Добірка наукової літератури з теми "Hyperspectral Phenotyping"
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Статті в журналах з теми "Hyperspectral Phenotyping"
Huang, Peikui, Xiwen Luo, Jian Jin, Liangju Wang, Libo Zhang, Jie Liu, and Zhigang Zhang. "Improving High-Throughput Phenotyping Using Fusion of Close-Range Hyperspectral Camera and Low-Cost Depth Sensor." Sensors 18, no. 8 (August 17, 2018): 2711. http://dx.doi.org/10.3390/s18082711.
Повний текст джерелаSarić, Rijad, Viet D. Nguyen, Timothy Burge, Oliver Berkowitz, Martin Trtílek, James Whelan, Mathew G. Lewsey, and Edhem Čustović. "Applications of hyperspectral imaging in plant phenotyping." Trends in Plant Science 27, no. 3 (March 2022): 301–15. http://dx.doi.org/10.1016/j.tplants.2021.12.003.
Повний текст джерелаKuska, Matheus Thomas, Anna Brugger, Stefan Thomas, Mirwaes Wahabzada, Kristian Kersting, Erich-Christian Oerke, Ulrike Steiner, and Anne-Katrin Mahlein. "Spectral Patterns Reveal Early Resistance Reactions of Barley Against Blumeria graminis f. sp. hordei." Phytopathology® 107, no. 11 (November 2017): 1388–98. http://dx.doi.org/10.1094/phyto-04-17-0128-r.
Повний текст джерелаSadeghi-Tehran, Pouria, Nicolas Virlet, and Malcolm J. Hawkesford. "A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery." Remote Sensing 13, no. 5 (February 27, 2021): 898. http://dx.doi.org/10.3390/rs13050898.
Повний текст джерелаBrugger, Anna, Jan Behmann, Stefan Paulus, Hans-Georg Luigs, Matheus Thomas Kuska, Patrick Schramowski, Kristian Kersting, Ulrike Steiner, and Anne-Katrin Mahlein. "Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range." Remote Sensing 11, no. 12 (June 12, 2019): 1401. http://dx.doi.org/10.3390/rs11121401.
Повний текст джерелаBanerjee, Bikram P., Sameer Joshi, Emily Thoday-Kennedy, Raj K. Pasam, Josquin Tibbits, Matthew Hayden, German Spangenberg, and Surya Kant. "High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response." Journal of Experimental Botany 71, no. 15 (March 18, 2020): 4604–15. http://dx.doi.org/10.1093/jxb/eraa143.
Повний текст джерелаBehmann, Jan, Anne-Katrin Mahlein, Stefan Paulus, Heiner Kuhlmann, Erich-Christian Oerke, and Lutz Plümer. "Calibration of hyperspectral close-range pushbroom cameras for plant phenotyping." ISPRS Journal of Photogrammetry and Remote Sensing 106 (August 2015): 172–82. http://dx.doi.org/10.1016/j.isprsjprs.2015.05.010.
Повний текст джерелаOerke, Erich-Christian, Katja Herzog, and Reinhard Toepfer. "Hyperspectral phenotyping of the reaction of grapevine genotypes toPlasmopara viticola." Journal of Experimental Botany 67, no. 18 (August 27, 2016): 5529–43. http://dx.doi.org/10.1093/jxb/erw318.
Повний текст джерелаRibes, Mathieu, Gaspard Russias, Denis Tregoat, and Antoine Fournier. "Towards Low-Cost Hyperspectral Single-Pixel Imaging for Plant Phenotyping." Sensors 20, no. 4 (February 19, 2020): 1132. http://dx.doi.org/10.3390/s20041132.
Повний текст джерелаMa, Dongdong, Tanzeel U. Rehman, Libo Zhang, Hideki Maki, Mitchell R. Tuinstra, and Jian Jin. "Modeling of Environmental Impacts on Aerial Hyperspectral Images for Corn Plant Phenotyping." Remote Sensing 13, no. 13 (June 28, 2021): 2520. http://dx.doi.org/10.3390/rs13132520.
Повний текст джерелаДисертації з теми "Hyperspectral Phenotyping"
Alisaac, Elias [Verfasser]. "Phenotyping of wheat resistance to Fusarium head blight using hyperspectral imaging / Elias Alisaac." Bonn : Universitäts- und Landesbibliothek Bonn, 2021. http://d-nb.info/1228978948/34.
Повний текст джерелаLeucker, Marlene [Verfasser]. "Phenotyping of Cercospora beticola resistance of sugar beet genotypes by hyperspectral imaging / Marlene Leucker." Bonn : Universitäts- und Landesbibliothek Bonn, 2018. http://d-nb.info/1163013196/34.
