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.
Highlights
Rice (Oryza sativa L.) is one of the three most important crops in the world, with a harvested area of 167 million ha and 769 million tons of total yield in 2017 [1]
The whole spectral data set was reduced to a matrix of dimensions m × n, where m represents the number of samples (m = 2400) and n was the number dimensions m × n, where m represents the number of samples (m = 2400) and n was the number of of selected wavelengths including 9, 8, 11 and 6 for raw data, SG, SG-D1 and multiplicative scatter correction (MSC) preprocessed selected wavelengths including 9, 8, 11 and 6 for raw data, SG, SG-D1 and MSC preprocessed data, data, respectively
The seed vitality was different in the seeds of three different years, and the seeds stored in later years could obtain a higher vigor, which was consistent with the change trend of spectral reflectance of the seeds
Summary
Rice (Oryza sativa L.) is one of the three most important crops in the world, with a harvested area of 167 million ha and 769 million tons of total yield in 2017 [1]. Any changes in field conditions (e.g., humidity, temperature, pests, diseases) and post-harvest processes (e.g., drying, storage) can lead to seed damage, and cause retardation or complete vitality loss if not carefully controlled. Hyperspectral imaging (HSI) is one of the most feasible methods for rapidly and non-destructively detecting the substances of agricultural products It combines the technologies of spectroscopy and digital imaging, and is able to obtain spectral and spatial information simultaneously from testing samples in the form of a hypercube with two spatial dimensions and one spectral dimension [14]. This study was conducted to determine optimal and spectral wavebands multivariable non-viable seeds from of different years, andseeds an alternative approach classification model to viable acquireseeds or detect the vigor of rice storedprovide for different years based on the of rapidly and non-destructively measuring the rice seed vitality for industrial or large-scale near-infrared hyperspectral imaging (NIR-HSI) technique, and attempt to build a model to identify application. Non-viable seeds from viable seeds of different years, and provide an alternative approach of rapidly and non-destructively measuring the rice seed vitality for industrial or large-scale application
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