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[前沿资讯 ] 华为、浙大联合开发基于棉花全基因组大数据与AI加速算法的“AI育种”芯片 进入全文
快科技
9月26日，浙江大学在“AI+生物育种”西湖学术论坛发布全球首款基于棉花全基因组大数据与AI加速算法的“一站式”育种智能体“AI育种家”。该平台由浙大张天真团队联合华为打造，集成昇腾910B芯片、EIHealth AI基因平台、盘古大模型与鸿蒙系统，依托团队构建的全球首个棉花多组学数据库COTTONOMICS，实现计算效率提升100倍、杂交育种组合效率提升20倍，将棉花育种周期从6—8年压缩至3—4年，并已具备向水稻、大豆、油菜、西瓜、西兰花等作物扩展的能力。 这是全球首款基于棉花全基因组大数据与AI加速算法的“一站式”育种智能体。标志着我国在智慧农业与生物育种技术领域跻身国际前列。 浙大张天真教授团队深耕二十余年，解析全球5000余个棉花品种的79642个基因，鉴定出1000多个产量、品质相关基因位点，构建全球首个棉花多组学数据库COTTONOMICS，为芯片研发奠定数据基础。 “AI育种家”搭载了华为AI芯片昇腾910B，采用华为EIHealth AI基因平台为基础，在盘古大模型和鸿蒙系统上构建。 团队成员介绍，这枚芯片能够加速计算效率能力提升100倍，棉花杂交育种组合效率提升20倍，棉花育种周期直接从传统的6-8年压缩到3-4年。 而且，“AI育种家”设计之初就具备多作物扩展能力，除了棉花，正在延伸应用于水稻、大豆、油菜、西瓜、西兰花等作物的育种研究。
[前沿资讯 ] 棉花纤维变长的关键“密码”找到了 进入全文
光明网
纤维长度是决定棉花商业价值和工业应用的重要农艺性状，是棉花驯化和遗传改良的核心目标。自然变异产生的核苷酸多态性被认为是驱动纤维长度多样性的主要遗传基础。 近日，中国农业科学院棉花研究所和西部农业研究中心等单位合作，系统解析了支架蛋白GhTTL启动子的自然变异调节棉纤维伸长分子机制，阐明了棉纤维长度自然变化的分子基础，为棉花分子遗传改良提供了候选靶点。相关研究成果发表在《植物通讯（Plant Communications）》上。 该研究明确了支架蛋白GhTTL是棉纤维伸长的关键正调控因子，发现启动子区域的自然多态性显著影响上游转录因子对其的结合亲和力，从而调节该蛋白在不同棉花品种中的表达水平。 该研究还揭示了一个全新的调节机制，其中该蛋白与蛋白激酶相互作用并将其锚定在细胞膜上，这种相互作用降低了蛋白激酶的细胞质水平，阻止其与转录因子和下游底物的结合和磷酸化，促进纤维细胞的伸长。研究结果为棉纤维伸长的调控提供了更深入的见解。 该研究得到国家自然科学基金、新疆维吾尔自治区重大科技专项等项目资助。
[前沿资讯 ] What happens when AI comes to the cotton fields 进入全文
PHYS ORG
Precision agriculture uses tools and technologies such as GPS and sensors to monitor, measure and respond to changes within a farm field in real time. This includes using artificial intelligence technologies for tasks such as helping farmers apply pesticides only where and when they are needed. However, precision agriculture has not been widely implemented in many rural areas of the United States. We study smart communities, environmental health sciences and health policy and community health, and we participated in a research project on AI and pesticide use in a rural Georgia agricultural community. Our team, led by Georgia Southern University and the City of Millen, with support from University of Georgia Cooperative Extension, local high schools and agriculture technology company FarmSense, is piloting AI-powered sensors to help cotton farmers optimize pesticide use. Georgia is one of the top cotton-producing states in the U.S., with cotton contributing nearly US$1 billion to the state's economy in 2024. But only 13% of Georgia farmers use precision agriculture practices. Public-private-academic partnership Innovation drives economic growth, but access to it often stops at major city limits. Smaller and rural communities are frequently left out, lacking the funding, partnerships and technical resources that fuel progress elsewhere. At the same time, 75% of generative AI's projected economic impact is concentrated in customer operations, marketing, software engineering and research and development, according to a 2023 McKinsey report. In contrast, applications of AI that improve infrastructure, food systems, safety and health remain underexplored. Yet smaller and rural communities are rich in potential—home to anchor institutions like small businesses, civic groups and schools that are deeply invested in their communities. And that potential could be tapped to develop AI applications that fall outside of traditional corporate domains. The Partnership for Innovation, a coalition of people and organizations from academia, government and industry, helps bridge that gap. Since its launch almost five years ago, the Partnership for Innovation has supported 220 projects across Georgia, South Carolina, Kentucky, Tennessee, Virginia, Texas and Alabama, partnering with more than 300 communities on challenges from energy poverty to river safety. One Partnership for Innovation program provides seed funding and technical support for community research teams. This support enables local problem-solving that strengthens both research scholarship and community outcomes. The program has recently focused on the role of civic artificial intelligence—AI that supports communities and local governments. Our project on cotton field pesticide use is part of this program. Cotton pests and pesticides Our project in Jenkins County, Georgia, is testing that potential. Jenkins County, with a population of around 8,700, is among the top 25 cotton-growing counties in the state. In 2024, approximately 1.1 million acres of land in Georgia were planted with cotton, and based on the 2022 agricultural county profiles census, Jenkins County ranked 173rd out of the 765 counties producing cotton in the United States. The state benefits from fertile soils, a subtropical-to-temperate climate, and abundant natural resources, all of which support a thriving agricultural industry. But these same conditions also foster pests and diseases. Farmers in Jenkins County, like many farmers, face numerous insect infestations, including stink bugs, cotton bollworms, corn earworms, tarnished plant bugs and aphids. Farmers make heavy use of pesticides. Without precise data on the bugs, farmers end up using more pesticides than they likely need, risking residents' health and adding costs. While there are some existing tools for integrated pest management, such as the Georgia Cotton Insect Advisor app, they are not widely adopted and are limited to certain bugs. Other methods, such as traditional manual