基于热红外成像技术的油菜菌核病早期检测研究

摘要

将热红外成像技术和低空遥感技术相结合, 基于冠层和叶片两个尺度对菌核病侵染油菜的过程进行检测研究.从冠层尺度分析, 首先获取整株样本的温度值 (平均温度与最大温差), 并采集其生理指数 (气孔导度、 CO2浓度、蒸腾速率及光合速率).然后, 将染病样本与健康样本的温度值进行判别分析, 并对其进行单因素方差分析.从结果可知, 平均温度和最大温差值都可以对染病样本与健康样本进行区分, 且最大温差相较平均温度结果较明显.同时单因素方差分析也显示, 最大温差三次检测中均存在显著性差异.对获取的生理指数进行分析, 发现染病样本与健康样本之间可以通过生理指数进行明显区分.另外, 将生理指数与叶片温度进行相关性分析, 结果表明二氧化碳浓度与叶片温度之间的三次检测均存在显著性差异.基于叶片尺度, 首先从单一叶片来看健康区域和染病区域的温度差异, 可以明显区分出染病区域和健康区域的温度差异.然后, 提取健康区域与染病区域的的温度值 (最大温度、最小温度、平均温度以及最大温差) 对进行对比分析, 并对其进行单因素方差分析.结果表明, 以上四个温度指标均可以区分叶片的染病区域和健康区域.但根据单因素方差分析结果可知, 与冠层尺度相同, 最大温差三次检测中均存在显著性差异, 可以实现对油菜菌核病的早期识别.%The early identification of sclerotinia sclerotiorum rot of oilseed rape was observed from the canopy and leaf scale by using the thermal infrared imager based on the UAV simulation platform. The thermal infrared image data were obtained from the canopy scale, and the temperature values of the canopy scale were extracted, and the rape leaves were monitored for acquiring the physiological index. Then, the average temperature and the maximum temperature difference were used to compare the healthy and diseased samples and one-way ANOVA was also used. The results showed that the difference in the maximum temperature difference between healthy and diseased plants was obvious, and the difference in the average temperature between healthy and diseased plants was obvious with the number of days. The single factor analysis of variance showed that there was significant difference (p＜0.01) between the maximum temperature difference at the first day after rape infection. Furthermore, the physiological indexes (stomatal conductance, photosynthetic rate, carbon dioxide concentration and transpiration rate) of rapeseed were analyzed with the number of days. The changes could be used to detect the correlation between physiological index and temperature Sexual analysis. The results showed that there was a significant correlation between photosynthetic rate, carbon dioxide concentration and transpiration rate and temperature. The temperature information of the healthy and diseased areas in the diseased leaves of the samples was obtained. The thermal infrared image could visually identify the disease infection process and use the pixel value to estimate temperature difference between the health and the affected area. The healthy and diseased samples were identified by maximum temperature, minimum temperature, average temperature and maximum temperature difference. The results were compared with the single factor analysis of variance (ANOVA), showing that the maximum temperature, the minimum temperature, the maximum temperature difference and the average temperature in the healthy and infected areas were significantly different, and the lesion area temperature was higher than that of the healthy area. The single factor analysis of variance showed that there was significant difference (p＜0.01) in the maximum temperature difference at the first day, and the early identification of sclerotinia sclerotiorum could be realized.