Relevant bibliographies by topics / Crop parameter

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Crop parameter'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Crop parameter"

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Wijesingha, Jayan, Supriya Dayananda, Michael Wachendorf, and Thomas Astor. "Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters." Sensors 21, no. 8 (April 20, 2021): 2886. http://dx.doi.org/10.3390/s21082886.

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Various remote sensing data have been successfully applied to monitor crop vegetation parameters for different crop types. Those successful applications mostly focused on one sensor system or a single crop type. This study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters from three monsoon crops in tropical regions: finger millet, maize, and lablab. The study was conducted in two experimental field layouts (irrigated and rainfed) in Bengaluru, India, over the primary agricultural season in 2
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Wallach, Daniel, Bruno Goffinet, Jacques-Eric Bergez, Philippe Debaeke, Delphine Leenhardt, and Jean-Noël Aubertot. "Parameter Estimation for Crop Models." Agronomy Journal 93, no. 4 (July 2001): 757–66. http://dx.doi.org/10.2134/agronj2001.934757x.

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Stanghellini, C., and W. Th M. van Meurs. "CROP TRANSPIRATION: A GREENHOUSE CLIMATE CONTROL PARAMETER." Acta Horticulturae, no. 245 (August 1989): 384–88. http://dx.doi.org/10.17660/actahortic.1989.245.51.

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Jacobs, Adrie F. G., and John H. Van Boxel. "Computational parameter estimation for a maize crop." Boundary-Layer Meteorology 42, no. 3 (February 1988): 265–79. http://dx.doi.org/10.1007/bf00123816.

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Tremblay, Marie, and Daniel Wallach. "Comparison of parameter estimation methods for crop models." Agronomie 24, no. 6-7 (September 2004): 351–65. http://dx.doi.org/10.1051/agro:2004033.

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T. Zhai, R. H. Mohtar, F. El-Awar, W. Jabre, and J. J. Volenec. "PARAMETER ESTIMATION FOR PROCESS-ORIENTED CROP GROWTH MODELS." Transactions of the ASAE 47, no. 6 (2004): 2109–19. http://dx.doi.org/10.13031/2013.17796.

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Manoharan, Dr Samuel. "Supervised Learning for Microclimatic parameter Estimation in a Greenhouse environment for productive Agronomics." September 2020 2, no. 3 (July 17, 2020): 170–76. http://dx.doi.org/10.36548/jaicn.2020.3.004.

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Maximum crop returns are essential in modern agriculture due to various challenges caused by water, climatic conditions, pests and so on. These production uncertainties are to be overcome by appropriate evaluation of microclimate parameters at commercial scale for cultivation of crops in a closed-field and emission free environment. Internet of Things (IoT) based sensors are used for learning the parameters of the closed environment. These parameters are further analyzed using supervised learning algorithms under MATLAB Simulink environment. Three greenhouse crop production systems as well as
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Zhao, Xia, Xingchuan Wang, Guangchao Cao, Kelong Chen, Wenjia Tang, and Zhijun Zhang. "Crop Identification by Using Seasonal Parameters Extracted from Time Series Landsat Images in a Mountainous Agricultural County of Eastern Qinghai Province, China." Journal of Agricultural Science 9, no. 4 (March 14, 2017): 116. http://dx.doi.org/10.5539/jas.v9n4p116.

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Time series vegetable indexes (Vis) have been evidenced a useful data to extract vegetable phenology and identify crop types. This paper conducted such a research in Qinghai Province by using Landsat TM images, via four steps, i) sampling single-crop plots and extracting crop spectrums based on pure signle-crop pixels; ii) building time-series vegetable indexes by using Landsat 8 TM images (2013-2014); iii) extracting seasonal parameters according to algorithms defined in TIMESAT program; vi) generating a decision tree for identifying crop types and validate classification accuracy via ground
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Bahrami, Hazhir, Saeid Homayouni, Abdolreza Safari, Sayeh Mirzaei, Masoud Mahdianpari, and Omid Reisi-Gahrouei. "Deep Learning-Based Estimation of Crop Biophysical Parameters Using Multi-Source and Multi-Temporal Remote Sensing Observations." Agronomy 11, no. 7 (July 3, 2021): 1363. http://dx.doi.org/10.3390/agronomy11071363.

