Monitoring Wheat Powdery Mildew Based on Hyperspectral, Thermal Infrared, and RGB Image Data Fusion

Powdery mildew severely affects wheat growth and yield; therefore, its effective monitoring is essential for the prevention and control of the disease and global food security. In the present study, a spectroradiometer and thermal infrared cameras were used to obtain hyperspectral signature and thermal infrared images data, and thermal infrared temperature parameters (TP) and texture features (TF) were extracted from the thermal infrared images and RGB images of wheat with powdery mildew, during the wheat flowering and filling periods. Based on the ten vegetation indices from the hyperspectral data (VI), TF and TP were integrated, and partial least square regression, random forest regression (RFR), and support vector machine regression (SVR) algorithms were used to construct a prediction model for a wheat powdery mildew disease index. According to the results, the prediction accuracy of RFR was higher than in other models, under both single data source modeling and multi-source data modeling; among the three data sources, VI was the most suitable for powdery mildew monitoring, followed by TP, and finally TF. The RFR model had stable performance in multi-source data fusion modeling (VI&TP&TF), and had the optimal estimation performance with 0.872 and 0.862 of R2 for calibration and validation, respectively. The application of multi-source data collaborative modeling could improve the accuracy of remote sensing monitoring of wheat powdery mildew, and facilitate the achievement of high-precision remote sensing monitoring of crop disease status.

Transformations in the Photogrammetric Co-Processing of Thermal Infrared Images and RGB Images

The photogrammetric processing of thermal infrared (TIR) images deals with several difficulties. TIR images ordinarily have low-resolution and the contrast of the images is very low. These factors strongly complicate the photogrammetric processing, especially when a modern structure from motion method is used. These factors can be avoided by a certain co-processing method of TIR and RGB images. Two of the solutions of co-processing were suggested by the authors and are presented in this article. Each solution requires a different type of transformation–plane transformation and spatial transformation. Both types of transformations are discussed in this paper. On the experiments that were performed, there are presented requirements, advantages, disadvantages, and results of the transformations. Both methods are evaluated mainly in terms of accuracy. The transformations are presented on suggested methods, but they can be easily applied to different kinds of methods of co-processing of TIR and RGB images.

Investigation of Submarine Groundwater Discharge using Thermal Satellite and Radon mapping along the East Coast of the Tamil Nadu and Pondicherry Region, India

Submarine groundwater discharge (SGD) demarcated as a significant component of hydrological cycle found to discharge greater volumes of terrestrial fresh and recirculated seawater to the ocean associated with chemical constituents (nutrients, metals, and organic compounds) aided by downward hydraulic gradient and sediment-water exchange. Delineating SGD is of primal significance due to the transport of nutrients and contaminants due to domestic, industrial, and agricultural practices that influence the coastal water quality, ecosystems, and geochemical cycles. An attempt has been made to demarcate the SGD using thermal infrared images and radon-222 (222Rn) isotope. Thermal infrared images processed from LANDSAT-8 data suggest prominent freshwater fluxes with higher temperature anomalies noted in Cuddalore and Nagapattinam districts, and lower temperature noted along northern and southern parts of the study area suggest saline/recirculated discharge. Groundwater samples were collected along the coastal regions to analyze Radon and Physico-chemical constituents. Radon in groundwater ranges between 127.39 Bq m-3 and 2643.41 Bq m-3 with an average of 767.80 Bq m-3. Calculated SGD fluxes range between -1.0 to 26.5 with an average of 10.32 m day-1. Comparison of the thermal infrared image with physio-chemical parameters and Radon suggest fresh, terrestrial SGD fluxes confined to the central parts of the study area and lower fluxes observed along with the northern and southern parts of the study area advocate impact due to seawater intrusion and recirculated seawater influence.

Real-Time Human Detection in Thermal Infrared Images

Human detection is a technology that detects pre-determined human shapes in the image and ignores everything else, which plays an irreplaceable role in video surveillance. However, modern person detectors have some inefficiencies in detecting pedestrians at night, and the accuracy rate is still insufficient. This paper presents a novel practical model for automatic real-time human detection at night-time. For this purpose, a new network architecture was proposed by improving the ting-yolov3 network for detecting pedestrians from TIR images based on the YOLO algorithm's tasks. The K-means clustering method clusters the image data, which contributes to obtaining excellent priority bounding-boxes. The proposed network was pre-trained on the original COCO dataset to obtain the initial weights. Through the comparison with the other three methods on the FLIR and DHU Night datasets showed that the proposed method performance was outperformed, in addition, to achieve a high score of accuracy (mAP%) in the TIR images. The method has a delay in detection time of 4.88ms. By improving the performance rates of human detection in TIR images, we expect this research to detect intruders in the night surveillance system.