scouting and using sticky traps, are labor-intensive and time-consuming, particularly in the hot summer climate. Our research team set out to combine AI-based early pest detection methods with existing integrated pest management practices and the insect advisor app. The goal was to significantly improve pest detection, decrease pesticide exposure levels and reduce insecticide use on cotton farms in Jenkins County. The work compares different insect monitoring methods and assesses pesticide levels in both the fields and nearby semi-urban areas. We selected eight large cotton fields operated by local farmers in Millen, four active and four control sites, to collect environmental samples before farmers began planting cotton and applying pesticides. The team was aided by a new AI-based insect monitoring system called the FlightSensor by FarmSense. The system uses a machine learning algorithm that was trained to recognize the unique wingbeats of each pest insect species. The specialized trap is equipped with infrared optical sensors that project an invisible infrared light beam—called a light curtain—across the entrance of a triangular tunnel. A sensor monitors the light curtain and uses the machine learning algorithm to identify each pest species as insects fly into the trap. FlightSensor provides information on the prevalence of targeted insects, giving farmers an alternative to traditional manual insect scouting. The information enables the farmers to adjust their pesticide-spraying frequency to match the need. What we've learned Here are three things we have learned so far: Predictive pest control potential—AI tools can help farmers pinpoint exactly where pest outbreaks are likely—before they happen. That means they can treat only the areas that need it, saving time, labor and pesticide costs. It's a shift from blanket spraying to precision farming—and it's a skill farmers can use season after season. Stronger decision-making for farmers—The preliminary results indicate that the proposed sensors can effectively monitor insect populations specific to cotton farms. Even after the sensors are gone, farmers who used them get better at spotting pests. That's because the AI dashboards and mobile apps help them see how pest populations grow over time and respond to different field conditions. Researchers also have the ability to access this data remotely through satellite-based monitoring platforms on their computers, further enhancing the collaboration and learning. Building local agtech talent—Training students and farmers on AI pest detection is doing more than protecting cotton crops. It's building digital literacy, opening doors to agtech careers and preparing communities for future innovation. The same tools could help local governments manage mosquitoes and ticks and open up more agtech innovations. Blueprint for rural innovation By using AI to detect pests early and reduce pesticide use, the project aims to lower harmful residues in local soil and air while supporting more sustainable farming. This pilot project could be a blueprint for how rural communities use AI generally to boost agriculture, reduce public health risks and build local expertise. Just as important, this work encourages more civic AI applications—grounded in real community needs—that others can adopt and adapt elsewhere. AI and innovation do not need to be urban or corporate to have a significant effect, nor do you need advanced technology degrees to be innovative. With the right partnerships, small towns, too, can harness innovations for economic and community growth.
[学术文献 ] Biochemical defense responses in cotton: secondary metabolite and antioxidant shifts under thrips infestation 进入全文
PHYTOPARASITICA
Thrips are among the most destructive pests of cotton, causing significant losses in yield and fiber quality through direct feeding. This study aimed to investigate the biochemical responses associated with thrips resistance in two cotton genotypes- LH 2107 (susceptible) and LD 491 (tolerant). Key defense-related parameters, including phenolic metabolism, antioxidant activity, and volatile compounds, were analyzed under both uninfested and thrips-infested conditions. Activities of phenylalanine ammonia-lyase and tyrosine ammonia lyase increased significantly in LD 491 during an infestation, indicating enhanced phenolic biosynthesis. Both genotypes exhibited increased levels of total phenols, o-dihydroxy phenols and total flavanols from 15 days post-infestation, with LD 491 showing significantly higher concentrations than LH 2107. Antioxidant assays revealed a marked rise in DPPH free radical scavenging activity, FRAP activity, total reducing power, superoxide anion radical scavenging activity, and hydroxyl radical scavenging activity, with LD 491 demonstrating the greatest enhancement. GC-MS analysis of volatile compounds showed genotype-specific differences, with the compound palmitin, 2-mono- uniquely present in LD 491 under both conditions. These findings suggested that elevated phenolic content, antioxidant activity, and specific volatiles contribute to thrips resistance in cotton. The identified biochemical markers may serve as valuable tools for screening and breeding thrips-resistant cotton genotypes.