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Remote sensing data are considered as one of the primary data sources for precise agriculture. Several studies have demonstrated the excellent capability of radar and optical imagery for crop mapping and biophysical parameter estimation. This paper aims at modeling the crop biophysical parameters, e.g., Leaf Area Index (LAI) and biomass, using a combination of radar and optical Earth observations. We extracted several radar features from polarimetric Synthetic Aperture Radar (SAR) data and Vegetation Indices (VIs) from optical images to model crops’ LAI and dry biomass. Then, the mutual correl
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Zeng, Wenzhi, Yuchao Lu, Amit Kumar Srivastava, Thomas Gaiser, and Jiesheng Huang. "Parameter Sensitivity and Uncertainty of Radiation Interception Models for Intercropping System." Ecological Chemistry and Engineering S 27, no. 3 (September 1, 2020): 437–56. http://dx.doi.org/10.2478/eces-2020-0028.

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AbstractEstimating the interception of radiation is the first and crucial step for the prediction of production for intercropping systems. Determining the relative importance of radiation interception models to the specific outputs could assist in developing suitable model structures, which fit to the theory of light interception and promote model improvements. Assuming an intercropping system with a taller and a shorter crop, a variance-based global sensitivity analysis (EFAST) was applied to three radiation interception models (M1, M2 and M3). The sensitivity indices including main (Si) and
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Dissertations / Theses on the topic "Crop parameter"

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Perkins, Seth A. "Crop model review and sweet sorghum crop model parameter development." Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/14037.

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Master of Science<br>Department of Biological and Agricultural Engineering<br>Kyle Douglas-Mankin<br>Opportunities for alternative biofuel feedstocks are widespread for a number of reasons: increased environmental and economic concerns over corn production and processing, limitations in the use of corn-based ethanol to 57 billion L (15 billion gal) by the Energy Independence and Security Act (US Congress, 2007), and target requirements of 136 billion L (36 billion gal) of renewable fuel production by 2022. The objective of this study was to select the most promising among currently available
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Lamsal, Abhishes. "Crop model parameter estimation and sensitivity analysis for large scale data using supercomputers." Diss., Kansas State University, 2016. http://hdl.handle.net/2097/34576.

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Doctor of Philosophy<br>Department of Agronomy<br>Stephen M. Welch<br>Global crop production must be doubled by 2050 to feed 9 billion people. Novel crop improvement methods and management strategies are the sine qua non for achieving this goal. This requires reliable quantitative methods for predicting the behavior of crop cultivars in novel, time-varying environments. In the last century, two different mathematical prediction approaches emerged (1) quantitative genetics (QG) and (2) ecophysiological crop modeling (ECM). These methods are completely disjoint in terms of both their mathematics
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Coppa, Isabel Patricia Maria, and Isabel coppa@csw com au. "The use of remote sensing data for broad acre grain crop monitoring in Southeast Australia." RMIT University. Mathematical and Geospatial Sciences, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20070201.095831.

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In 2025, there will be almost 8 billion people to feed as the worlds population rapidly increases. To meet domestic and export demands, Australian grain productivity needs to approximately triple in the next 20 years, and this production needs to occur in an environmentally sustainable manner. The advent of Hi-tech Precision Farming in Australia has shown promise in recent time to optimize the use of resources. Most
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Abebe, Yibekal Alemayehu. "Managing the soil water balance of hot pepper (Capsicum annuum L.) to improve water productivity." Thesis, University of Pretoria, 2010. http://hdl.handle.net/2263/25257.

