School of Technology, Beijing Forestry University, Beijing 100083, China.
Sensors (Basel). 2017 Apr 12;17(4):845. doi: 10.3390/s17040845.
This paper presents a viability assessment method for 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.
本文提出了一种基于红外热成像技术的种子活力评估方法。在这项工作中，通过不同的人工处理，制备了具有不同活力的种子样本。在标准的五天发芽试验中，每五分钟记录一次热图像和可见图像。试验结束后，测量每个样本的根长，作为种子活力指数。利用边缘检测方法对可见图像中的每个个体种子区域进行分割，计算红外图像中对应区域的平均温度，作为该种子在该时刻的代表温度。绘制每个种子在发芽过程中的温度曲线。分析从温度曲线中提取的 13 个特征参数，以显示不同活力种子样本之间温度波动的差异。利用上述参数，通过支持向量机（SVM）将种子样本分为三类：有活力、老化和死亡，根据根长的分类准确率为 95%。在此基础上，仅利用发芽前三个小时的温度数据，提出了另一个 SVM 模型来对种子样本进行分类，准确率约为 91.67%。从这些实验结果可以看出，基于 SVM 算法，红外热成像技术可用于预测种子活力。