Повний текст джерелаMakdessi, Nathalie Al. "Couplage entre modélisation opto-physique des scènes de végétation complexes et chimiométrie : application au phénotypage par imagerie hyperspectrale de proximité." Thesis, Montpellier, 2017. http://www.theses.fr/2017MONTS017.
Повний текст джерелаShort range hyperspectral imagery is a promising tool for phenotyping and vegetation survey. When associated with partial least square regression (PLS-R), it allows high spatial resolution mapping of the plant chemical content at the canopy scale. However, several optical phenomena have to be taken into account when applying this approach to vegetation scenes in natural conditions. For instance, additive and multiplicative factors due respectively to specular reflection and leaf inclination can be overcome by spectral preprocessing. But the most challenging phenomenon is multiple scattering. It appears when a leaf is partly lightened by the reflected or transmitted light from surrounding leaves, resulting in strong non linear effects in its apparent reflectance spectrum. Though this effect can be taken into account in some remote sensing models at the canopy scale, no study has been proposed until now concerning its impact on spectral prediction of vegetation chemical content by short range imagery.The objective of this project, associated with a PhD work, was to analyze these effects in the context of hyperspectral imagery for vegetation phenotyping purpose, and to propose spectral processing methods to overcome them.The methodological development has been based on simulation tools included in the open source platform OpenAlea (http://openalea.gforge.inria.fr/dokuwiki/doku.php). A typical wheat canopy scene has been modelled using Adel-Wheat and combined with the light propagation model Caribu. The proposed tool simulates the apparent reflectance of every visible leaf in the canopy for a given actual reflectance and transmittance, allowing to synthetize realistic hyperspectral images.This simulation approach has allowed us, in a first step, to analyze the distribution of deviations due to multiple scattering in the spectral space, and then to infer a correction method in the frame of PLS regression. This method relies on the building of two subspaces EW and EB respectively generated by the analytic formulation of multiple scattering and by the variable of interest. It allows us to define a projection operation on EB subspace along EW direction (oblique projection), in order to remove multiple scattering effects while preserving useful information. This projection operation is then applied on every spectra during learning phase and using phase of the PLS model.The method has first been developed and tuned using simulated data, in the frame of leaf nitrogen content (LNC) prediction of wheat leaves. For this purpose, reflectance spectra (450-1100 nm) of 57 wheat leaves have been collected using a ASD filed spectrometer (FieldSpec®, Analytical Spectral Devices, Inc., Boulder, Colorado, USA), while their LNC was measured through reference chemical analyses. Regression models with and without oblique projection have then been built from the ASD spectra and applied to simulated data. The model with oblique projection provided excellent results (R² = 0.931; RMSEP = 0.29% DM), compared to the classical one (R² = 0.915; RMSEP = 0.42% DM).The same method has then been applied in real conditions on wheat pot plants and field plants. For this purpose, some leaves have been collected and laid on a black paper background to be imaged, in order to build PLS models that have then been applied on in-situ plants. These experimentations have confirmed that the classical PLS-R induces a strong overestimation of LNC on leaves surrounded by other leaves, and that oblique projection corrects this overestimation (same prediction on surrounded then isolated leaf)
(9224231), Dongdong Ma. "Ameliorating Environmental Effects on Hyperspectral Images for Improved Phenotyping in Greenhouse and Field Conditions." Thesis, 2020.
Знайти повний текст джерелаAngel, Yoseline. "Monitoring crop development and health using UAV-based hyperspectral imagery and machine learning." Diss., 2021. http://hdl.handle.net/10754/670149.
Повний текст джерелаMakdessi, Nathalie al. "Couplage entre modélisation opto-physique des scènes de végétation complexes et chimiométrie : application au phénotypage par imagerie hyperspectrale de proximité." Thesis, 2017. http://www.theses.fr/2017MONTS017/document.