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A series of field, rainshelter, growth cabinet and modelling studies were conducted to investigate hot pepper response to different irrigation regimes and row spacings; to generate crop-specific model parameters; and to calibrate and validate the Soil Water Balance (SWB) model. Soil, climate and management data of five hot pepper growing regions of Ethiopia were identified to develop irrigation calendars and estimate water requirements of hot pepper under different growing conditions. High irrigation regimes increased fresh and dry fruit yield, fruit number, harvest index and top dry matter pr
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Rabe, Nicole J., and University of Lethbridge Faculty of Arts and Science. "Remote sensing of crop biophysical parameters for site-specific agriculture." Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2003, 2003. http://hdl.handle.net/10133/195.

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Support for sustainable agriculture by farmers and consumers is increasing as environmental and socio-economic issues rise due to more intensive farm practices. Site-specific crop management is an important component of sutainable agriculture, within which remote sensing can play an integral role. Field and image data were acquired over a farm in Saskatchewan as part of a national research project to demonstrate the advantages of site-specific agriculture for farmers. This research involved the estimation of crop biophysical parameters from airborne hyperspectral imagery using Spectral Mixture
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Jalali-Farahani, Hamid Reza 1960. "Crop water stress parameters for turfgrass and their environmental dependability." Thesis, The University of Arizona, 1987. http://hdl.handle.net/10150/191950.

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The concept of crop water stress index (CWSI) was explored using empirical and theoretical models to evaluate bermudagrass water status. The empirical methods were simplifications of the crop energy balance equation. Measured field data were employed to develop the empirical CWSI parameters. Field data were collected from turf plots under three levels of irrigation for the 1986 growing season in Tucson, Arizona. The simplest empirical model of Idso gave the highest variance in estimates of CWSI for all treatments with the estimates being highly influenced by net radiation. An improved empirica
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Burkart, Andreas [Verfasser]. "Multitemporal assessment of crop parameters using multisensorial flying platforms / Andreas Burkart." Bonn : Universitäts- und Landesbibliothek Bonn, 2016. http://d-nb.info/1096330075/34.

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Varella, Hubert Vincent. "Inversion d’un modèle de culture pour estimer spatialement les propriétés des sols et améliorer la prédiction de variables agro-environnementales." Thesis, Avignon, 2009. http://www.theses.fr/2009AVIG0638/document.

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Les modèles de culture constituent des outils indispensables pour comprendre l’influence des conditions agropédoclimatiques sur le système sol-plante à différentes échelles spatiales et temporelles. A l’échelle locale de la parcelle agricole, le modèle peut être utilisé dans le cadre de l’agriculture de précision pour optimiser les pratiques de fertilisation azotée de façon à maximiser le rendement ou le revenu tout en minimisant le lessivage des nitrates vers la nappe. Cependant, la pertinence de l’utilisation du modèle repose sur la qualité des prédictions réalisées, basée entre autres sur u
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Maqrot, Sara. "Méthodes d'optimisation combinatoire en programmation mathématique : Application à la conception des systèmes de verger-maraîcher." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30131.

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Dans le cadre du développement durable et des innovations dans les systèmes agroalimentaires, les systèmes mixtes horticoles (vergers et maraîchage) visent à répondre aux enjeux actuels auxquels l'agriculture est confrontée, à savoir une diminution de la pollution des sols, une meilleure gestion des ressources (eau, énergies) et un enrichissement de la biodiversité, tout en continuant d'assurer des fonctions alimentaires. Ils combinent des productions à la fois diversifiées et relativement intensifiées, leur permettant de s'insérer en périphérie urbaine. Ces systèmes agroforestiers reposent su
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Portz, Gustavo. "Use of crop canopy sensors in the measurement of sugarcane parameters aiming site-specific nitrogen fertilization management." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/11/11152/tde-17092015-101022/.

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Plant canopy sensors have emerged as a new tool for in field on-the-go spatially localized prediction of plant biomass and nitrogen (N) uptake by crops in an indirectly and plant indestructible way as base for N variable rate fertilization. Sensors based on the combination of specific reflectance bands from the electromagnetic spectrum constitute the vast majority of canopy sensors, and this principle has already been validated in many crops. Alternatively to this concept, the use of ultrasonic distance sensors to measure crop canopy height has been presented as an option to estimate biomass.
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Books on the topic "Crop parameter"

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Mandal, Dipankar, Avik Bhattacharya, and Yalamanchili Subrahmanyeswara Rao. Radar Remote Sensing for Crop Biophysical Parameter Estimation. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4424-5.

