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.
Highlights
Soybean is a major agricultural commodity in world trade, and is a rich source of protein and oil for consumption by both humans and animals
This study investigates the feasibility of the hyperspectral imaging (HSI) technique in combination with the Partial Least-Squares Discriminant Analysis (PLS-DA), optimal variable selection method, and image processing technique, for determining viable and nonviable soybean seeds
A kernel-based image processing technique was adopted to the whole as viable nonviable instead of classifying individual pixels ofpixels hyperspectral images
Summary
Soybean is a major agricultural commodity in world trade, and is a rich source of protein and oil for consumption by both humans and animals. The latest data from the United States Department of Agriculture report that US soybean production increased by 59% between 2000 and 2017 [1]. In 2016, the total global production of soybean was approximately 335 million tons [2]. Soybeans are produced by only a few countries, they are traded widely to meet soybean demand in every country in the world. More than 90% of global soybean production comes from the US, Brazil, Paraguay, and Argentina, while the biggest importers are China, Korea, and Japan [3]. Ensuring the quality of Sensors 2019, 19, 271; doi:10.3390/s19020271 www.mdpi.com/journal/sensors
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