Rapid and Nondestructive Measurement of Rice Seed Vitality of Different Years Using Near-Infrared Hyperspectral Imaging

Abstract

Seed vitality is one of the primary determinants of high yield that directly affects the performance of seedling emergence and plant growth. However, seed vitality may be lost during storage because of unfavorable conditions, such as high moisture content and temperatures. It is therefore vital for seed companies as well as farmers to test and determine seed vitality to avoid losses of any kind before sowing. In this study, near-infrared hyperspectral imaging (NIR-HSI) combined with multiple data preprocessing methods and classification models was applied to identify the vitality of rice seeds. A total of 2400 seeds of three different years: 2015, 2016 and 2017, were evaluated. The experimental results show that the NIR-HSI technique has great potential for identifying vitality and vigor of rice seeds. When detecting the seed vitality of the three different years, the extreme learning machine model with Savitzky–Golay preprocessing could achieve a high classification accuracy of 93.67% by spectral data from only eight wavebands (992, 1012, 1119, 1167, 1305, 1402, 1629 and 1649 nm), which could be developed for a fast and cost-effective seed-sorting system for industrial online application. When identifying non-viable seeds from viable seeds of different years, the least squares support vector machine model coupled with raw data and selected wavelengths of 968, 988, 1204, 1301, 1409, 1463, 1629, 1646 and 1659 nm achieved better classification performance (94.38% accuracy), and could be adopted as an optimal combination to identify non-viable seeds from viable seeds.

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].…”

Recognition of different Longjing fresh tea varieties using hyperspectral imaging technology and chemometrics

“…Hyperspectral imaging technology is a green, efficient, nondestructive‐testing technology which utilizes visible and near‐infrared bands. Image information can be gained while acquiring spectral information, so that the sample can be fully analyzed from a three‐dimensional data block (He et al, ; Wang, Sun, Pu, & Zhu, ). This technology has been widely used in agriculture and forestry research (Caporaso, Whitworth, Grebby, & Fisk, ; Zhang, Dai, & Cheng, ; Zhang, Feng, Liu, & He, ).…”

“…Hyperspectral imaging technology is a green, efficient, nondestructive-testing technology which utilizes visible and nearinfrared bands. Image information can be gained while acquiring spectral information, so that the sample can be fully analyzed from a threedimensional data block (He et al, 2019;Wang, Sun, Pu, & Zhu, 2016).…”

Advances in Near Infrared Spectroscopy and Related Computational Methods

“…However, for sake of clarity a tentative and brief overview of these contributions may be helpful to present the Special Issue to the readership. The majority of the articles focuses on applied qualitative and quantitative analyses in a variety of fields [9,11,12,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Roughly, half of these may be associated with pharmaceutical and medical applications [17][18][19][20][21][22][23][24].…”

“…Roughly, half of these may be associated with pharmaceutical and medical applications [17][18][19][20][21][22][23][24]. Most of the remaining applied studies were directed at agricultural applications [25][26][27][28][29][30][31][32][33][34], well reflecting the ever-growing significance of NIR spectroscopy in this area; a good perspective of this topic is included in a focused review article published in the Special Issue [30]. Modern strategies for food analysis also rely on this technique, and few contributions touched that field as well [9,12,33,34].…”