Automated per-plot leaf-level imaging and analysis for small plot arable field trials

针对小地块耕地试验的自动每地块叶级成像和分析

• 批准号: 10060164
• 金额: $ 77.32万
• 依托单位: DRONE AG LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10060164
• 关键词: Automated; per; plot; leaf; level

CHALLENGE The FAO estimates that food production needs to increase 70% by 2050 to achieve global food security. Development of new crop varieties, agrochemicals and crop management techniques are key to increasing food productivity and reducing environmental impact. They rely on multi-phase testing; initially lab based, in greenhouses, then in field trials across multiple soil types and climates. The 4 largest Ag companies have a combined R&D budget of over $5bn:* In the UK; NIAB run 150,000+ plots, Agrii, 40,000+* Worldwide; UPL run 1500 multi-plot trials, Syngenta run 114 R&D sitesTrials programmes are in desperate need of innovation, As most monitoring is still done visually, manually, and in-person. SOLUTION Drone tech and AI image analysis can provide more frequent monitoring with notable time and cost savings over other techniques (up to 30x faster) plus uniform data capture/analysis for faster product development and better environmental profiles. Micro-SME business, Drone Ag will develop a new trials plot imaging and analysis system to deliver unique per-plot, extreme high resolution analytics within minutes. Through this technology, Drone Ag will enable:* Workforce upskilling; freed from manual trial evaluation by eye.* Rapid chemical and breeding iteration through time/cost saving.* Data provision, feeding new AI models for companies who previously could not repeatedly gather uniform data at such a low cost/high volume ratio.Since this would replace current practice with a more efficient, detailed system, this solution is highly innovative, disruptive and would be highly sought-after. We will be making use of feedback from key industry stakeholders throughout the project and beyond, to ensure the solution meets their requirements. The project will deliver economic benefits to and end-users, and the wider supply-chain.I would translate to wider benefits for the population as a whole including healthier and cleaner air, water and food.

挑战 粮农组织估计，到 2050 年，粮食产量需要增加 70%，才能实现全球粮食安全。开发新作物品种、农用化学品和作物管理技术是提高粮食生产力和减少环境影响的关键。他们依赖于多阶段测试；最初是在实验室的温室中进行，然后是在多种土壤类型和气候下进行的现场试验。四大农业公司的研发预算合计超过 50 亿美元：* 在英国； NIAB 运行 150,000 多个地块，Agrii，全球 40,000 多个*； UPL 运行 1500 个多区试验，先正达运行 114 个研发中心试验项目迫切需要创新，因为大多数监控仍然是通过视觉、手动和亲自进行。 解决方案 无人机技术和人工智能图像分析可以提供更频繁的监控，与其他技术相比可显着节省时间和成本（速度高达 30 倍），并且统一的数据捕获/分析可加快产品开发速度并改善环境状况。微型中小企业业务 Drone Ag 将开发一种新的试验图成像和分析系统，可在几分钟内提供独特的每图、极高分辨率分析。通过这项技术，Drone Ag 将实现：* 劳动力技能提升；摆脱肉眼手动试验评估。*通过节省时间/成本实现快速化学和育种迭代。*数据提供，为以前无法​​以如此低的成本/高体积比重复收集统一数据的公司提供新的人工智能模型。该解决方案将用更高效、更详细的系统取代当前的做法，具有高度创新性、颠覆性，并且将受到高度追捧。我们将在整个项目期间及之后利用主要行业利益相关者的反馈，以确保解决方案满足他们的要求。该项目将为最终用户以及更广泛的供应链带来经济效益。我将转化为全体人民更广泛的利益，包括更健康、更清洁的空气、水和食物。