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26. Orchestrating seed storage protein and starch accumulation toward overcoming yield–quality trade‐off in cereal crops.

Author

Cao, Shuanghe, Liu, Bingyan, Wang, Daowen, Rasheed, Awais, Xie, Lina, Xia, Xianchun, and He, Zhonghu

Subjects
SEED storage, SEED proteins, STARCH, CROPS, CROP quality, GRAIN
Abstract

Achieving high yield and good quality in crops is essential for human food security and health. However, there is usually disharmony between yield and quality. Seed storage protein (SSP) and starch, the predominant components in cereal grains, determine yield and quality, and their coupled synthesis causes a yield–quality trade‐off. Therefore, dissection of the underlying regulatory mechanism facilitates simultaneous improvement of yield and quality. Here, we summarize current findings about the synergistic molecular machinery underpinning SSP and starch synthesis in the leading staple cereal crops, including maize, rice and wheat. We further evaluate the functional conservation and differentiation of key regulators and specify feasible research approaches to identify additional regulators and expand insights. We also present major strategies to leverage resultant information for simultaneous improvement of yield and quality by molecular breeding. Finally, future perspectives on major challenges are proposed. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Molecular Markers Help with Breeding for Agronomic Traits of Spring Wheat in Kazakhstan and Siberia.

Author

Morgounov, Alexey, Babkenov, Adylkhan, Ben, Cécile, Chudinov, Vladimir, Dolinny, Yuriy, Dreisigacker, Susanne, Fedorenko, Elena, Gentzbittel, Laurent, Rasheed, Awais, Savin, Timur, Shepelev, Sergey, Zhapayev, Rauan, and Shamanin, Vladimir

Subjects
GENETIC markers, GRAIN yields, WHEAT, GERMPLASM, ALLELES, GENOTYPES
Abstract

The Kazakhstan-Siberia Network for Spring Wheat Improvement (KASIB) was established in 2000, forming a collaboration between breeding and research programs through biannual yield trials. A core set of 142 genotypes from 15 breeding programs was selected, genotyped for 81 DNA functional markers and phenotyped for 10 agronomic traits at three sites in Kazakhstan (Karabalyk, Shortandy and Shagalaly) and one site in Russia (Omsk) in 2020–2022. The study aim was to identify markers demonstrating significant effects on agronomic traits. The average grain yield of individual trials varied from 118 to 569 g/m2. Grain yield was positively associated with the number of days to heading, plant height, number of grains per spike and 1000-kernel weight. Eight DNA markers demonstrated significant effects. The spring-type allele of the Vrn-A1 gene accelerated heading by two days (5.6%) and was present in 80% of the germplasm. The winter allele of the Vrn-A1 gene significantly increased grain yield by 2.7%. The late allele of the earliness marker per se, TaMOT1-D1, delayed development by 1.9% and increased yield by 4.5%. Translocation of 1B.1R was present in 21.8% of the material, which resulted in a 6.2% yield advantage compared to 1B.1B germplasm and a reduction in stem rust severity from 27.6 to 6.6%. The favorable allele of TaGS-D1 increased both kernel weight and yield by 2–3%. Four markers identified in ICARDA germplasm, ISBW2-GY (Kukri_c3243_1065, 3B), ISBW3-BM (TA004946-0577, 1B), ISBW10-SM2 (BS00076246_51, 5A), ISBW11-GY (wsnp_Ex_c12812_20324622, 4A), showed an improved yield in this study of 3–4%. The study recommends simultaneous validation and use of selected markers in KASIB's network. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Genetic Signature Controlling Root System Architecture in Diverse Spring Wheat Germplasm.

