ARCC Journals’ Post

🧩🔬👨🎓📈➡️🧑🌾📈👍💚 #agrobiodiversity Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction Yunbi Xu, Xingping Zhang, Huihui Li, Hongjian Zheng, Jianan Zhang, Michael S. Olsen, Rajeev K. Varshney, Boddupalli M. Prasanna, Qian Qian, Molecular Plant, Volume 15, Issue 11, 2022, Pages 1664-1695, https://lnkd.in/d3cGpeJ4 #plantbreeding #bigdata #AI Abstract The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support. Key words smart breeding genomic selection integrated genomic-enviromic selection spatiotemporal omics crop design machine and deep learning big data artificial intelligence

🧩🔬👨🎓📈➡️🧑🌾📈👍💚 #agrobiodiversity - Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction Yunbi Xu, Xingping Zhang, Huihui Li, Hongjian Zheng, Jianan Zhang, Michael S. Olsen, Rajeev K. Varshney, Boddupalli M. Prasanna, Qian Qian, Molecular Plant, Volume 15, Issue 11, 2022, Pages 1664-1695, https://lnkd.in/d3cGpeJ4 #plantbreeding #bigdata #AI Abstract The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support. Key words smart breeding genomic selection integrated genomic-enviromic selection spatiotemporal omics crop design machine and deep learning big data artificial intelligence

🧩🔬👨🎓📈➡️🧑🌾📈👍💚 #agrobiodiversity Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction Yunbi Xu, Xingping Zhang, Huihui Li, Hongjian Zheng, Jianan Zhang, Michael S. Olsen, Rajeev K. Varshney, Boddupalli M. Prasanna, Qian Qian, Molecular Plant, Volume 15, Issue 11, 2022, Pages 1664-1695, https://lnkd.in/d3cGpeJ4 #plantbreeding #bigdata #AI #Genomics #climatechange Abstract The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support. Key words smart breeding genomic selection integrated genomic-enviromic selection spatiotemporal omics crop design machine and deep learning big data artificial intelligence

🌾 The Future of Crop Breeding: Insights from a New Critical Review 🌾 I’m excited to share a recent study titled "Modern Plant Breeding Techniques in Crop Improvement and Genetic Diversity," which highlights the transformative impact of advanced technologies on agriculture. This comprehensive review dives deep into the tools and methods reshaping how we approach crop breeding and sustainability. 🔬 Study highlights: Gene Editing (CRISPR/Cas9): Precise gene editing allows for targeted crop improvements, such as enhancing disease resistance and stress tolerance. Molecular Markers & GWAS: Techniques like genome-wide association studies (GWAS) and molecular markers are helping scientists link specific genes to desirable traits more efficiently, speeding up the breeding process. Artificial intelligence (AI): AI is being integrated into plant breeding to analyze large-scale data, making phenotypic and genotypic analysis faster and more accurate. Genetic Diversity: By utilizing advanced techniques like transposable elements, mutagenesis, and other genetic mapping tools, this study addresses the critical need to expand crop genetic diversity, making plants more resilient to environmental stresses. 🌱 Why This Matters: The innovations covered in this study are critical for addressing challenges like climate change, food security, and sustainability. These modern breeding techniques improve crop yields and make crops more adaptable to future environmental conditions. For a deeper dive into the science and potential applications of these techniques, please check out the complete study in the attached document. #PlantBreeding #Genomics #GeneEditing #AIInAgriculture #CropImprovement #Sustainability #FoodSecurity

📢 AI isn't just for tech—it's transforming agriculture too! Explore the latest AI methods for genomic prediction and revolutionize your breeding programs. #AgriculturalAI #GenomicPrediction ✨ Register for the 2nd Multi-Omic Short Course and learn first hand from the experts. This week-long short course will take place here at The University of Florida Institute of Food and Agricultural Sciences #Straughn Center, July 15-19. Dr. Diego Jarquin, Assistant Professor of Multi-Omics has invited a cadre of speakers that you would not want to miss. ❇ Dr. Esteban Rios, Assistant Professor UF/IFAS Agronomy Department, his specialization in plant breeding and genetics, quantitative genetics, and forage production are the foundation of his research questions as he looks to improve yield, nutritive value, abiotic and biotic stress tolerance in forage species. Two major goals in his program is to contribute to the livestock and agricultural industries by creating and releasing cultivars with higher yield and quality, and greater resilience to abiotic and biotic stresses, and to conduct theoretical and applied research in quantitative genetics and genomics to make the plant breeding process more efficient. #ForageBreeding ❇ Dr. Xu Wang, is an Assistant Professor University of Florida Department of Agricultural and Biological Engineering located Gulf Coast REC. His research focused on building automated proximal and remote sensing systems for field-based plant phenotyping, development novel data analysis and image processing pipelines for plant trait extraction and supporting cutting-edge machine learning methods for complex trait detection and yield prediction. He coordinates a data center storing high-value crops referencing data that includes images, derived phenotypes, along with genetic and genomic data for breeding and genetic research. #MultiOmic #IFASResearch #MachineLearning #AIatUF #Phenomics #GeonomicPrediction For more information on the course visit: https://lnkd.in/ejt38dtc To register: https://lnkd.in/eSpfXUYf

