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[IEEE 2018 IEEE Sciences and Humanities International Research Conference (SHIRCON) - Lima, Peru (2018.11.20-2018.11.22)] 2018 IEEE Sciences and Humanities International Research Conference (SHIRCON) - A Visible Fluorescence Method Induced by UV Radiation for Detection of Infestation in Canary Beans
摘要： The proposed study aims to present an algorithm for the detection of infestation of canary beans of the species "Phaseolus Vulgaris" by generating a visible fluorescence under UV radiation, which allows the bean to be distinguished as healthy or infested. Currently, since many of the symptoms of infestation cannot be detected by the human eye, the beans sample analysis is highly subjective. The proposed method uses images of the beans taken under UV radiation within a hermetic enclosure. Then the image is acquired and an image segmentation algorithm is executed in order to identify the beans. Each bean is labeled so that the infestation can be detected by an algorithm based on histogram analysis. For the validation of the proposed method, several samples were evaluated and the results were compared with those obtained by two experts through an exhaustive visual analysis. The results were expressed through specificity and sensitivity, obtaining 99.78% for specificity and 90.70% for sensitivity.
关键词： canary bean,UV radiation,image processing,infestation,visible fluorescence,specificity,sensitivity,detection
更新于2025-09-23 15:22:29
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Continuous-wave Terahertz Imaging Applied to Detect Infestations Caused by Insects in Grain
摘要： Detection infestations caused by insects in grain are important control measures for ensuring storage longevity, seed quality and food safety. The efficiency of the continuous wave terahertz imaging method to detect infestations caused by insects in wheat kernels was determined in this study. A continuous wave terahertz experimental setup was designed for recording of THz images corresponding to different infestations caused by different life stages of insects. The experimental results indicate that the absorbance is generally highest for un-infested wheat kernels and decreased at later growth stages from THz pseudo-color images. Our study intended to demonstrate how the method of continuous wave Terahertz imaging could be applied to detect Infestations Caused by Insects in Grain.
关键词： terahertz imaging,wheat grain,infestation,insect,Detection
更新于2025-09-23 15:22:29
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Remote sensing of cropping practice in Northern Italy using time-series from Sentinel-2
摘要： Maps of cropping practice, including the level of weed infestation, are useful planning tools e.g. for the assessment of the environmental impact of the crops, and Northern Italy is an important example due to the large and diverse agricultural production and the high weed infestation. Sentinel-2A is a new satellite with a high spatial and temporal resolution which potentially allows the creation of detailed maps of cropping practice including weed infestation. To explore the applicability of Sentinel-2A for mapping cropping practice, we analysed the Normalised Differential Vegetation Index (NDVI) time series from five weed-infested crop fields as well as the areas designated as non-irrigated agricultural land in Corine Land Cover, which also contributed to an increased understanding of the cropping practice in the region. The analysis of the case studies showed that the temporal resolution of Sentinel-2A was high enough to distinguish the gross features of the cropping practice, and that high weed infestations can be detected at this spatial resolution. The analysis of the entire region showed the potential for mapping cropping practice using Sentinel-2. In conclusion, Sentinel-2A is to some extent applicable for mapping cropping practice with reasonable thematic accuracy.
关键词： Clustering,Phenology,Weed infestation,NDVI,Time-series analysis
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Evaluating the Potential of Sentinel-2 for Low Severity Mites Infestation Detection in Grapes
摘要： The Mite is one of the major sucking pests in grape which goes undetected in its initial phase as the symptoms are not easily visible to the naked eyes. In this paper, we address the problem of mites infestation detection using temporal hyper-spectral data and also evaluate the potential of using Sentinel-2 data for mites infestation detection. The reflectance data from grape leaves with healthy and low infestations of mites have been collected using spectroradiometer. The hyperspectral remote sensing data is collected from 213 bands with wavelength ranging from 350nm to 1052nm during 15th Jan-18th Feb 2017. Variations observed in the spectral reflectance over time makes the detection based on multi-temporal data difficult. Data in 213 narrow contiguous bands is used as feature set for hyperspectral data analysis but this large feature set may cause the over-fitting problem and also poses the requirement of large storage and greater processing time. To avoid this, feature selection using Least Absolute Shrinkage and Selection Operator (LASSO) has been carried out to get the optimum band set. Features selected by LASSO were fed to classifiers such as Random Forest (RF), Artificial Neural Network (ANN) and Logistic Regression (LR) to evaluate their performance. Results suggest that LR based model provides maximum accuracy of 93.24%. In addition to this, to investigate the potential of using Sentinel-2, data in 213 narrow bands were simulated to Sentinel-2. Data has been simulated to 10 and 20m spatial resolution bands available in 350-1050nm range. This simulated 8 band feature set has been fed to the same set of classifiers to evaluate their performance. Results suggests that LR provides maximum classification accuracy of 89.12% using simulated Sentinel-2 bands. Further to validate the algorithm using actual ground observations from the field, we have implemented simulated Sentinel-2 based algorithm on two Sentinel-2 images available during the study period and results are compared with actual ground observations about mites infestation. Results suggests mites detection accuracy of 83.33% which shows the good agreement and potential of Sentinel-2 for mites infestation detection.
关键词： Sentinel-2,Classification,Multi-temporal Sensing,Hyperspectral Remote Sensing,Pest Infestation
更新于2025-09-10 09:29:36