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Spectrochim Acta A Mol Biomol Spectrosc ; 245: 118888, 2021 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-32947159


In this study, the feasibility of estimation and forecast of different vitality Quercus variabilis seeds by a hyperspectral imaging technique were investigated. Artificially accelerated aging was conducive to achieve the division of four vitality levels. Hyperspectral data in the first 10 h of germination were continuously collected at one-hour intervals. The optimal band was selected for the original and pre-processed spectra which were treated by multiple scatter correction (MSC) and the Savitzky-Golay first derivative (SG 1st). Five characteristic wavelength methods were compared: successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), variable important in projection (VIP), and random frog (RF). Partial least square-discriminant analysis (PLS-DA) and K-nearest neighbor (KNN) built the vitality estimation model based on different data sets, and GA + PLS-DA constructed the optimal model with the highest accuracy. According to the weight coefficient and reflectance of the characteristic band extracted by the GA, the reflectance curves of different levels over time were plotted. The data of 0 h was employed to establish the vitality forecast model. The forecast model had a high recognition rate, with PLS-DA exceeding 99% and KNN exceeding 85%. This indicated that hyperspectral imaging of seed germination processes could achieve non-destructive estimation of Q. variabilis seed vitality, and accurate prediction in a shorter time is feasible.

Sensors (Basel) ; 17(4)2017 Apr 12.
Artigo em Inglês | MEDLINE | ID: mdl-28417907


This paper presents a viability assessment method for Pisum sativum L. seeds based on the infrared thermography technique. In this work, different artificial treatments were conducted to prepare seeds samples with different viability. Thermal images and visible images were recorded every five minutes during the standard five day germination test. After the test, the root length of each sample was measured, which can be used as the viability index of that seed. Each individual seed area in the visible images was segmented with an edge detection method, and the average temperature of the corresponding area in the infrared images was calculated as the representative temperature for this seed at that time. The temperature curve of each seed during germination was plotted. Thirteen characteristic parameters extracted from the temperature curve were analyzed to show the difference of the temperature fluctuations between the seeds samples with different viability. With above parameters, support vector machine (SVM) was used to classify the seed samples into three categories: viable, aged and dead according to the root length, the classification accuracy rate was 95%. On this basis, with the temperature data of only the first three hours during the germination, another SVM model was proposed to classify the seed samples, and the accuracy rate was about 91.67%. From these experimental results, it can be seen that infrared thermography can be applied for the prediction of seed viability, based on the SVM algorithm.

Termografia , Germinação , Ervilhas , Sementes , Máquina de Vetores de Suporte