Добірка наукової літератури з теми "Crop Phenotyping"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Crop Phenotyping".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Crop Phenotyping"
Wang, Ya-Hong, and Wen-Hao Su. "Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review." Agronomy 12, no. 11 (October 27, 2022): 2659. http://dx.doi.org/10.3390/agronomy12112659.
Повний текст джерелаNguyen, Giao N., and Sally L. Norton. "Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm." Plants 9, no. 7 (June 29, 2020): 817. http://dx.doi.org/10.3390/plants9070817.
Повний текст джерелаYuan, Huali, Yiming Liu, Minghan Song, Yan Zhu, Weixing Cao, Xiaoping Jiang, and Jun Ni. "Design of the Mechanical Structure of a Field-Based Crop Phenotyping Platform and Tests of the Platform." Agronomy 12, no. 9 (September 11, 2022): 2162. http://dx.doi.org/10.3390/agronomy12092162.
Повний текст джерелаAnchekov, M. I. "High throughput crop phenotyping systems." News of the Kabardin-Balkar Scientific Center of RAS 5, no. 109 (2022): 19–24. http://dx.doi.org/10.35330/1991-6639-2022-5-109-19-24.
Повний текст джерелаJin, Xiuliang, Wanneng Yang, John H. Doonan, and Clement Atzberger. "Crop phenotyping studies with application to crop monitoring." Crop Journal 10, no. 5 (October 2022): 1221–23. http://dx.doi.org/10.1016/j.cj.2022.09.001.
Повний текст джерелаStanschewski, Clara S., Elodie Rey, Gabriele Fiene, Evan B. Craine, Gordon Wellman, Vanessa J. Melino, Dilan S. R. Patiranage, et al. "Quinoa Phenotyping Methodologies: An International Consensus." Plants 10, no. 9 (August 24, 2021): 1759. http://dx.doi.org/10.3390/plants10091759.
Повний текст джерелаNobuhara, Hajime. "Aerial Imaging for Field Crop Phenotyping." Journal of the Robotics Society of Japan 34, no. 2 (2016): 123–26. http://dx.doi.org/10.7210/jrsj.34.123.
Повний текст джерелаXu, Rui, and Changying Li. "A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots." Plant Phenomics 2022 (June 17, 2022): 1–20. http://dx.doi.org/10.34133/2022/9760269.
Повний текст джерелаWatt, Michelle, Fabio Fiorani, Björn Usadel, Uwe Rascher, Onno Muller, and Ulrich Schurr. "Phenotyping: New Windows into the Plant for Breeders." Annual Review of Plant Biology 71, no. 1 (April 29, 2020): 689–712. http://dx.doi.org/10.1146/annurev-arplant-042916-041124.
Повний текст джерелаIlakiya, T., E. Parameswari, V. Davamani, Dumpala Swetha, and E. Prakash. "High-throughput crop phenotyping in vegetable crops." Pharma Innovation 9, no. 8 (August 1, 2020): 184–91. http://dx.doi.org/10.22271/tpi.2020.v9.i8c.5035.
Повний текст джерелаДисертації з теми "Crop Phenotyping"
Wang, Huan. "Crop assessment and monitoring using optical sensors." Diss., Kansas State University, 2017. http://hdl.handle.net/2097/38224.
Повний текст джерелаDepartment of Agronomy
V. P. Vara Prasad
Crop assessment and monitoring is important to crop management both at crop production level and research plot level, such as high-throughput phenotyping in breeding programs. Optical sensors based agricultural applications have been around for decades and have soared over the past ten years because of the potential of some new technologies to be low-cost, accessible, and high resolution for crop remote sensing which can help to improve crop management to maintain producers’ income and diminish environmental degradation. The overall objective of this study was to develop methods and compare the different optical sensors in crop assessment and monitoring at different scales and perspectives. At crop production level, we reviewed the current status of different optical sensors used in precision crop production including satellite-based, manned aerial vehicle (MAV)-based, unmanned aircraft system (UAS)-based, and vehicle-based active or passive optical sensors. These types of sensors were compared thoroughly on their specification, data collection efficiency, data availability, applications and limitation, economics, and adoption. At research plot level, four winter wheat experiments were conducted to compare three optical sensors (a Canon T4i® modified color infrared (CIR) camera, a MicaSense RedEdge® multispectral imager and a Holland Scientific® RapidScan CS-45® hand-held active optical sensor (AOS)) based high-throughput phenotyping for in-season biomass estimation, canopy estimation, and grain yield prediction in winter wheat across eleven Feekes stages from 3 through 11.3. The results showed that the vegetation indices (VIs) derived from the Canon T4i CIR camera and the RedEdge multispectral camera were highly correlated and can equally estimate winter wheat in-season biomass between Feekes 3 and 11.1 with the optimum point at booting stage and can predict grain yield as early as Feekes 7. Compared to passive sensors, the RapidScan AOS was less powerful and less temporally stable for biomass estimation and yield prediction. Precise canopy height maps were generated from a CMOS sensor camera and a multispectral imager although the accuracy could still be improved. Besides, an image processing workflow and a radiometric calibration method were developed for UAS based imagery data as bi-products in this project. At temporal dimension, a wheat phenology model based on weather data and field contextual information was developed to predict the starting date of three key growth stages (Feekes 4, 7, and 9), which are critical for N management. The model could be applied to new data within the state of Kansas to optimize the date for optical sensor (such as UAS) data collection and save random or unnecessary field trips. Sensor data collected at these stages could then be plugged into pre-built biomass estimation models (mentioned in the last paragraph) to estimate the productivity variability within 20% relative error.
