Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis

Abstract

Viability is an important quality factor influencing seed germination and crop yield. Current seed-viability testing methods rely on conventional manual inspections, which use destructive, labor-intensive and time-consuming measurements. The aim of this study is to distinguish between viable and nonviable soybean seeds, using a near-infrared (NIR) hyperspectral imaging (HSI) technique in a rapid and nondestructive manner. The data extracted from the NIR–HSI of viable and nonviable soybean seeds were analyzed using a partial least-squares discrimination analysis (PLS-DA) technique for classifying the viable and nonviable soybean seeds. Variable importance in projection (VIP) was used as a waveband selection method to develop a multispectral imaging model. Initially, the spectral profile of each pixel in the soybean seed images was subjected to PLS-DA analysis, which yielded a reasonable classification accuracy; however, the pixel-based classification method was not successful for high accuracy detection for nonviable seeds. Another viability detection method was then investigated: a kernel image threshold method with an optimum-detection-rate strategy. The kernel-based classification of seeds showed over 95% accuracy even when using only seven optimal wavebands selected through VIP. The results show that the proposed multispectral NIR imaging method is an effective and accurate nondestructive technique for the discrimination of soybean seed viability.

A Rapid and Highly Efficient Method for the Identification of Soybean Seed Varieties: Hyperspectral Images Combined with Transfer Learning

“…The spectral analysis method reflects the difference in the internal physical structure and the chemical composition of seed varieties through spectral information, which has the advantages of being fast, accurate, and nondestructive [12]; it has been applied to multiple tasks related to seeds, such as the identification of soybean [13,14], rice [6] and maize [15][16][17] seed varieties, the identification of nontransgenic and transgenic seeds [18][19][20], the identification of seed geographical sources [21], the identification of infected germ seeds [22], the identification of infected pest seeds and healthy seeds [23], identification of the year of seeds [24,25], and the determination of tomato [26], soybean [27,28], corn [28], muskmelon [29], cabbage and radish [30] seed vitality. Less research has been performed on image analysis than spectral analysis, and only some plants have been studied through image analysis, such as peppers [31], paddy [32,33], and corn [34].…”

Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method

“…In the same pattern, the 1690 nm peaks [ 47 ] and 1800 nm represented the existence of protein in the nuts [ 48 ], that peanuts spectral shows slightly higher peaks compared to other nuts. The peaks at 995 nm were thought to be caused by the N-H second overtone associated with peptides and proteins [ 49 ].…”

Online Application of a Hyperspectral Imaging System for the Sorting of Adulterated Almonds

“…PLS-DA has been used for evaluations of various food products' quality and has been proven to be a powerful technique for this purpose [18,25,26]. There are many articles giving detailed descriptions of the basic theory of PLS-DA [24], which is well-expressed by [27], which explains that PLS-DA is a robust method for classification models, commonly used for model classification.…”

“…Consequently, it is considered to be the most practical and relevant industrial detection method that can offer the fast, real-time, and non-destructive inspection of individual seeds. Previous studies have shown the potential of using HSI coupled with multivariate data analysis techniques for the quality analysis of agriculture products with different concepts, including the rapid assessment of corn seed viability using shortwave infrared line-scan hyperspectral imaging and chemometrics reported by [16], the total nitrogen concentration in almonds using HSI studied by [17], the rapid measurement of soybean seed viability using kernel-based multispectral image analysis reported by [18], the quality analysis of bell peppers based on HSI [19], and the prediction of the viability and vigor in muskmelon seeds [20]. Based on the above reports, HSI has a high potential for industrial application and the rapid real-time quality analysis of products.…”