Accelerated generation of elite inbreds in maize using doubled haploid technology

The creation of homozygous parental lines for hybrid development is one of the key components of commercial maize breeding programs. It usually takes up to 6 to 7 generations of selfing to obtain homozygous inbreds from the initial cross using the conventional pedigree method. Using doubled haploid (DH) method, concurrent fixation of all the genes covering entire chromosomes is possible within a single generation. For generation of DH lines, haploids are generated first by several means such as in-vitro method using tissue culture technique and in-vivo method using the haploid inducer (HI) lines. Of which, tissue culture-based methods have shown little promise for large-scale DH production as it needs good infrastructures and technical requirements. In contrast, inducer-based method provides more optimistic solutions for large-scale DH lines production. Due to its rapidity, DH technology is now being adopted in many countries including India for reducing the breeding cycle

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects

The ornamental crop industry is an important contributor to the economy in the United States. The industry has been facing challenges due to continuously increasing labor and agricultural input costs. Sensing and automation technologies have been introduced to reduce labor requirements and to ensure efficient management operations. This article reviews current sensing and automation technologies used for ornamental nursery crop production and highlights prospective technologies that can be applied for future applications. Applications of sensors, computer vision, artificial intelligence (AI), machine learning (ML), Internet-of-Things (IoT), and robotic technologies are reviewed. Some advanced technologies, including 3D cameras, enhanced deep learning models, edge computing, radio-frequency identification (RFID), and integrated robotics used for other cropping systems, are also discussed as potential prospects. This review concludes that advanced sensing, AI and robotic technologies are critically needed for the nursery crop industry. Adapting these current and future innovative technologies will benefit growers working towards sustainable ornamental nursery crop production

Análise de sementes associado a aprendizagem de máquina para identificar espécies florestais nativas

Orientador: Prof. Dr. Antonio Carlos NogueiraCoorientadores: Profª. Drª. Dagma Kratz e Prof. Dr. Richardson RibeiroTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Agrárias, Programa de Pós-Graduação em Engenharia Florestal. Defesa : Curitiba, 31/07/2023Inclui referênciasResumo: A identificação e caracterização de sementes nativas representam um desafio para o setor florestal devido à variabilidade de características morfobiométricas. Atualmente, as metodologias para a análise biométrica de sementes florestais são realizadas por especialistas humanos utilizando métodos tradicionais de medições, como os paquímetros e variáveis baseadas em tamanho. Nesse contexto, concebeu-se uma nova metodologia empregando técnicas de processamento de imagens digitais e aprendizado de máquina com base em características externas das sementes para possibilitar a identificação de espécies florestais. A pesquisa foi dividida em três capítulos distintos. No primeiro capítulo foi realizada uma análise bibliométrica para quantificar e analisar os estudos científicos que abordam a análise de imagens e o aprendizado de máquina aplicados às sementes, e com isso apontar os principais tópicos e lacunas existentes para pesquisas com sementes florestais com esse enfoque. Os resultados indicam um aumento significativo de publicações a partir de 2017, com foco predominante em espécies de culturas agrícolas. Esses estudos estão direcionados principalmente para a classificação, identificação/detecção de cultivares e avaliação da qualidade das sementes, em que apenas 6,6% das publicações abordam espécies florestais, evidenciando a necessidade de mais pesquisas nesse campo com espécies nativas. No segundo capítulo foi proposta uma metodologia de captura e processamento de imagens para caracterização e diferenciação de espécies florestais nativas. Os resultados demonstraram que a análise de imagens de sementes, por meio dessa metodologia, contribuiu para a caracterização e diferenciação de espécies florestais nativas do Brasil, o que apresenta implicações diretas nos aspectos silviculturais, ecológicos e genéticos. No terceiro capítulo foram aplicados diferentes classificadores de aprendizado de máquina associados à análise de imagens para identificar espécies florestais nativas com base em características morfobiométricas das sementes. Os resultados revelaram que é possível identificar espécies florestais nativas com taxa satisfatória de acurácia usando imagens de sementes e aprendizado de máquina. Recomenda-se o classificador de árvores de decisão para a identificação de espécies. Os resultados fornecem subsídios importantes para aprimorar a caracterização e identificação de espécies, o que pode ser aplicado em diversos campos. Por fim, este trabalho contribui para identificar espécies florestais nativas, por meio do desenvolvimento de uma metodologia de análise e processamento de imagens e da aplicação de técnicas de aprendizado de máquina em sementes florestais.Abstract: The identification and characterization of native seeds represent a challenge for the forest sector due to the variability of morphobiometric characteristics. Currently, methodologies for the biometric analysis of forest seeds are carried out by human specialists using traditional measurement methods, such as calipers and variables based on size. In this context, a new methodology was conceived using techniques of digital image processing and machine learning based on external characteristics of the seeds to enable the identification of forest species. The research was divided into three distinct chapters. In the first chapter, a bibliometric analysis was carried out to quantify and analyze scientific studies that address image analysis and machine learning applied to seeds, and thereby point out the main topics and existing gaps for research with forest seeds with this focus. The results indicate a significant increase in publications from 2017 onwards, with a predominant focus on agricultural crop species. These studies are mainly focused on classification, identification/detection of cultivars and evaluation of seed quality, in which only 6.6% of publications address forest species, highlighting the need for further research in this field with native species. In the second chapter, a methodology for capturing and processing images for the characterization and differentiation of native forest species was proposed. The results showed that the analysis of seed images, using this methodology, contributed to the characterization and differentiation of forest species native to Brazil, which has direct implications for silvicultural, ecological, and genetic aspects. In the third chapter, different machine learning classifiers associated with image analysis were applied to identify native forest species based on morphobiometric characteristics of seeds. The results revealed that it is possible to identify native forest species with a satisfactory rate of accuracy using seed images and machine learning. The decision tree classifier is recommended for species identification. The results provide important subsidies to improve the characterization and identification of species, which can be applied in several fields. Finally, this work contributes to identify native forest species, through the development of an image analysis and processing methodology and the application of machine learning techniques in forest seeds