Author

Zaman, Zahra, Iqbal, Rubab, Jabbar, Abdul, Zahra, Nageen, Saleem, Bilal, Kiran, Aysha, Maqbool, Saman, Rasheed, Awais, Naeem, Muhammad Kashif, and Khan, Muhammad Ramzan

Subjects
GENOME-wide association studies, CHROMOSOMES, BIOMASS, PLANT biomass, GERMPLASM, WHEAT
Abstract

Roots are the main sensing organ, initiating multiple signaling pathways in response to abiotic factors, including nutrients, drought, and salt stress. A focus on improving the root system architecture is a key strategy to mitigate these stresses in wheat crop. In the present study, a diversity panel comprising indigenous landraces and historical cultivars from Pakistan was characterized for the root system architecture (RSA) and important loci were identified using a genome‐wide association study (GWAS). RSA of the diversity panel was characterized 30 days after sowing in brunch tubes, and root images were taken. A high‐throughput root imaging analysis using Rhizovision software was performed by setting the scale to extract the eight RSA traits and four plant biomass‐related traits. GWAS identified 323 association signals for 12 root and biomass traits present on all wheat chromosomes, while the most important and reliable genetic loci (based on pleotropic loci and candidate genes) were identified on chromosomes 2A, 2B, 5A, 5D, 6A, 7B, and 7D for RSA. SNP annotation and transcriptome profiling identified nine candidate genes regulating the RSA and plant biomass traits, including ROOTLESS WITH UNDETECTABLE MERISTEM1, MYB TRANSCRIPTION FACTOR4, BRASSINOSTEROID INSENSITIVE1, SLENDER RICE1, AUXIN‐RESPONSIVE FACTOR25, SCARECROW, NARROW LEAF2, PIN‐FORMED1 AND PHOSPHATE TRANSCRIPTION FACTOR1. This study provided pre‐breeding information for deep‐rooting genotypes and associated markers that will accelerate the incorporation of such traits in breeding. [ABSTRACT FROM AUTHOR]

Published
2024
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34. 4th Conference of Cereal Biotechnology and Breeding jointly organized by EUCARPIA Cereal Section November 6–9, 2017 • Budapest, Hungary : BOOK OF ABSTRACTS