DON'T FORGET IT! 📅 This Friday 23rd February, 12:30H (CET) 📍Conference Hall, 3rd floor, IBMCP 🔬"AI-based tools for fruit breeding" 🗣️Dr. María José Aranzana. Crop improvement has significantly advanced over the past decades due to the shift from traditional breeding, exclusively dependent on phenotypic information, to decisions informed by genetic data. This progress has been recently accelerated thanks to high-throughput phenotyping platforms and the modernization of phenotyping techniques such as the utilization of drones and robots in the field coupled with machine learning image processing and analysis. We have contributed to this field with the development of AI-based tools to evaluate fruit shape and size from images (Shape Analyzer), and with a model to track leaves along plant growth while acquiring relevant data for leaf photosynthetic capacity (LeTra). While phenotyping tools have advanced rapidly, genetic tools have not lagged behind. All of this contributes to the generation of a vast amount of data that must be analyzed and interrelated.In plant breeding, few molecular markers that detect genetic variants associated with traits controlled by major genes are used to select new individuals without the need to observe their phenotype. In the case of complex traits, the use of a greater number of markers and statistical models is usually required to predict the behavior of new individuals. However, these methods have limitations associated with the distribution of phenotypic variables (in the case of linear models) or the required specifications of Bayesian models. To overcome these limitations, we have built a prediction model based on convolutional neural networks (CNN), GenoDrawing, capable of predicting and generating apple images from a reduced matrix of SNP markers. A similar strategy could be used to predict other hereditary traits of greater agricultural or economic relevance, such as fruit color or severity of symptoms caused by pests and diseases in specific genotypes.

Ten Facts bout PLANT GENETICS 🧬 ⬇️⬇️⬇️ 1. DNA and Genes: Just like animals, plants have DNA made up of genes that determine their traits. These genes control everything from the color of flowers to the height of the plant. 2. Polyploidy: Many plants are polyploid, meaning they have more than two sets of chromosomes. This can lead to greater genetic diversity and can result in new species. 3. Mendelian Inheritance: Gregor Mendel’s work with pea plants laid the foundation for modern genetics. His studies on how traits are passed from one generation to the next are still relevant in plant genetics today. 4. Genetic Modification: Scientists use genetic engineering to create genetically modified organisms (GMOs), including plants that are resistant to pests, diseases, or harsh environmental conditions. 5. Hybridization: Plant breeders often cross different species or varieties to create hybrids with desirable traits, like better yield or resistance to certain diseases. 6. Epigenetics: In plants, epigenetic changes can influence traits without altering the underlying DNA sequence. These changes can be passed on to future generations and can affect how plants respond to their environment. 7. Genome Editing: Techniques like CRISPR-Cas9 allow precise modifications of plant genes, enabling the development of crops with specific traits, such as improved nutritional content or drought tolerance. 8. Natural Selection: In the wild, plants undergo natural selection, where those best suited to their environment survive and reproduce, passing on their genes to the next generation. 9. Genetic Diversity: Maintaining genetic diversity in crops is crucial for food security, as it allows for the adaptation to changing environmental conditions and resistance to diseases. 10. Cloning: Some plants can reproduce asexually through cloning, producing genetically identical offspring. This is common in plants like potatoes and strawberries. #Proudly Plant Breeder 🌱 #Proudly Sustainable Agriculture 👩🌾

➡️AgroGenome: Interactive Genomic-Based Web Server Developed Based on Data Collected for Accessions Stored in Polish Genebank J. H. Czembor, E. Czembor, M. Krystek, J. Pukacki ➡️ https://lnkd.in/dA3JkJuV ➡️ #agrobank #gospostrateg #NCBR - AKIS PLATFORM FOR FARMERS, BREEDERS AND SCIENTISTS 🧬🔬👨🎓💻🌾🌽📲🧑🌾📈💚🍞👍 #PCSS #IHAR #MRiRW #germplasm #plantbreeding #genebank #Genomics #FoodSecurity #climatechange #information #AgData #data #agriculture #farmers #genetics #cropimprovement #resistance #biodiversity #agrobiodiversity Abstract New intensive farming systems have resulted in a narrowing of the genetic diversity used in breeding programs. Breeders are looking for new sources of variation of specific traits to make genetic progress in adaptation to changing environmental conditions. Genomics-based plant germplasm research seeks to apply the techniques of genomics to germplasm characterization. Using these new methods and obtained data, plant breeders can increase the rate of genetic gains in specific breeding programs. Due to the complexity of heterogeneous sources of information, it is necessary to collect large quantities of referenced data. Molecular platforms are becoming increasingly important for the development of strategic germplasm resources for more effective molecular breeding of new cultivars. Following this trend in plant breeding, the AgroGenome portal for precise breeding programs was developed based on data collected for accessions stored in the Polish Genebank. It combines passport data of genotypes, phenotypic characteristics and interactive GWAS analysis visualization on the Manhattan plots based on GWAS results and on JBrowse interface. The AgroGenome portal can be utilized by breeders or researchers to explore diversity among investigated genomes. It is especially important to identify markers for tracking specific traits and identify QTL. The AgroGenome portal facilitates the exploitation and use of plant genetic resources stored in the Polish Genebank. Keywords: AgroGenome portal; biodiversity; crop design; big data; genomic selection; genomics; plant genetic resources; barley; wheat; soybean; pea