Wheat Improvement

This open-access textbook provides a comprehensive, up-to-date guide for students and practitioners wishing to access in a single volume the key disciplines and principles of wheat breeding. Wheat is a cornerstone of food security: it is the most widely grown of any crop and provides 20% of all human calories and protein. The authorship of this book includes world class researchers and breeders whose expertise spans cutting-edge academic science all the way to impacts in farmers’ fields. The book’s themes and authors were selected to provide a didactic work that considers the background to wheat improvement, current mainstream breeding approaches, and translational research and avant garde technologies that enable new breakthroughs in science to impact productivity. While the volume provides an overview for professionals interested in wheat, many of the ideas and methods presented are equally relevant to small grain cereals and crop improvement in general. The book is affordable, and because it is open access, can be readily shared and translated -- in whole or in part -- to university classes, members of breeding teams (from directors to technicians), conference participants, extension agents and farmers. Given the challenges currently faced by academia, industry and national wheat programs to produce higher crop yields --- often with less inputs and under increasingly harsher climates -- this volume is a timely addition to their toolkit

Development of new tools and germplasms for improvement of wheat resistance to Fusarium head blight

Doctor of PhilosophyDepartment of AgronomyGuihua BaiXiaomao LinWheat Fusarium head blight (FHB) is a devastating disease of wheat worldwide, which can significantly reduce grain yield and quality. Although the application of fungicides can reduce FHB damage, growing FHB resistant wheat is the most effective and eco-friendly approach to reduce the losses. To develop locally adapted FHB-resistant hard winter wheat germplasm, we transferred three major QTLs: Fhb1, Qfhs.ifa-5A, and Qfhb.rwg-5A.2 into two hard winter wheat cultivars, ‘Everest’ and ‘Overland’, using marker-assisted backcrossing and multiplex restriction amplicon sequencing (MRASeq). Ten ‘Overland’ background lines and nine ‘Everest’ background lines with better FHB resistance, recurrent parent similar agronomic traits were selected. They can be used as FHB resistant bridge parents for hard winter wheat breeding. To identify native FHB resistant sources, a population of 201 U.S. breeding lines and cultivars were genotyped using 90K wheat SNP arrays and phenotyped for the percentage of symptomatic spikelets (PSS), Fusarium damaged kernels (FDK) and deoxynivalenol (DON), a toxin produced by the pathogen. Genome-wide association studies (GWAS) identified significant trait associations with single nucleotide polymorphisms (SNPs) on chromosomes 1A, 1D, 2B, 3A, 3B, 4A, 5B and 5D. These marker-trait associations (MTAs) were significant for at least two of the three traits or a single trait in at least two experiments. To accelerate the evaluation of the FDK, we developed an algorithm that can separate FDK from healthy kernels with an accuracy of 90% based on color differences using image processing and unsupervised machine learning methods. Discovery and creation of the new FHB resistant germplasms and development of the fast FDK phenotyping algorithm will accelerate the improvement of U.S. hard winter wheat cultivars for FHB resistance

Capturing wheat phenotypes at the genome level

Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world’s most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public–private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

Current status and trends in forest genomics

Forests are not only the most predominant of the Earth\u27s terrestrial ecosystems, but are also the core supply for essential products for human use. However, global climate change and ongoing population explosion severely threatens the health of the forest ecosystem and aggravtes the deforestation and forest degradation. Forest genomics has great potential of increasing forest productivity and adaptation to the changing climate. In the last two decades, the field of forest genomics has advanced quickly owing to the advent of multiple high-throughput sequencing technologies, single cell RNA-seq, clustered regularly interspaced short palindromic repeats (CRISPR)-mediated genome editing, and spatial transcriptomes, as well as bioinformatics analysis technologies, which have led to the generation of multidimensional, multilayered, and spatiotemporal gene expression data. These technologies, together with basic technologies routinely used in plant biotechnology, enable us to tackle many important or unique issues in forest biology, and provide a panoramic view and an integrative elucidation of molecular regulatory mechanisms underlying phenotypic changes and variations. In this review, we recapitulated the advancement and current status of 12 research branches of forest genomics, and then provided future research directions and focuses for each area. Evidently, a shift from simple biotechnology-based research to advanced and integrative genomics research, and a setup for investigation and interpretation of many spatiotemporal development and differentiation issues in forest genomics have just begun to emerge

Integrative Advances in Rice Research

This book describes some recent advances in rice research in terms of crop breeding and improvement (Section 1), crop production and protection (Section 2), and crop quality control and food processing (Section 3). It contains fourteen chapters that cover such topics as two-line rice breeding in India, the different aspects of aromatic rice, bacterial diseases of rice, quality control and breeding strategies, and much more. This volume is a useful reference for professionals and graduate students working in all areas of rice science and technology