Author

Consortium INEW, PPP Barley Consortium, Börner, Andreas, Nagel, Manuela, Tarawneh, Rasha, Arif, Mian Abdur Rehman, Agacka-Mołdoch, Monika, Börner, Mariann, Lohwasser, Ulrike, Rieweach, David, Wieb, Janine, Altmann, Thomas, Góralska, Magdalena, Lenarczyk-Puri, Natalia, Podgórska, Katarzyna, Berdzik, Marcin, Stojałowski, Stefan, Myśków, Beata, Kiss, Tibor, Veisz, Ottó, Karsai, Ildikó, Martín, Antonio, Rasmussen, Søren K., Legzdina, Linda, Fan, Weiyao, Tikhenko, Natalia, Rutten, Twan, Senula, Angelika, Rubtsova, Myroslava, Keller, E. R. Joachim, Vitányi, Beáta, Nagy, Katalin, Fekete, Anna Katalin, Dudás, Brigitta, Lantos, Csaba, Pauk, János, Jenes, Barnabás, Yu, Bianyun, Zhang, Wentao, Gao, Peng, Sidebottom, Christine, Lokuruge, Prabhath, Polley, Brittany, Clarke, Carling, Sharpe, Andrew, Knox, Ron, Cuthbert, Richard, Ruan, Yuefeng, Pozniak, Curtis J., Fobert, Pierre, Dziurka, Kinga, Noga, Angelika, Marcińska, Izabela, Czyczyło-Mysza, Ilona, Warchoł, Marzena, Kapłoniak, Kamila, Warzecha, Tomasz, Sutkowska, Agnieszka, Werwińska, Krystyna, Nita, Zygmunt, Skrzypek, Edyta, Fradgley, Nick, Bentley, Alison, Gardner, Keith, Howell, Phil, Mackay, Ian, Scott, Mike, Mott, Richard, Cockram, James, Galani, Saddia, Sahar, Shagufta, Musharraf, Syed Ghulam, Azhar, Abid, Ferjaoui, Sahbi, Bouslimi, Chadha, Udupa, Sripada M., Hamza, Sonia, Mariana, Ittu, Ciuca, Matilda, Marinciu, Cristina, Cristina, Daniel, Turcu, Alina, Mandea, Vasile, Ittu, Gheorghe, Jabłoński, Bartosz, Onyśk, Agnieszka, Wyszyńska, Renata, Boczkowska, Maja, Przyborowski, Mateusz, Gasparis, Sebastian, Orczyk, Wacław, Nadolska-Orczyk, Anna, Bińkowski, Jan, Kubiś, Jakub, Pogorzelska, Katarzyna, Pellny, Till K, Bolibok-Brągoszewska, Hanna, Idziak-Helmcke, Dominika, Rojek, Magdalena, Hosiawa-Barańska, Marta, Tsai, Hsin-Yuan, Cericola, Fabio, Edriss, Vahid, Andersen, Jeppe, Orabi, Jihad, Jensen, Jens, Jahoor, Ahmed, Janss, Luc, Jensen, Just, Lawrenson, Tom, Hayta, Sadiye, Smedley, Mark, Hinchliffe, Alison, Harwood, Wendy, Gimenez, Guillermo G., Barakate, Abdellah, Russell, Joanne, Stephens, Jennifer, Lampugnani, Edwin R., Doblin, Monika S., Fincher, Geoffrey B., Burton, Rachel A., Waugh, Robbie, Tucker, Matthew, Houston, Kelly, Jouanin, Aurelie, Leigh, Fiona, Wallington, Emma, Schaart, Jan, Smulders, Marinus J.M., Kała, Maciej, Kim, Sung-Yong, Brueggeman, Robert, Åhman, Inger, Zhu, Li-Hua, Bouchet, Sophie, Lemarié, Stéphane, Fugeray-Scarbel, Aline, Auzanneau, Jérôme, Charmet, Gilles, Ali, Ahmad, Mastrangelo, Anna Maria, De Vita, Pasquale, Laido, Giovanni, Marone, Daniale, Arshad, Muhammad, Naqvi, S.M. Sawlan, Sher, Hassan, Rasheed, Awais, Kazi, Abdul