Crain, Jared Levi. "Leveraging the genomics revolution with high-throughput phenotyping for crop improvement of abiotic stresses." Diss., Kansas State University, 2016. http://hdl.handle.net/2097/32566.
Повний текст джерелаGenetics Interdepartmental Program - Plant Pathology
Jesse A. Poland
A major challenge for 21st century plant geneticists is to predict plant performance based on genetic information. This is a daunting challenge, especially when there are thousands of genes that control complex traits as well as the extreme variation that results from the environment where plants are grown. Rapid advances in technology are assisting in overcoming the obstacle of connecting the genotype to phenotype. Next generation sequencing has provided a wealth of genomic information resulting in numerous completely sequenced genomes and the ability to quickly genotype thousands of individuals. The ability to pair the dense genotypic data with phenotypic data, the observed plant performance, will culminate in successfully predicting cultivar performance. While genomics has advanced rapidly, phenomics, the science and ability to measure plant phenotypes, has slowly progressed, resulting in an imbalance of genotypic to phenotypic data. The disproportion of high-throughput phenotyping (HTP) data is a bottleneck to many genetic and association mapping studies as well as genomic selection (GS). To alleviate the phenomics bottleneck, an affordable and portable phenotyping platform, Phenocart, was developed and evaluated. The Phenocart was capable of taking multiple types of georeferenced measurements including normalized difference vegetation index and canopy temperature, throughout the growing season. The Phenocart performed as well as existing manual measurements while increasing the amount of data exponentially. The deluge of phenotypic data offered opportunities to evaluate lines at specific time points, as well as combining data throughout the season to assess for genotypic differences. Finally in an effort to predict crop performance, the phenotypic data was used in GS models. The models combined molecular marker data from genotyping-by-sequencing with high-throughput phenotyping for plant phenotypic characterization. Utilizing HTP data, rather than just the often measured yield, increased the accuracy of GS models. Achieving the goal of connecting genotype to phenotype has direct impact on plant breeding by allowing selection of higher yielding crops as well as selecting crops that are adapted to local environments. This will allow for a faster rate of improvement in crops, which is imperative to meet the growing global population demand for plant products.
Thomas, C. L. "High throughput phenotyping of root and shoot traits in Brassica to identify novel genetic loci for improved crop nutrition." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/43440/.
Повний текст джерелаMcNulty, Sarah Kristine. "Accelerated Crop Domestication through Identification of Phenotypic Characteristics of Taraxacum kok-saghyz Relevant to Rubber Yield." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574766469110561.
Повний текст джерелаCampbell, Lesley G. "Rapid evolution in a crop-weed complex (Raphanus spp.)." The Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=osu1166549627.
Повний текст джерелаAgostinelli, Andres Mateo. "PHENOTYPIC AND GENOTYPIC SELECTION FOR HEAD SCAB RESISTANCE IN WHEAT." UKnowledge, 2009. http://uknowledge.uky.edu/gradschool_theses/582.
Повний текст джерелаVergara, Díaz Omar. "High-throughput field phenotyping in cereals and implications in plant ecophysiology." Doctoral thesis, Universitat de Barcelona, 2019. http://hdl.handle.net/10803/668314.