Indian Journal of Agricultural Research Published Volume 58 Issue 3 (June 2024) Molecular Identification and Genetic Diversity of Alternaria Isolates Causing Leaf Spot Disease in Cotton from Major Cotton Growing Areas of South Zone of India A. Sampathkumar, K.P. Raghavendra doi: 10.18805/IJARe.A-6104 Cite article:- Sampathkumar A., Raghavendra K.P. (2024). Molecular Identification and Genetic Diversity of Alternaria Isolates Causing Leaf Spot Disease in Cotton from Major Cotton Growing Areas of South Zone of India . Indian Journal of Agricultural Research. 58(3): 532-538. doi: 10.18805/IJARe.A-6104. ABSTRACT Background: Alternaria leaf spot caused by two species namely Alternaria macrospora and Alternaria alternata is an important foliar disease of cotton. Conidial morphology showed that most of the isolates in the study belonged to A. macrospora. Molecular confirmation is necessary to strengthen the identification of species in Alternaria. Genetic diversity study of Alternaria isolates using ISSR and hyper variable SSR primers will provide variation and grouping among the isolates collected from major areas of South Zone of India. Present study was conducted to identify Alternaria isolates at species level using molecular methods (species specific primers) and genetic diversity analysis using ITS, SSR and ISSR primers. Methods: Reported species-specific primers such as AmF and AmR as well as AaF2 and AaF3 were used for Alternaria species identification. ITS region was amplified through ITS1 and ITS4 and sequences were used for identification and clustering of isolates. Thirteen hyper variable SSR primers specific to Alternaria were designed based on the sequences retrieved from NCBI and used for diversity study. Six different ISSR primers were also used for genetic diversity study. Result: Reported species-specific primers found not suitable to identify A. macrospora and A. alternata at species level. Two SSR primers were found to be effective in showing variability among the isolates. Six clusters were formed at 71 percent genetic dissimilarity among 15 isolates of Alternaria through ISSR primers. Five clusters were formed in ITS sequence’s diversity analysis. Blasting of ITS sequences of 15 selected isolates at NCBI showed that all belong to A. alternata. This was due to absence or presence of very few sequences of A. macrospora in NCBI database itself. Further house-keeping genes like Alt a1, Plasma membrane ATPase, GAPDH and TEF -1 α sequence analysis will be useful for confirmation of A. macrospora at species level. KEYWORDS Alternaria Species, Cotton, ISSR, Its, Molecular Identification And Characterization, SSR https://lnkd.in/gbBywiGd

Agricultural Science Digest Published Volume 44 Issue 3 (JUNE 2024) Assessing Genetic Diversity and Population Structure of Rice Genotypes using ISSR Markers M. Jegadeeswaran, V.N. Nithya, A.P. Salini, J. Vanitha, R. Mahendran, M. Maheswaran doi10.18805/ag.D-5855 Cite article:- Jegadeeswaran M., Nithya V.N., Salini A.P., Vanitha J., Mahendran R., Maheswaran M. (2024). Assessing Genetic Diversity and Population Structure of Rice Genotypes using ISSR Markers . Agricultural Science Digest. 44(3): 453-459. doi: 10.18805/ag.D-5855. ABSTRACT Background: The realm of genetic diversity within rice is immense and undermining it provides the opportunity to utilize them in rice improvement programs. Hence in our study, we aimed to undermine the genetic composition and structure of the selected rice accessions utilizing ISSR marker systems. Methods: ISSR analysis encompassed a set of thirty genotypes, comprising 16 cultivated varieties and 14 landraces. The screening was performed with a total of 49 ISSR primers. The consensus tree constructed from banding patterns generated by ISSR-PCR clustered 30 genotypes according to their respective genomes. The Sequential Agglomerative Hierarchical Non-overlapping (SAHN) clustering was employed with the Unweighted Pair Group Method with Arithmetic Averages (UPGMA) method. The grouping of the 30 accessions was carried out through data analysis using NTSYSpc 2.02. Result: Utilizing 49 ISSR markers, the cluster analysis produced three clusters. These clusters displayed pronounced separation and exhibited evident patterns. The Cluster I encompassed Bharathi, BG367-2, PTB33, ASD9, ASD16, ASD20, Rathu Heenati and Columbia-2. Notably, the largest cluster was Cluster II comprising 20 accessions, while Cluster III contained only Jeeraga Samba and Basmati 370. The study validates the efficacy of ISSR markers in detecting polymorphism within and among rice populations and/or species. The resulting DNA profiles hold potential for serving as diagnostic fingerprints of both cultivated and wild rice germplasm, aiding in comprehending evolutionary relationships. KEYWORDS Genetic Diversity, Issr Markers, Population Structure, Rice https://lnkd.in/gA5v-aPd