Повний текст джерелаShort range hyperspectral imagery is a promising tool for phenotyping and vegetation survey. When associated with partial least square regression (PLS-R), it allows high spatial resolution mapping of the plant chemical content at the canopy scale. However, several optical phenomena have to be taken into account when applying this approach to vegetation scenes in natural conditions. For instance, additive and multiplicative factors due respectively to specular reflection and leaf inclination can be overcome by spectral preprocessing. But the most challenging phenomenon is multiple scattering. It appears when a leaf is partly lightened by the reflected or transmitted light from surrounding leaves, resulting in strong non linear effects in its apparent reflectance spectrum. Though this effect can be taken into account in some remote sensing models at the canopy scale, no study has been proposed until now concerning its impact on spectral prediction of vegetation chemical content by short range imagery.The objective of this project, associated with a PhD work, was to analyze these effects in the context of hyperspectral imagery for vegetation phenotyping purpose, and to propose spectral processing methods to overcome them.The methodological development has been based on simulation tools included in the open source platform OpenAlea (http://openalea.gforge.inria.fr/dokuwiki/doku.php). A typical wheat canopy scene has been modelled using Adel-Wheat and combined with the light propagation model Caribu. The proposed tool simulates the apparent reflectance of every visible leaf in the canopy for a given actual reflectance and transmittance, allowing to synthetize realistic hyperspectral images.This simulation approach has allowed us, in a first step, to analyze the distribution of deviations due to multiple scattering in the spectral space, and then to infer a correction method in the frame of PLS regression. This method relies on the building of two subspaces EW and EB respectively generated by the analytic formulation of multiple scattering and by the variable of interest. It allows us to define a projection operation on EB subspace along EW direction (oblique projection), in order to remove multiple scattering effects while preserving useful information. This projection operation is then applied on every spectra during learning phase and using phase of the PLS model.The method has first been developed and tuned using simulated data, in the frame of leaf nitrogen content (LNC) prediction of wheat leaves. For this purpose, reflectance spectra (450-1100 nm) of 57 wheat leaves have been collected using a ASD filed spectrometer (FieldSpec®, Analytical Spectral Devices, Inc., Boulder, Colorado, USA), while their LNC was measured through reference chemical analyses. Regression models with and without oblique projection have then been built from the ASD spectra and applied to simulated data. The model with oblique projection provided excellent results (R² = 0.931; RMSEP = 0.29% DM), compared to the classical one (R² = 0.915; RMSEP = 0.42% DM).The same method has then been applied in real conditions on wheat pot plants and field plants. For this purpose, some leaves have been collected and laid on a black paper background to be imaged, in order to build PLS models that have then been applied on in-situ plants. These experimentations have confirmed that the classical PLS-R induces a strong overestimation of LNC on leaves surrounded by other leaves, and that oblique projection corrects this overestimation (same prediction on surrounded then isolated leaf)
(8789954), Ali Masjedi. "MULTI-TEMPORAL MULTI-MODAL PREDICTIVE MODELLING OF PLANT PHENOTYPES." Thesis, 2020.
Знайти повний текст джерелаHigh-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this study, the potential of accurate and reliable sorghum biomass prediction using hyperspectral and LiDAR data acquired by sensors mounted on UAV platforms is investigated. Experiments comprised multiple varieties of grain and forage sorghum, including some photoperiod sensitive varieties, providing an opportunity to evaluate a wide range of genotypes and phenotypes.
Feature extraction is investigated, where various novel features, as well as traditional features, are extracted directly from the hyperspectral imagery and LiDAR point cloud data and input to classical machine learning (ML) regression based models. Predictive models are developed for multiple experiments conducted during the 2017, 2018, and 2019 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Using geometric based features derived from the LiDAR point cloud and the chemistry-based features extracted from hyperspectral data provided the most accurate predictions. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method. The characteristics of the experiments, including the number of samples and the type of sorghum genotypes in the experiment also impacted prediction accuracy.
Including the genomic information and weather data in the “multi-year” predictive models is also investigated for prediction of the end of season biomass. Models based on one and two years of data are used to predict the biomass yield for the future years. The results show the high potential of the models for biomass and biomass rank predictions. While models developed using one year of data are able to predict biomass rank, using two years of data resulted in more accurate models, especially when RS data, which encode the environmental variation, are included. Also, the possibility of developing predictive models using the RS data collected until mid-season, rather than the full season, is investigated. The results show that using the RS data until 60 days after sowing (DAS) in the models can predict the rank of biomass with R2 values of around 0.65-0.70. This not only reduces the time required for phenotyping by avoiding the manual sampling process, but also decreases the time and the cost of the RS data collections and the associated challenges of time-consuming processing and analysis of large data sets, and particularly for hyperspectral imaging data.
In addition to extracting features from the hyperspectral and LiDAR data and developing classical ML based predictive models, supervised and unsupervised feature learning based on fully connected, convolutional, and recurrent neural networks is also investigated. For hyperspectral data, supervised feature extraction provides more accurate predictions, while the features extracted from LiDAR data in an unsupervised training yield more accurate prediction.
Predictive models based on Recurrent Neural Networks (RNNs) are designed and implemented to accommodate high dimensional, multi-modal, multi-temporal data. RS data and weather data are incorporated in the RNN models. Results from multiple experiments focused on high throughput phenotyping of sorghum for biomass predictions are provided and evaluated. Using proposed RNNs for training on one experiment and predicting biomass for other experiments with different types of sorghum varieties illustrates the potential of the network for biomass prediction, and the challenges relative to small sample sizes, including weather and sensitivity to the associated ground reference information.
Частини книг з теми "Hyperspectral Phenotyping"
Gampa, Suraj, and Rubi Quiñones. "Data-Driven Techniques for Plant Phenotyping Using Hyperspectral Imagery." In Intelligent Image Analysis for Plant Phenotyping, 175–94. First edition. | Boca Raton, FL : CRC Press, 2021.: CRC Press, 2020. http://dx.doi.org/10.1201/9781315177304-11.