Mujeeb, Oakey, Helena, Bayer, Micha, Macaulay, Malcolm, Byrt, Caitlin, Gilliham, Matthew, Salt, David E, István, Monostori, Fruzsina, Szira, Alessandro, Tondelli, Tamás, Árendás, Krisztián, Gierczik, Luigi, Cattivelli, Gábor, Galiba, Attila, Vágújfalvi, Bakuma, Alla, Chebotar, Galyna, Filimonov, Vadim, Kyrylyuk, Tetyana, Chebotar, Sabina, Kristensen, Peter Skov, Andersen, Jeppe Reitan, Guzmán, Carlos, Schurr, Ulrich, Pieruschka, Roland, Neumann, Kerstin, Zhao, Yusheng, Dhanagond, Sidram, Reif, Jochen C., Graner, Andreas, Raymaekers, Dries, Taylor, Mike, Åhman, Inger M., Crespo-Herrera, Leonardo A., Kobiljski, Borislav, Mladenović, Gojko, Jestrović, Zorica, Jancsó, Mihály, Ács, Katalin, Bóna, Lajos, Purgel, Szandra, Békés, Ferenc, Schwember, A.R., Matus, I., Sharma, Dew Kumari, Rosenqvist, Eva, Carl-Otto-Ottosen, Torp, Anna Maria, Andersen, Sven Bode, El-Haddoury, Jamal, Henkrar, Fatima, Grana, Zahra, Nyiraguhirwa, Solange, Gyawali, Sanjaya, Verma, Ramesh, Ouabbou, Hassan, Ibriz, Mohammed, Iraqi, Driss, Forgeois, Paul, Grausgruber, Heinrich, Cuendet, Catherine, Dell’Avo, Franca, Koppel, Reine, Weyermann, Verena, Mikó, Péter, Vida, Gyula, Rakszegi, Marianna, Lafferty, Julia, Lorentz, Bruno, Longin, Carl Friedrich Horst, Megyeri, Mária, Kaskarbayev, Zheksenbay, Zhloba, Ludmila, Kiyas, Aldabergen, Kunanbayev, Kairat, Churkina, Galina, Zueva, Nataliya, Cattivelli, Luigi, Guerra, Davide, Tondelli, Alessandro, Bustos-Korts, D., Rizza, Fulvia, Bretani, Gianluca, Ferrandi, Chiara, Lazzari, Barbara, Rossini, Laura, Ozkan, Hakan, Molnar-Lang, Marta, van Eeuwijk, Fred, Dawson, Ian K, Fricano, Agostino, Azad, Md. Abul Kalam, Kamruzzaman, Md., Yasmine, Fahmina, Islam, Mirza Mofazzal, Jankuloski, Ljupcho, Christopher, John (Jack), Anzooman, Monia, Menzies, Neal, Dalal, Ram, Dang, Yash, Novák, Aliz, Boldizsár, Ákos, Gierczik, Krisztián, Mohamed, Ahres, Ádám, Éva, Bognár, László Kozma, Vágújfalvi, Attila, Båga, Monica, Chibbar, Ravindra, Galiba, Gábor, Gottwald, Sven, Bhandari, Dhaka Ram, Wang, Qing, Li, Bin, Römpp, Andreas, Friedt, Wolfgang, Spengler, Bernhard, Jäger, Katalin, Sáfrán, Eszter, Fábián, Attila, Deák, Csilla, Papp, István, Barnabás, Beáta, Mesterhazy, Akos, Varga, Mónika, Bartók, Tibor, Lehoczki-Krjak, Szabolcs, Toth, Beata, Miloud, Hafsi, Pál, Magda, Szalai, Gabriella, Janda, Tibor, Molnár, István, Lovegrove, Alison, Darkó, Éva, Farkas, András, Láng, László, Bedő, Zoltán, Doležel, Jaroslav, Molnár-Láng, Márta, Shewry, Peter, Tiryakioğlu, Murat, Sakallı, Abdulla, Alloudane, Rajae, El