Повний текст джерелаEls efectes del canvi climàtic sobre els agro-ecosistemes i l’increment de la població mundial posa en risc la seguretat alimentària i l’estabilitat dels ecosistemes. Actualment, satisfer les demandes de producció d’aliments sota l’escenari del canvi climàtic és el repte central a la Biologia Vegetal. Per això, és indispensable entendre els mecanismes subjacents de l’aclimatació a l’estrès que permeten obtenir cultius resilients. També és precís desenvolupar nou mètodes de recerca que permetin caracteritzar de manera no destructiva els trets d’interès. L’avenç del fenotipat vegetal amb sistemes d’alt rendiment és clau per abordar aquests reptes. La present tesi s’enfoca en el blat i secundàriament en el panís com a espècies d’estudi ja que constitueixen els cultius bàsics arreu del món. Un ampli ventall de mètodes de fenotipat s’han utilitzat, des sensors RGB a híper-espectrals fins a la caracterització metabolòmica. La recerca s’ha dut a terme en assajos de camp i s’han avaluat diversos tipus d’estrès representatius de les majors limitacions pel creixement i producció vegetal: estrès hídric i biòtic i deficiència de nitrogen. Els resultats demostraren el gran potencial dels trets del color RGB (des de la planta a la capçada) pel fenotipat de camp, ja que foren indicadors precisos del rendiment a blat i panís sota condicions de malaltia i deficiència de nitrogen i de la concentració de nitrogen foliar a panís. La caracterització metabolòmica de teixits de blat contribuí a esbrinar els processos metabòlics endegats per l’estrès hídric i la seva relació amb comportament genotípic, proporcionant bio-marcadors potencials per rendiments més alts i l’adaptació a l’estrès. Estudis espectroscòpics en blat van demostrar que la dorsoventralitat pot afectar més que l’estrès hídric sobre l’espectre de reflectància i consegüentment sobre el comportament de les aproximacions multi/híper-espectrals per avaluar el rendiment i d’altres trets fenotípics com anatòmics i contingut de pigments. Finalment, l’ús de l’espectroscòpia per l’estimació del contingut metabòlic als teixits de blat resulta prometedor per molts metabòlits, la qual cosa obre les portes per a un fenotipat de nova generació. El fenotipat pot beneficiar-se d’aquestes troballes, tant en els mètodes de baix cost com de les tecnologies més sofisticades i d’avantguarda.
Hasing, Rodriguez Tomas Nestor. "Genomic Reconstruction of the Domestication History of Sinningia speciosa (Lodd.) Hiern, and the Development of a Novel Genotyping Approach." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/95510.
Повний текст джерелаDoctor of Philosophy
Most staple food crops were domesticated thousands of years ago through unrelated processes that were initiated across different regions of the world. Studies of the history of such crops have been essential to our understanding of plant domestication, a process that started with the collection of wild material and continued with subsequent propagation and cultivation under human care. Plant domestication has often involved a complex combination of ancestral lineages that encompass multiple populations, crosses with other species, and large DNA reorganizations that occurred hundreds to thousands of years earlier. Such intricate origins make the systematic study of plant domestication very challenging. The analysis of recently domesticated plants such as the 'florist's gloxinia' (Sinningia speciosa), can help us to better understand some of the changes that have occurred during domestication, as well as to comprehend modern patterns of plant domestication and to broaden our understanding of general trends. Florist's gloxinias are ornamental plants that have been cultivated during the last 200 years. In this study we examined 115 specimens, including wild and cultivated types of florist's gloxinias, as well as closely related species in Sinningia. We also constructed and evaluated an artificial population of 150 individuals from the cross of a wild and a cultivated form. Our analyses revealed that all of the domesticated varieties are descendants from a single wild population that originated in or near the city of Rio de Janeiro in Brazil. We also identified two regions of DNA that are responsible for the changes in flower shape and color, and crosses with other species did not introduce such alterations. Our findings, in conjunction with other features such as its small nuclear DNA content, the ease of cultivation indoors, and a rapid generation time, makes the florists' gloxinia an attractive crop to the study the effects of plant domestication. Research on organisms with low economic importance is uncommon but necessary to understand the world from a broader perspective. In such cases, analyzing the entire genetic information that is stored as DNA may be cost-prohibitive. Instead, approaches that sample small portions of DNA from each individual can be utilized. Most of these technologies are currently patented and subject to licensing processes and fees that limit their implementation by small non-profit research organizations. In this study we designed a protocol to sample small portions of DNA, similarly to existing techniques. However, our approach, called Targeted Amplification of Scattered Sites (TASS), employs a sampling process that deviates from the traditional patented procedure that is used in most current methods. At present, TASS is not as consistent and delivers less information than traditional approaches. However, we have established a foundation on which further optimization can produce an accessible and easy to implement technique.