[前沿资讯 ] UTIA participates in national study analyzing microbial communities, environmental factors impacting cotton development 进入全文
UNIVERSITY OF TENNESSEE INSTITUTE OF AGRICULTURE
Soil microbial communities play a vital role in plant health, influencing root development, disease resistance, nutrient and soil water uptake and more. In a pioneering study, the University of Tennessee Institute of Agriculture (UTIA) is partnering with universities across the country to investigate how these microbial communities impact cotton development and overall yield across diverse climates, agricultural practices and environmental stressors. In addition to extreme conditions such as drought and flooding, cotton crops are often affected by plant diseases like cotton leaf crumple virus and cotton leafroll dwarf virus, as well as insect infestations from whiteflies, aphids and others. While these factors often cause only minimal damage individually, their combined effect with abiotic and environmental stressors can hinder crop growth and disrupt the soil rhizosphere, or the area around a plant’s root system containing microbial communities. To assess the health of the soil rhizosphere in varying environments and agricultural practices, as well as to determine the beneficial or harmful roles of different microbes, research teams are using advanced sequencing technologies to analyze leaf and soil samples from geographically diverse areas. This includes the low desert of Palo Verde Valley, California; the high desert of Safford, Arizona; the High Plains of Lubbock, Texas and the Cotton Belt in West Tennessee, each with distinct elevations, temperatures, soil compositions, precipitation rates, humidity levels and more. “Few studies have explored the relationship between microorganisms, agricultural practices such as cover crops and select cotton varieties. This research is only possible thanks to the collaboration of universities nationwide,” says Avat Shekoofa, crop physiology researcher at UTIA. “Our data has the potential to better shape agricultural and plant breeding practices, as well as help farmers incorporate soil microbial considerations into their cotton operations regardless of their location or environmental challenges.” “Crop production is complex at both macro and micro levels,” says Judith Brown, project lead and plant pathologist from The School of Plant Sciences at the University of Arizona. “As farmers continue to navigate agronomic, economic, and environmental pressures, there’s a clear need for reliable assessment tools for soil health.” Randy Norton, Extension agronomist and cotton specialist with the University of Arizona, hopes their findings will improve yield and long-term sustainability. “The more control we can give to farmers, the better off their operations will be at all stages of production.” The project is led by the University of Arizona, in collaboration with UTIA, Texas A&M University and the University of California. The research is made possible thanks to support from Cotton Incorporated. Preliminary data collected in 2025 will be used to formulate a proposal to the USDA National Institute of Food and Agriculture’s Agriculture and Food Research Initiative Commodity Board Co-funding Topics for further research in the coming years.
[学术文献 ] The Genetic Loci Associated with Fiber Development in Upland Cotton (Gossypium hirsutum L.) Were Mapped by the BSA-Seq Technique 进入全文
PLANTS-BASEL
Cotton fiber quality improvement remains a fundamental challenge in breeding programs due to the complex genetic architecture underlying fiber development. The narrow genetic base of upland cotton (Gossypium hirsutum L.) and the quantitative nature of fiber quality traits necessitate innovative approaches for identifying and incorporating superior alleles from related species. We developed a BC6F2 population by introgressing chromosome segments from the sea island cotton variety Xinhai 36 (G. barbadense) into the upland cotton variety Xinluzhong 60 (G. hirsutum). Based on fiber strength phenotyping, we constructed two DNA bulks representing extreme phenotypes (20 superior and 12 inferior individuals) for bulked segregant analysis sequencing (BSA-Seq). High-throughput sequencing generated 225.13 Gb of raw data with average depths of 20x for parents and 30x for bulks. SNP calling and annotation were performed using GATK and ANNOVAR against the upland cotton reference genome (TM-1). BSA-Seq analysis identified 13 QTLs primarily clustered within a 1.6 Mb region (20.6-22.2 Mb) on chromosome A10. Within this region, we detected nonsynonymous mutation genes involving a total of six genes. GO and KEGG enrichment analyses revealed significant enrichment for carbohydrate metabolic processes, protein modification, and secondary metabolite biosynthesis pathways. Integration with transcriptome data prioritized GH_A10G1043, encoding a beta-amylase family protein, as the key candidate gene. Functional validation through overexpression and RNAi knockdown in Arabidopsis thaliana demonstrated that GH_A10G1043 significantly regulates starch content and beta-amylase activity, though without visible morphological alterations. This study successfully identified potential genomic regions and candidate genes associated with cotton fiber strength using chromosome segment substitution lines combined with BSA-Seq. The key candidate gene GH_A10G1043 provides a valuable target for marker-assisted selection in cotton breeding programs. Our findings establish a foundation for understanding the molecular mechanisms of fiber quality formation and offer genetic resources for developing superior cotton varieties with enhanced fiber strength.