Mourabit, Nourdin, Barrijal, Said, Bengtsson, Therése, Manninen, Outi, Bulatova, Kulpash, Mazkirat, Shynar, Babissekova, Dilyara, Aytimbetova, Klara, Goncharova, Anastasiya, Lavrinenko, Yuriy, Bazaliy, Galyna, Cope, Jonathan, Norton, Gareth, George, Timothy, Newton, Adrian, Császár, Orsolya, Lajos, Károly, Szalai, Márk, Dziurka, Michał, Dashkevich, Svetlana, Utebayev, Maral, Chilimova, Irina, Babkenov, Adilchan, Akhmetova, Galiya, Chaimae, Senhaji, Khadija, Ahansal, Rabha, Abdelwahd, Allal, Douira, Driss, Iraqi, Gracz, Joanna, Karłowski, Wojciech, Kuczyński, Jakub, Tyczewska, Agata, Haberle, Jan, Svoboda, Pavel, Neumannová, Adéla, Kurešová, Gabriela, Raimanová, Ivana, Holková, Ludmila, Solařová, Eva, Bradáčová, Marta, Smutná, Pavlína, Horčička, P., Pospíšilová, V., Veškrna, O., Bížova, I., Chrpova, J., Sumíková, T., Ittu, Mariana, Marinciu, Cristina Mihaela, Voica, Maria, Banateanu, Cecilia, Mayer, Marianna, Karsai, Idikó, Mirosavljević, Milan, Momčilović, Vojislava, Mikić, Sanja, Trkulja, Dragana, Denčić, Srbislav, Jocković, Bojan, Aćin, Vladimir, Pržulj, Novo, Nakhoda, Babak, Mohammadi-Nejad, Ghasem, Mardi, Mohsen, Amini, Ashkboos, Tabatabaei, Mohammad-Taghi, Parsayev, Evgeniy, Filippova, Nadezhda, Permana, Hadian, Ishii, Kazuo, Murata, Kazumasa, Yamada, Tetsuya, Kanekatsu, Motoki, Prokesova, Lenka, Smutna, Pavlina, Holkova, Ludmila, Rukavitsina, Irina, Suleimenov, Mechlis, Yerpasheva, Dana, Shtefan, Galina, Ivanova, Galina, Ferderer, Ella, Kalapos, Balázs, Ahres, Mohamed, Székely, András, Gulyás, Zsolt, Kovács, Kristóf, Jones, Huw D, Dalmay, Tamás, Soltész, Alexandra, Trávníčková, Martina, Chrpová, Jana, Martinek, Petr, Hnilička, František, Żywicki, Marek, Twardowski, Tomasz, Végh, Balázs, Marček, Tihana, Wiśniewska, Halina, Kwiatek, Michał, Majka, Maciej, Gawłowska, Magdalena, Korbas, Marek, Danielewicz, Jakub, Belter, Jolanta, Bilgrami, Sayedeh Saba, Sichani, Jalal Sabouri, Zandi, Peiman, Zhunusbayeva, Zhazira, Omirbekova, Nargul, Zhussupova, Aizhan, Mursalieva, Valentina, Nam, Svetlana, Żur, Iwona, Surówka, Ewa, Dubas, Ewa, Nowicka, Anna, Krzewska, Monika, Hura, Tomasz, Rapacz, Marcin, Wójcik-Jagła, Magdalena, Ostrowska, Agnieszka, Malaga, Sabina, Janowiak, Franciszek, Pellny, Till, Patil, Archana, Freeman, Jackie, Plummer, Amy, Hess, Linde, Phillips, Andy, Berry, Simon, Mitchell, Rowan, Larkin, Philip, Tanner, Gregory, Colgrave, Michelle, Blundell, Malcolm, Howitt, Crispin, Morita, Naofumi, Van Hung, Pham, Cegiełko, Małgorzata, Kiecana, Irena, Mielniczuk, Elżbieta, Pastucha, Alina, Perkowski, Juliusz, Áy, Zoltán, Czirle, Csaba, Palágyi, András, Mihály, Róbert, Vermeulen, Philippe, Suman, Michele, Pierna, Juan Antonio Fernández, and Baeten, Vincent