Damesa, Tigist Mideksa [Verfasser], and Hans-Peter [Akademischer Betreuer] Piepho. "Weighting methods for variance heterogeneity in phenotypic and genomic data analysis for crop breeding / Tigist Mideksa Damesa ; Betreuer: Hans-Peter Piepho." Hohenheim : Kommunikations-, Informations- und Medienzentrum der Universität Hohenheim, 2019. http://d-nb.info/1199440035/34.
Повний текст джерелаFernández, Gallego José Armando. "Image processing techniques for plant phenotyping using RGB and thermal imagery = Técnicas de procesamiento de imágenes RGB y térmicas como herramienta para fenotipado de cultivos." Doctoral thesis, Universitat de Barcelona, 2019. http://hdl.handle.net/10803/669111.
Повний текст джерелаLas existencias mundiales de cereales deben aumentar para satisfacer la creciente demanda. Actualmente, el maíz, el arroz y el trigo son los principales cultivos a nivel mundial, otros cereales como la cebada, el sorgo y la avena están también bien ubicados en la lista. La productividad de los cultivos se ve afectada directamente por factores del cambio climático como el calor, la sequía, las inundaciones o las tormentas. Los investigadores coinciden en que el cambio climático global está teniendo un gran impacto en la productividad de los cultivos. Es por esto que muchos estudios se han centrado en escenarios de cambio climático y más específicamente en estrés abiótico. Por ejemplo, en el caso de estrés por calor, las altas temperaturas entre antesis y llenado de grano pueden disminuir el rendimiento del grano. Para hacer frente al cambio climático y escenarios ambientales futuros, el mejoramiento de plantas es una de las principales alternativas; incluso se considera que las técnicas de mejoramiento contribuyen en mayor medida al aumento del rendimiento que el manejo del cultivo. Los programas de mejora se centran en identificar genotipos con altos rendimientos y calidad para actuar como progenitores y promover los mejores individuos para desarrollar nuevas variedades de plantas. Los mejoradores utilizan los datos fenotípicos, el desempeño de las plantas y los cultivos, y la información genética para mejorar el rendimiento mediante selección (GxE, donde G y E indican factores genéticos y ambientales). El fenotipado plantas está relacionado con las características observables (o medibles) de la planta mientras crece el cultivo, así como con la asociación entre el fondo genético de la planta y su respuesta al medio ambiente (GxE). En el fenotipado tradicional, las mediciones se clasifican manualmente, lo cual es tedioso, consume mucho tiempo y es propenso a errores subjetivos. Sin embargo, hoy en día la tecnología está involucrada en muchas aplicaciones. Desde el punto de vista del fenotipado de plantas, la tecnología se ha incorporado como una herramienta. El uso de técnicas de procesamiento de imágenes que integran sensores y algoritmos son por lo tanto una alternativa para evaluar automáticamente (o semiautomáticamente) estas características.
Книги з теми "Crop Phenotyping"
Zhou, Jianfeng, and Henry T. Nguyen, eds. High-Throughput Crop Phenotyping. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73734-4.
Повний текст джерелаZhou, Jianfeng, and Henry T. Nguyen. High-Throughput Crop Phenotyping. Springer International Publishing AG, 2022.
Знайти повний текст джерелаZhou, Jianfeng, and Henry T. Nguyen. High-Throughput Crop Phenotyping. Springer International Publishing AG, 2021.
Знайти повний текст джерелаPanguluri, Siva Kumar, and Are Ashok Kumar. Phenotyping for Plant Breeding: Applications of Phenotyping Methods for Crop Improvement. Springer, 2013.
Знайти повний текст джерелаPanguluri, Siva Kumar, and Are Ashok Kumar. Phenotyping for Plant Breeding: Applications of Phenotyping Methods for Crop Improvement. Springer, 2013.
Знайти повний текст джерелаPanguluri, Siva Kumar, and Are Ashok Kumar. Phenotyping for Plant Breeding: Applications of Phenotyping Methods for Crop Improvement. Springer, 2016.
Знайти повний текст джерелаPanguluri, Siva Kumar, and Are Ashok Kumar. Phenotyping for Plant Breeding: Applications of Phenotyping Methods for Crop Improvement. Springer London, Limited, 2013.
Знайти повний текст джерелаSudhakar, P., P. Latha, and P. V. Reddy. Phenotyping Crop Plants for Physiological and Biochemical Traits. Elsevier Science & Technology Books, 2016.
