapaugh, W.T. (2004) Classification of fungal-damaged soybean seeds using near-infrared spectroscopy. International Journal of Food Properties 7, 7582.
Wang, L., Wang, Q., Liu, H., Liu, L. and Du, Y. (2012) Determining the contents of protein and amino acids in peanuts using near-infrared reflectance spectroscopy. Journal of the Science of Food and Agriculture 93, 118124.
Wang, W., Heitschmidt, G.W., Ni, X., Windham, W.R., Hawkins, S. and Chu, X. (2014) Identification of aflatoxin B1 on maize kernel surfaces using hyperspectral imaging. Food Control 42, 7886.
Weinstock, B.A., Janni, J., Hagen, L. and Wright, S. (2006) Prediction of oil and oleic acid concentrations in individual corn (Zea mays L.) kernels using near-infrared reflectance hyperspectral imaging and multivariate analysis. Applied Spectroscopy 60, 916.
Williams, P., Geladi, P., Fox, G. and Manley, M. (2009) Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis. Analytica Chimica Acta 653, 121130.
Williams, P.J., Geladi, P., Britz, T.J. and Manley, M. (2012) Investigation of fungal development in maize kernels using NIR hyperspectral imaging and multivariate data analysis. Journal of Cereal Science 55, 272278.
Wilson, A.D. and Baietto, M. (2009) Applications and advances in electronic-nose technologies. Sensors 9, 50995148.
Wittkop, B., Snowdon, R.J. and Friedt, W. (2012) New NIRS calibrations for fiber fractions reveal broad genetic variation in Brassica napus seed quality. Journal of Agricultural and Food Chemistry 60, 22482256.
Wu, D. and Sun, D.-W. (2013) Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: a review – Part I: Fundamentals. Innovative Food Science and Emerging Technologies 19, 114.
Wu, J. and Shi, C. (2004) Prediction of grain weight, brown rice weight and amylose content in single rice grains using near-infrared reflectance spectroscopy. Food and Crop Research 87, 1321.
Wu, Z., Zhang, J., Song, P., Li, W. and Lan, Y. (2013) A sorting method for maize haploid based on computer vision. ASABE Annual International Meeting, St Joseph, MI, American Society of Agricultural and Biological Engineers.
Xing, J., Van Hung, P., Symons, S., Shahin, M. and Hatcher, D. (2009) Using a short wavelength infrared (SWIR) hyperspectral imaging system to predict alpha amylase activity in individual Canadian western wheat kernels. Sensing and Instrumentation for Food Quality and Safety 3, 211218.
Xing, J., Symons, S., Shahin, M. and Hatcher, D. (2010) Detection of sprout damage in Canada Western Red Spring wheat with multiple wavebands using visible/near-infrared hyperspectral imaging. Biosystems Engineering 106, 188194.
Xing, J., Symons, S., Hatcher, D. and Shahin, M. (2011) Comparison of short-wavelength infrared (SWIR) hyperspectral imaging system with an FT-NIR spectrophotometer for predicting alpha-amylase activities in individual Canadian Western Red Spring (CWRS) wheat kernels. Biosystems Engineering 108, 303310.
Yang, X.-S., Wang, L.-L., Zhou, X.-R., Shuang, S.-M., Zhu, Z.-H., Li, N., Li, Y., Liu, F., Liu, S.-C., Lu, P. et al. (2013) Determination of protein, fat, starch, and amino acids in foxtail millet [Setaria italica (L.) Beauv.] by Fourier transform near-infrared reflectance spectroscopy. Food Science and Biotechnology 22, 14951500.
Yao, H., Hruska, Z., Kincaid, R., Brown, R.L., Bhatnagar, D. and Cleveland, T.E. (2013) Detecting maize inoculated with toxigenic and atoxigenic fungal strains with fluorescence hyperspectral imagery. Biosystems Engineering 115, 125135.
Yi, X., Junxiong, Z., Wei, L. and Weiguo, C. (2007) Multi-objective dynamic detection of seeds based on machine vision. Progress in Natural Science 17, 217221.
Zapotoczny, P. (2011a) Discrimination of wheat grain varieties using image analysis and neural networks. Part I. Single kernel texture. Journal of Cereal Science 54, 6068.
Zapotoczny, P. (2011b) Discrimination of wheat grain varieties using image analysis: morphological features. European Food Research and Technology 233, 769779.
Zayas, I., Lai, F.S. and Pomeranz, Y. (1986) Discrimination between wheat classes and varieties by image analysis. Cereal Chemistry 63, 5256.
Zhang, B., Rong, Z.Q., Shi, Y., Wu, J.G. and Shi, C.H. (2011) Prediction of the amino acid composition in brown rice using different sample status by near-infrared reflectance spectroscopy. Food Chemistry 127, 275281.
Zhang, X., Liu, F., He, Y. and Li, X. (2012) Application of hyperspectral imaging and chemometric calibrations for variety discrimination of maize seeds. Sensors 12, 17234.
Zhou, B., Wang, J. and Qi, J. (2012) Identification of different wheat seeds by electronic nose. International Agrophysics 26, 413418.
Zhu, D., Wang, K., Zhang, D., Huang, W., Yang, G., Ma, Z. and Wang, C. (2011) Quality assessment of crop seeds by near-infrared hyperspectral imaging. Sensor Letters 9, 11441150.
Zhu, D., Wang, C., Pang, B., Shan, F., Wu, Q. and Zhao, C. (2012) Identification of wheat cultivars based on the hyperspectral image of single seed. Journal of Nanoelectronics and Optoelectronics 7, 167172.