基于热红外和RGB图像的番茄苗期高温胁迫检测方法

    Detection method of high temperature stress of tomato at seedling stage based on thermal infrared and RGB images

    • 摘要:
      目的  针对实际生产场景中番茄苗期生长遇到的高温胁迫问题,提出一种基于热红外和RGB图像的番茄苗期高温胁迫检测方法。
      方法  首先,通过番茄苗期热红外图像反演获取番茄冠层温度参数,采用偏最小二乘(Partial least squares, PLS)模型提取冠层温度特征指标;然后,建立采用3种不同主干特征提取网络的Mask-RCNN模型,通过迁移学习的方式将番茄苗期RGB图像输入Mask-RCNN模型,进行高温胁迫症状实例分割,得到番茄苗期胁迫症状特征指标;最后,利用提取的温度和胁迫症状特征指标构建分级数据集,输入高温胁迫分级模型,得到高温胁迫等级。
      结果  基于PLS模型提取的冠层温度特征指标累计贡献率达95.45%;基于ResNet101+Mask-RCNN的高温胁迫症状分割网络对番茄苗期轻度和重度胁迫的分割精度最高,均值平均查准率(Mean average precision, mAP)分别为77.3%和73.8%;基于温度和胁迫症状特征指标构建的4种高温胁迫分级模型中,反向传播神经网络(Back propagation neural network, BPNN)获得最好的高温胁迫分级效果,分级准确率达95.6%。
      结论  该方法对番茄苗期高温胁迫检测效果较好,可为番茄苗期高温胁迫早期精准检测和快速自动预警提供技术支撑。

       

      Abstract:
      Objective  Aiming at the problem of high temperature stress encountered in the growth of tomato at seedling stage in actual production scenarios, a method for detecting high temperature stress of tomato at seedling stage based on thermal infrared and RGB images was proposed.
      Method  Firstly, the tomato canopy temperature parameters were obtained by inversion through the thermal infrared image of tomato plant at seedling stage, and the canopy temperature characteristic indicators were extracted by the partial least squares (PLS) model. Then, a Mask-RCNN model using three different backbone feature extraction networks was established, and the RGB images of tomato seedlings were input into the Mask-RCNN model by means of transfer learning for instance segmentation of high temperature stress symptoms. The characteristic indicators of tomato stress symptoms at seedling stage were obtained. Finally, the extracted temperature and stress symptom characteristic indicators were used to construct a hierarchical data set and fed into the high temperature stress classification model to obtain the high temperature stress level.
      Result  The cumulative contribution rate of the canopy temperature characteristic indicators extracted based on the PLS model reached 95.45%. The high temperature stress symptom segmentation network based on ResNet101+Mask-RCNN had the highest segmentation accuracy for mild and severe stress of tomato at seedling stage, with mean average precision (mAP) of 77.3% and 73.8% respectively. Among the four high temperature stress grading models constructed based on temperature and stress symptom characteristic indicators, the back propagation neural network (BPNN) showed the best high temperature stress grading performance with the grading accuracy rate of 95.6%.
      Conclusion  The proposed method in this study achieves better detection performance for high temperature stress of tomato at seedling stage, and provides a technical support for early detection and rapid automatic warning of high temperature stress of tomato at seedling stage.

       

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