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Baron, William R. Growing season parameter reconstructions for New England using killing frost records, 1697-1947. Orono, Me: Maine Agricultural and Forest Research Station, 1996.

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Pal, S. K. Impact of climatic parameters on agricultural production and minimizing crop productivity losses through weather forecast and advisory service in SAARC countries. Dhaka: SAARC Agriculture Centre, 2012.

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Byrne, Robert James. An evaluation of the effect of nitrogen management programmes on plant nitrogen concentration in milling wheat and subsequent yield and quality parameters. Dublin: University College Dublin, 1998.

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Weather and Rice: Proceedings of the International Workshop on the Impact of Weather Parameters on Growth and Yield of Rice 7-10 April, 1986. Agribookstore, 1987.

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Busuioc, Aristita, and Alexandru Dumitrescu. Empirical-Statistical Downscaling: Nonlinear Statistical Downscaling. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.770.

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This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.The concept of statistical downscaling or empirical-statistical downscaling became a distinct and important scientific approach in climate science in recent decades, when the climate change issue and assessment of climate change impact on various social and natural systems have become international challenges. Global climate models are the best tools for estimating future climate conditions. Even if improvements can be made in state-of-the art
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Book chapters on the topic "Crop parameter"

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Akhter, Shamim, Keigo Sakamoto, Yann Chemin, and Kento Aida. "Parameter-Less GA Based Crop Parameter Assimilation with Satellite Image." In Computational Science and Its Applications – ICCSA 2009, 118–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02454-2_9.

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Mandal, Dipankar, Avik Bhattacharya, and Yalamanchili Subrahmanyeswara Rao. "Radar Vegetation Indices for Crop Growth Monitoring." In Radar Remote Sensing for Crop Biophysical Parameter Estimation, 177–228. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4424-5_7.

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Mandal, Dipankar, Avik Bhattacharya, and Yalamanchili Subrahmanyeswara Rao. "Biophysical Parameter Retrieval Using Compact-Pol SAR Data." In Radar Remote Sensing for Crop Biophysical Parameter Estimation, 155–76. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4424-5_6.

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Mandal, Dipankar, Avik Bhattacharya, and Yalamanchili Subrahmanyeswara Rao. "Introduction." In Radar Remote Sensing for Crop Biophysical Parameter Estimation, 1–6. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4424-5_1.

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Mandal, Dipankar, Avik Bhattacharya, and Yalamanchili Subrahmanyeswara Rao. "Vegetation Models: Empirical and Theoretical Approaches." In Radar Remote Sensing for Crop Biophysical Parameter Estimation, 37–72. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4424-5_3.

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Mandal, Dipankar, Avik Bhattacharya, and Yalamanchili Subrahmanyeswara Rao. "Evolution of Semi-empirical Approach: Modeling and Inversion." In Radar Remote Sensing for Crop Biophysical Parameter Estimation, 73–106. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4424-5_4.

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Mandal, Dipankar, Avik Bhattacharya, and Yalamanchili Subrahmanyeswara Rao. "Biophysical Parameter Retrieval Using Full- and Dual-Pol SAR Data." In Radar Remote Sensing for Crop Biophysical Parameter Estimation, 107–53. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4424-5_5.

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Mandal, Dipankar, Avik Bhattacharya, and Yalamanchili Subrahmanyeswara Rao. "Basic Theory of Radar Polarimetry." In Radar Remote Sensing for Crop Biophysical Parameter Estimation, 7–35. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4424-5_2.

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Mandal, Dipankar, Avik Bhattacharya, and Yalamanchili Subrahmanyeswara Rao. "Summary and Conclusions." In Radar Remote Sensing for Crop Biophysical Parameter Estimation, 229–34. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4424-5_8.