Published
2017

35. UAV‐based RGB imagery and ground measurements for high‐throughput phenotyping of senescence and QTL mapping in bread wheat.

Author

Li, Lei, Hassan, Muhammad Adeel, Song, Jie, Xie, Yongdun, Rasheed, Awais, Yang, Shurong, Li, Hongye, Liu, Peng, Xia, Xianchun, He, Zhonghu, and Xiao, Yonggui

Abstract

Sustainable wheat production is challenged by climate change events such as drought, salinity, and heat stress. Senescence is a gradual programmed cell death trait occurring post anthesis in wheat (Triticum aestivum), with significant affecting stability of yield and quality‐related traits under climate severities. Phenotyping of complex traits is increasingly perceived as a bottleneck due to elevated labor costs and time in large field conditions. Unmanned aerial vehicle (UAV)‐based platforms using RGB imagery and ground phenotyping–based novel traits can facilitate the repeated nondestructive measurements of crop canopy traits cost‐effectively. Here, we described combined application of UAV‐based RGB imaging and ground measurements based novel trait to quantify canopy senescence in wheat. We reported senescence related traits with high heritability in a recombinant inbred line population derived from the cross Zhongmai 175/Lunxuan 987. Our results showed that the selection of slow senescence genotypes using UAV‐based vegetation indices (VIs) was equally effective as ground‐based traits and illustrated significant variations among the genotypes. We also identified five quantitative trait loci (QTL) for canopy senescence in both UAV and ground‐based datasets using a 50K single‐nucleotide polymorphism array. QTL for UAV‐based VIs from RGB imaging and ground measurements based traits were mapped on chromosomes 1B, 2B, 3A, and 4B. The integration of both datasets with genetic analysis identified an important slow senescence/stay‐green related locus on chromosome 4B that explained phenotypic variation up to 23.07% for both UAV and ground traits at late grain‐ filling stage. Three QTL (QTL‐caas.1B, QTL‐caas.3A, and QTL‐caas.4B2) on chromosomes 1B, 3A, and 4B were also validated for slow senescence with high chlorophyll level and cool canopy temperature in a natural population of 160 lines by developing kompetitive allele‐specific PCR markers. QTL‐caas.4B1 was validated not only for senescence but also for plant height. Our findings suggest that UAV‐based RGB imagery is advantageous for precise and rapid assessment of senescence related traits and genetic studies of senescence in wheat. Core Ideas: Unmanned aerial vehicle (UAV)‐based phenotyping platforms using RGB imagery can facilitate the repeated nondestructive measurements of canopy greenness and senescence cost‐effectively.We have integrated vegetation indices derived from UAV‐based RGB pixels and ground measurements with quantitative genetic analysis to identify loci controlling canopy senescence in wheat.We have developed kompetitive allele‐specific PCR (KASP) markers based on identified loci that contribute to variations in wheat senescence. [ABSTRACT FROM AUTHOR]

Published
2023
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42. HSP90.2 promotes CO2 assimilation rate, grain weight and yield in wheat.

Author

Yan, Yan, Wang, Meng‐Lu, Guo, Yue‐Ting, Ding, Ci‐Hang, Niu, Ke‐Xin, Li, Xiao‐Ming, Sun, Congwei, Dong, Zhongdong, Cui, Dangqun, Rasheed, Awais, Hao, Chenyang, Zhang, Xueyong, Guo, Ganggang, Ni, Zhongfu, Sun, Qixin, Chen, Feng, and Gou, Jin‐Ying

Subjects
DURUM wheat, GRAIN yields, PHOTOSYNTHETIC rates, ATMOSPHERIC carbon dioxide, WHEAT, EMMER wheat
Abstract

Summary: Wheat fixes CO2 by photosynthesis into kernels to nourish humankind. Improving the photosynthesis rate is a major driving force in assimilating atmospheric CO2 and guaranteeing food supply for human beings. Strategies for achieving the above goal need to be improved. Here, we report the cloning and mechanism of CO2ASSIMILATION RATE AND KERNEL‐ENHANCED 1 (CAKE1) from durum wheat (Triticum turgidum L. var. durum). The cake1 mutant displayed a lower photosynthesis rate with smaller grains. Genetic studies identified CAKE1 as HSP90.2‐B, encoding cytosolic molecular chaperone folding nascent preproteins. The disturbance of HSP90.2 decreased leaf photosynthesis rate, kernel weight (KW) and yield. Nevertheless, HSP90.2 over‐expression increased KW. HSP90.2 recruited and was essential for the chloroplast localization of nuclear‐encoded photosynthesis units, for example PsbO. Actin microfilaments docked on the chloroplast surface interacted with HSP90.2 as a subcellular track towards chloroplasts. A natural variation in the hexaploid wheat HSP90.2‐B promoter increased its transcription activity, enhanced photosynthesis rate and improved KW and yield. Our study illustrated an HSP90.2–Actin complex sorting client preproteins towards chloroplasts to promote CO2 assimilation and crop production. The beneficial haplotype of Hsp90.2 is rare in modern varieties and could be an excellent molecular switch promoting photosynthesis rate to increase yield in future elite wheat varieties. [ABSTRACT FROM AUTHOR]

Published
2023
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48. Exploiting the drought tolerance of wild Elymus species for bread wheat improvement.