Знайти повний текст джерелаPhenotyping Crop Plants for Physiological and Biochemical Traits. Elsevier Science & Technology Books, 2016.
Знайти повний текст джерелаPrashar, Ankush, Lindsey Compton, Martina Stromvik, and Helen H. Tai, eds. High-Throughput Phenotyping for Crop Improvement and Breeding. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-88974-283-7.
Повний текст джерелаЧастини книг з теми "Crop Phenotyping"
Tariq, Muhammad, Mukhtar Ahmed, Pakeeza Iqbal, Zartash Fatima, and Shakeel Ahmad. "Crop Phenotyping." In Systems Modeling, 45–60. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4728-7_2.
Повний текст джерелаAraus, José Luis, Shawn Carlisle Kefauver, Mainassara Zaman-Allah, Mike S. Olsen, and Jill E. Cairns. "Phenotyping: New Crop Breeding Frontier." In Crop Science, 493–503. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-8621-7_1036.
Повний текст джерелаTariq, Muhammad, Muhammad Habib Ur Rehman, Feng Ling Yang, Muhammad Hayder Bin Khalid, Muhammad Ali Raza, Muhammad Jawad Hassan, Tehseen Ahmad Meraj, et al. "Rice Phenotyping." In Modern Techniques of Rice Crop Production, 151–64. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-4955-4_11.
Повний текст джерелаPaulus, Stefan, Gustavo Bonaventure, and Marcus Jansen. "Multisensor Phenotyping for Crop Physiology." In Intelligent Image Analysis for Plant Phenotyping, 25–42. First edition. | Boca Raton, FL : CRC Press, 2021.: CRC Press, 2020. http://dx.doi.org/10.1201/9781315177304-3.
Повний текст джерелаAraus, José Luis, Shawn Carlisle Kefauver, Mainassara Zaman-Allah, Mike S. Olsen, and Jill E. Cairns. "Phenotyping: New Crop Breeding Frontier." In Encyclopedia of Sustainability Science and Technology, 1–11. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-2493-6_1036-1.
Повний текст джерелаYol, Engin, Cengiz Toker, and Bulent Uzun. "Traits for Phenotyping." In Phenomics in Crop Plants: Trends, Options and Limitations, 11–26. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2226-2_2.
Повний текст джерелаChen, Ying Long, Ivica Djalovic, and Zed Rengel. "Phenotyping for Root Traits." In Phenomics in Crop Plants: Trends, Options and Limitations, 101–28. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2226-2_8.
Повний текст джерелаRajendran, Karthika, Somanagouda Patil, and Shiv Kumar. "Phenotyping for Problem Soils." In Phenomics in Crop Plants: Trends, Options and Limitations, 129–46. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2226-2_9.
Повний текст джерелаThavarajah, Dil, Casey R. Johnson, Rebecca McGee, and Pushparajah Thavarajah. "Phenotyping Nutritional and Antinutritional Traits." In Phenomics in Crop Plants: Trends, Options and Limitations, 223–33. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2226-2_15.
Повний текст джерелаPratap, Aditya, Rakhi Tomar, Jitendra Kumar, Vankat Raman Pandey, Suhel Mehandi, and Pradeep Kumar Katiyar. "High-Throughput Plant Phenotyping Platforms." In Phenomics in Crop Plants: Trends, Options and Limitations, 285–96. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2226-2_19.
Повний текст джерелаТези доповідей конференцій з теми "Crop Phenotyping"
Pour, Majid Khak, Reza Fotouhi, and Pierre Hucl. "Development of a Mobile Platform for Wheat Phenotyping." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-24329.
Повний текст джерелаZhang, QianWei, Reza Fotouhi, Joshua Cote, and Majid Khak Pour. "Lightweight Long-Reach 5-DOF Robot Arm for Farm Application." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98366.
Повний текст джерелаZhang, Qianwei, and Reza Fotouhi. "Vibration Analysis of a Long Boom for a Farm Machine." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-86188.
Повний текст джерелаSmitt, Claus, Michael Halstead, Tobias Zaenker, Maren Bennewitz, and Chris McCool. "PATHoBot: A Robot for Glasshouse Crop Phenotyping and Intervention." In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. http://dx.doi.org/10.1109/icra48506.2021.9562047.
Повний текст джерела"Global Patent Analysis on Computer Vision Powered Crop Phenotyping." In 2022 the 12th International Workshop on Computer Science and Engineering. WCSE, 2022. http://dx.doi.org/10.18178/wcse.2022.06.006.
