The evaluation of the genetic quality of a seed lot is crucial for the quality control process in its production and commercialization, as well as in the identification of superior genotypes and the verification of the correct crossing in plant breeding programmes. Current techniques, based on the identification of seed morphological characteristics, require skilled analysts, while biochemical methods are time-consuming and costly. The application of spectral imaging analysis, which combines digital imaging with spectroscopy, is gaining ground as a fast, accurate and non-destructive method. The success of this technique is closely linked to chemometric techniques, which use statistical and mathematical tools in data processing. The aim of the work was to evaluate the main procedures in terms of spectral image analysis and chemometric procedures applied in seed phenotyping and its practical application. A systematic review was conducted using the PRISMA methodology, in which a total of 1304 articles were identified and screened to the inclusion of 44 articles pertaining to the scope. It was concluded that spectral image analysis has a high ability to classify seeds of different genotypes (93.33%) in a range of situations: between cultivars; hybrids and progenitors; and hybrids and lines, as well as in the separation of coated seeds. Accurate classification can be obtained by different strategies, such as the choice of the equipment type, the spectrum range and extra features, guided by the characteristics of the species, as well as in the choice of algorithms and dimensionality reduction procedures for the optimization of models when there is a large amount of data. Despite the fact that the practical application of this technique in seed phenotyping still needs to be developed for use in laboratories with large volumes of analyses, lots, genotypes and harvests. Research has been accelerated to overcome the practical challenges of this method, as seen in works using model update algorithms, online classification systems, and real-time classification maps. Thus, there are strong indications that the application of multispectral image analysis will reach the routine of seed analysis laboratories.

Germination of fruit of common lambsquarters (Chenopodium album L.) from three out of four sources was enhanced by 9 to 41% by removal of the fruit coat, and by 21% by washing the fruit for 70 h. Brown-black seed germinated more rapidly than the black and brown seed after washing for 70 h. The washing treatment appeared to remove some unknown inhibitors. After washing, seed germination was found to be positively correlated with seed size.

Giant ragweed exhibits a high degree of polymorphism among individual plants in seed size, shape, spininess, and color. These features may play an important role in giant ragweed seed survival and predation avoidance; however, they are difficult to evaluate because of lack of quantification methods. A computer imaging technique was developed for describing and classifying giant ragweed seeds using digital images of the seed top and side views. Seed samples collected from 20 different giant ragweed plants (classes) were mounted and digitally scanned. Quantitative features were extracted from the seed images, including color, width, height, area, and seed perimeter. A polygon (convex hull) of the seed image based on the seed outline was constructed, from which spininess indices were developed. Fisher's linear discriminant with normalized nearest neighbor classification was used to classify randomly selected images of individual seeds according to class (maternal origin), using the extracted features as a database. The best classification rate achieved was 99%, with 138 out of 140 seeds correctly matched using data from both the top and side views. Seed features were easily extracted and varied from 1.2- to 4.5-fold among classes. Area and perimeter measurements varied least within classes but varied most among classes, suggesting that these features discriminate effectively among seeds from different plants in giant ragweed. Convex hull area : seed area ratio, using the seed top view images, was the best index of seed spininess, aligning well with visual assessment and providing greatest discrimination among classes. This experiment shows that in the case of giant ragweed, seeds from different plants are distinguishable in an objective and quantitative manner. This imaging technique can be applied to identification of seeds from different species and to studies on variable seed morphology within a species.

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.