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Chen, Yuting, Samis Trevezas, and Paul-Henry Cournede. "Iterative convolution particle filtering for nonlinear parameter estimation and data assimilation with application to crop yield prediction." In 2013 Proceedings of the Conference on Control and its Applications, 67–74. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2013. http://dx.doi.org/10.1137/1.9781611973273.10.

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Conference papers on the topic "Crop parameter"

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Revill, Andrew, Anna Florence, Steve Hoad, Bob Rees, Alasdair MacArthur, and Mathew Williams. "UAV-Based Approaches for Crop Parameter Retrievals." In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8518284.

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Shastry, Aditya, Sanjay H A, and Madhura Hegde. "A parameter based ANFIS model for crop yield prediction." In 2015 IEEE International Advance Computing Conference (IACC). IEEE, 2015. http://dx.doi.org/10.1109/iadcc.2015.7154708.

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Yingying Dong, Jinkai Zhang, Zhijie Wang, Karl Staenz, Craig Coburn, Wei Xu, Xiaodong Yang, and Jihua Wang. "Method to speed up LUT-based crop canopy parameter mapping." In IGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2014. http://dx.doi.org/10.1109/igarss.2014.6946874.

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Tian, Xin, Erxue Chen, Zengyuan Li, Z. Bob Su, Feilong Ling, Lina Bai, and Fengyu Wang. "Comparison of crop classification capabilities of spaceborne multi-parameter SAR data." In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5651326.

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Kang, MengZhen, XianWen Wang, Rui Qi, and Philippe de Reffye. "GreenScilab-Crop, an open source software for plant simulation and parameter estimation." In 2009 IEEE International Workshop on Open-source Software for Scientific Computation (OSSC). IEEE, 2009. http://dx.doi.org/10.1109/ossc.2009.5416863.

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Sagues, Lluis, Xavier Fabregas, and Antoni Broquetas. "Crop height monitoring and surface parameter estimating using polarimetric and interferometric radar techniques." In Europto Remote Sensing, edited by Manfred Owe, Guido D'Urso, and Eugenio Zilioli. SPIE, 2001. http://dx.doi.org/10.1117/12.413938.

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Varella, Hubert, Martine Guerif, and Samuel Buis. "Global Sensitivity Analysis (GSA) Measures the Quality of Parameter Estimation. Case of Soil Parameter Estimation with a Crop Model." In IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2008. http://dx.doi.org/10.1109/igarss.2008.4779531.

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Schmullius, C., and T. Schrage. "Classification, crop parameter estimation and synergy effects using airborne DLR E-SAR and DAEDALUS images." In IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174). IEEE, 1998. http://dx.doi.org/10.1109/igarss.1998.702810.

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Guo, Yiqing, Feng Zhao, Yanbo Huang, Matthew A. Lee, Krishna N. Reddy, Reginald S. Fletcher, Steven J. Thomson, and Jianxi Huang. "Early detection of crop injury from glyphosate by foliar biochemical parameter inversion through leaf reflectance measurement." In 2013 Second International Conference on Agro-Geoinformatics. IEEE, 2013. http://dx.doi.org/10.1109/argo-geoinformatics.2013.6621891.

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Turin, E. N., K. G. Zhenchenko, A. A. Gongalo, V. Yu Ivanov, N. V. Karaeva, and V. V. Reent. "The results of the study of the direct seeding in the Research Institute of Agriculture of Crimea." In CURRENT STATE, PROBLEMS AND PROSPECTS OF THE DEVELOPMENT OF AGRARIAN SCIENCE. Federal State Budget Scientific Institution “Research Institute of Agriculture of Crimea”, 2020. http://dx.doi.org/10.33952/2542-0720-2020-5-9-10-49.

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The research aimed to study the influence of different tillage-and-planting systems on the soil density of chernozem southern in the central steppe of the Crimea. The soil density is a very important parameter both in the direct seeding and conventional tillage since the no-tillage crop production system is that left soil undisturbed. The stationary experimental site is situated in the village of Klepinino Krasnogvardeyskiy district Republic of Crimea (Department of Field Сrops, FSBSI “Research Institute of Agriculture of Crimea”). This report provides data for 2019. Even though the direct see
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