Повний текст джерелаBhugra, Swati, Nitish Agarwal, Shubham Yadav, Soham Banerjee, Santanu Chaudhury, and Brejesh Lall. "Extraction of Phenotypic Traits for Drought Stress Study Using Hyperspectral Images." In Lecture Notes in Computer Science, 608–14. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69900-4_77.
Повний текст джерелаPotgieter, Andries B., James Watson, Barbara George-Jaeggli, Gregory McLean, Mark Eldridge, Scott C. Chapman, Kenneth Laws, et al. "The Use of Hyperspectral Proximal Sensing for Phenotyping of Plant Breeding Trials." In Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation, 127–47. CRC Press, 2018. http://dx.doi.org/10.1201/9781315164151-5.
Повний текст джерелаТези доповідей конференцій з теми "Hyperspectral Phenotyping"
Arnold, Thomas, Raimund Leitner, and Gernot Bodner. "Near infrared hyperspectral imaging system for root phenotyping." In Sensing for Agriculture and Food Quality and Safety IX, edited by Moon S. Kim, Byoung-Kwan Cho, Bryan A. Chin, and Kuanglin Chao. SPIE, 2017. http://dx.doi.org/10.1117/12.2262441.
Повний текст джерелаAccettura, Margot, Carl Salvaggio, Timothy Bauch, Joe Mallia, and Nina Raqueno. "Hyperspectral detection of methane stressed vegetation." In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III, edited by J. Alex Thomasson, Mac McKee, and Robert J. Moorhead. SPIE, 2018. http://dx.doi.org/10.1117/12.2304045.
Повний текст джерелаHurley, Sean, Marc Horney, and Aaron Drake. "Using hyperspectral imagery to detect water stress in vineyards." In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, edited by J. Alex Thomasson, Mac McKee, and Robert J. Moorhead. SPIE, 2019. http://dx.doi.org/10.1117/12.2518660.
Повний текст джерелаZhu, Feiyu, Yu Pan, Tian Gao, Harkamal Walia, and Hongfeng Yu. "Interactive Visualization of Time-Varying Hyperspectral Plant Images for High-Throughput Phenotyping." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006003.
Повний текст джерелаLuculescu, Marius Cristian, Luciana Cristea, Sorin Constantin Zamfira, and Attila Laszlo Boer. "Using hyperspectral sensors for crop vegetation status monitoring in precision agriculture." In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III, edited by J. Alex Thomasson, Mac McKee, and Robert J. Moorhead. SPIE, 2018. http://dx.doi.org/10.1117/12.2305156.
Повний текст джерелаBhandari, Subodh, Amar Raheja, Mohammad R. Chaichi, Frank H. Pham, Tristan M. Sherman, Matthew B. Dohlen, and Sharafat U. Khan. "Lettuce plant health assessment using UAV-based hyperspectral sensor and proximal sensors." In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V, edited by J. Alex Thomasson and Alfonso F. Torres-Rua. SPIE, 2020. http://dx.doi.org/10.1117/12.2557686.
Повний текст джерелаBagherian, Kamand, Rafael Bidese Puhl, Yin Bao, Qiong Zhang, Alvaro Sanz-Saez, Charles Chen, and Phat Dang. "Phenotyping Agronomic Traits of Peanuts using UAV-based Hyperspectral Imaging and Deep Learning." In 2022 Houston, Texas July 17-20, 2022. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2022. http://dx.doi.org/10.13031/aim.202200814.
Повний текст джерелаXie, Chuanqi, Ce Yang, and Ali Moghimi. "Detection of cold stressed maize seedlings for high throughput phenotyping using hyperspectral imagery." In SPIE Commercial + Scientific Sensing and Imaging, edited by David P. Bannon. SPIE, 2017. http://dx.doi.org/10.1117/12.2262781.
Повний текст джерелаHamidisepehr, Ali, and Michael Sama. "A low-cost method for collecting hyperspectral measurements from a small unmanned aircraft system." In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III, edited by J. Alex Thomasson, Mac McKee, and Robert J. Moorhead. SPIE, 2018. http://dx.doi.org/10.1117/12.2305934.
Повний текст джерелаBhandari, Subodh, Amar Raheja, Mohammad R. Chaichi, Frank Pham, Tristan Sherman, Matt Dohlen, and Sharafat Khan. "Comparing the effectiveness of hyperspectral and multispectral data in detecting citrus nitrogen and water stresses." In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, edited by J. Alex Thomasson, Mac McKee, and Robert J. Moorhead. SPIE, 2019. http://dx.doi.org/10.1117/12.2518822.
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