Повний текст джерелаManish, Raja, Ze An, Ayman Habib, Mitchell R. Tuinstra, and David J. Cappelleri. "AgBug: Agricultural Robotic Platform for In-Row and Under Canopy Crop Monitoring and Assessment." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-68143.
Повний текст джерелаNiu, Haoyu, Dong Wang, and YangQuan Chen. "Estimating actual crop evapotranspiration using deep stochastic configuration networks model and UAV-based crop coefficients in a pomegranate orchard." 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/10.1117/12.2558221.
Повний текст джерелаGao, Tianshuang, Hamid Emadi, Homagni Saha, Jiaoping Zhang, Alec Lofquist, Arti Singh, Baskar Ganapathysubramanian, Soumik Sarkar, Asheesh Singh, and Sourabh Bhattacharya. "Navigation Strategies for a Multi-Robot Ground-Based Row Crop Phenotyping Platform." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9096.
Повний текст джерелаPhillips, Ryan, and Jason Ward. "Assessing crop response to simulated damage utilizing UAS imagery." 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/10.1117/12.2560702.
Повний текст джерелаWeyler, Jan, Federico Magistri, Peter Seitz, Jens Behley, and Cyrill Stachniss. "In-Field Phenotyping Based on Crop Leaf and Plant Instance Segmentation." In 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2022. http://dx.doi.org/10.1109/wacv51458.2022.00302.
Повний текст джерелаЗвіти організацій з теми "Crop Phenotyping"
Gur, Amit, Edward Buckler, Joseph Burger, Yaakov Tadmor, and Iftach Klapp. Characterization of genetic variation and yield heterosis in Cucumis melo. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7600047.bard.
Повний текст джерелаHunter, Martha S., and Einat Zchori-Fein. Rickettsia in the whitefly Bemisia tabaci: Phenotypic variants and fitness effects. United States Department of Agriculture, September 2014. http://dx.doi.org/10.32747/2014.7594394.bard.
Повний текст джерелаPaterson, Andrew H., Yehoshua Saranga, and Dan Yakir. Improving Productivity of Cotton (Gossypsum spp.) in Arid Region Agriculture: An Integrated Physiological/Genetic Approach. United States Department of Agriculture, December 1999. http://dx.doi.org/10.32747/1999.7573066.bard.
Повний текст джерелаBlum, Abraham, and Henry T. Nguyen. Molecular Tagging of Drought Resistance in Wheat: Osmotic Adjustment and Plant Productivity. United States Department of Agriculture, November 2002. http://dx.doi.org/10.32747/2002.7580672.bard.
Повний текст джерелаWeil, Clifford F., Anne B. Britt, and Avraham Levy. Nonhomologous DNA End-Joining in Plants: Genes and Mechanisms. United States Department of Agriculture, July 2001. http://dx.doi.org/10.32747/2001.7585194.bard.
Повний текст джерелаSherman, Amir, Rebecca Grumet, Ron Ophir, Nurit Katzir, and Yiqun Weng. Whole genome approach for genetic analysis in cucumber: Fruit size as a test case. United States Department of Agriculture, December 2013. http://dx.doi.org/10.32747/2013.7594399.bard.
Повний текст джерелаRowe, Randall C., Jaacov Katan, Talma Katan, and Leah Tsror. Sub-Specific Populations of Verticillium dahliae and their Roles in Vascular Wilt Pathogsystems. United States Department of Agriculture, October 1996. http://dx.doi.org/10.32747/1996.7574343.bard.
Повний текст джерелаAbbo, Shahal, Hongbin Zhang, Clarice Coyne, Amir Sherman, Dan Shtienberg, and George J. Vandemark. Winter chickpea; towards a new winter pulse for the semiarid Pacific Northwest and wider adaptation in the Mediterranean basin. United States Department of Agriculture, January 2011. http://dx.doi.org/10.32747/2011.7597909.bard.
Повний текст джерелаLers, Amnon, and Gan Susheng. Study of the regulatory mechanism involved in dark-induced Postharvest leaf senescence. United States Department of Agriculture, January 2009. http://dx.doi.org/10.32747/2009.7591734.bard.
Повний текст джерелаAharoni, Asaph, Zhangjun Fei, Efraim Lewinsohn, Arthur Schaffer, and Yaakov Tadmor. System Approach to Understanding the Metabolic Diversity in Melon. United States Department of Agriculture, July 2013. http://dx.doi.org/10.32747/2013.7593400.bard.
Повний текст джерела