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

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.