Author

Khan, Ajab, Ali, Ahmad, Ullah, Zahid, Ali, Iftikhar, Kaushik, Prashant, Alyemeni, Mohammed Nasser, Rasheed, Awais, and Sher, Hassan

Subjects
DROUGHT tolerance, WHEAT, CLIMATE extremes, SPECIES, POLYETHYLENE glycol, BREAD
Abstract

Crop wild resources are excellent sources of new genetic variation for resilience against climate extremes. However, detailed characterization of the desirable phenotypes is essential before using these crop wild resources in breeding programs. This current study was, therefore, conducted to investigate the water stress responses of eight wild Elymus species and two wheat cultivars. The experiment was carried out under varying levels of osmotic stress induced by polyethylene glycol and progressive water stress through different field capacities. Water stress significantly reduced both physiological and biochemical traits compared to control, ranging from7.1% (protein content) to 34.5% (chlorophyll) under moderate stress and 9.1-45.8% under severe stress. The anatomical features were also affected under progressive water stress, including a reduction in xylem vessel diameter (7.92 and 16.50%), phloem length (4.36 and 7.18%), vascular bundle length (3.09 and 6.04%), and ground tissue thickness (2.36 and 5.52%), respectively. Conclusively, Elymus borianus (endemic to Swat, Pakistan), E. russelli, E. caninus, E. longioristatus, and E. dauhuricus outperformed the check wheat cultivar, Pirsabak 2005, which is a rainfed variety. The results revealed that Elymus species belonging to the tertiary gene pool of bread wheat could be an excellent drought tolerance source for use in a breeding program. [ABSTRACT FROM AUTHOR]

Published
2022
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49. Development of image-based wheat spike counter through a Faster R-CNN algorithm and application for genetic studies.

Author

Lei Li, Hassan, Muhammad Adeel, Shurong Yang, Furong Jing, Mengjiao Yang, Rasheed, Awais, Jiankang Wang, Xianchun Xia, Zhonghu He, and Yonggui Xiao

Subjects
CONVOLUTIONAL neural networks, DEEP learning, GENOTYPES, HAPLOIDY, STATISTICAL correlation
Abstract

Spike number (SN) per unit area is one of the major determinants of grain yield in wheat. Development of high-throughput techniques to count SN from large populations enables rapid and cost-effective selection and facilitates genetic studies. In the present study, we used a deep-learning algorithm, i.e., Faster Region-based Convolutional Neural Networks (Faster R-CNN) on Red-Green-Blue (RGB) images to explore the possibility of image-based detection of SN and its application to identify the loci underlying SN. A doubled haploid population of 101 lines derived from the Yangmai 16/Zhongmai 895 cross was grown at two sites for SN phenotyping and genotyped using the high-density wheat 660K SNP array. Analysis of manual spike number (MSN) in the field, image-based spike number (ISN), and verification of spike number (VSN) by Faster R-CNN revealed significant variation (P < 0.001) among genotypes, with high heritability ranged from 0.71 to 0.96. The coefficients of determination (R²) between ISN and VSN was 0.83, which was higher than that between ISN and MSN (R² = 0.51), and between VSN and MSN (R² = 0.50). Results showed that VSN data can effectively predict wheat spikes with an average accuracy of 86.7% when validated using MSN data. Three QTL Qsnyz.caas-4DS, Qsnyz.caas-7DS, and QSnyz.caas-7DL were identified based on MSN, ISN and VSN data, while QSnyz.caas-7DS was detected in all the three data sets. These results indicate that using Faster R-CNN model for image-based identification of SN per unit area is a precise and rapid phenotyping method, which can be used for genetic studies of SN in wheat. [ABSTRACT FROM AUTHOR]

Published
2022
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