SBIR Phase I: Using Aerial Imagery Analysis to Manage Stress in Coffee Production

SBIR 第一阶段：利用航空图像分析来管理咖啡生产中的压力

• 批准号: 1913969
• 负责人: Gregory Rose
• 金额: $ 22.5万
• 依托单位: Intelinair, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2020-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1913969&HistoricalAwards=false
• 关键词: SBIR; Phase; Using; Aerial; Imagery

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is significant. Based on the company's experience with the willingness of farmers to pay for such services, the potential commercial market for the envisioned solution for coffee alone would amount to USD 1 billion, based on the estimated 27 million acres currently in production around the world. The commercial potential for additional specialty crops would exceed several times this amount. The company's agronomy team believes that the proposed solution would result in yield improvements of the order of 5% to 10% and a reduction in combined fertilizer and water costs in the range of 3% to 8%. Moreover, the solution would result in higher-quality coffee beans which would improve the commercial viability of small coffee farms in emerging economies. This would have the potential of lifting hundreds of thousands of sub-tropical farmers and their families out of poverty while sustainably protecting soil and water resources. Precision agriculture techniques developed in this project will also enable coffee producers to address the effects of climate change by identifying destructive patterns and prescribing effective ways to maintain or improve yield and quality. This Small Business Innovation Research (SBIR) Phase I project seeks to develop novel deep learning algorithms and computer vision methods for coffee and for specialty crops in general, and thereby identify, through analysis, signatures of critical coffee tree stress conditions. To date, image analysis algorithms developed in academia typically focus on lab environments or controlled research plots. The tasks outlined in this project focus on real world coffee orchards with more demanding conditions. Challenges from the imagery point of view include managing the effects of clouds and the sun angle on the canopy, capturing tree foliage at an angle and identifying individual trees. Challenges from the deep learning area include identifying with confidence anomalous areas of stress or increasing stress for varying coffee strains, soil and topographical conditions within the orchard using multispectral and thermal imagery. From an agronomy standpoint, the project will seek to assist and/or correlate the deep learning findings with soil moisture sensors and other ground truth data. These elements will form the core of a low-cost commercial remote sensing and alerting system that is expected to be available to coffee-growing communities in the near future.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是显着的。根据该公司在农民愿意为这种服务付费方面的经验，根据目前全世界约2，700万英亩的生产面积，仅咖啡的设想解决方案的潜在商业市场就将达到10亿美元。 其他特种作物的商业潜力将超过这一数额的几倍。该公司的农学团队认为，拟议的解决方案将导致产量提高5%至10%，并将化肥和水的综合成本降低3%至8%。 此外，这一解决方案将产生更高质量的咖啡豆，从而提高新兴经济体小型咖啡农场的商业可行性。这将有可能使数十万亚热带农民及其家庭摆脱贫困，同时可持续地保护土壤和水资源。 该项目开发的精准农业技术还将使咖啡生产者能够通过确定破坏性模式和规定保持或提高产量和质量的有效方法来应对气候变化的影响。这个小型企业创新研究（SBIR）第一阶段项目旨在为咖啡和一般特种作物开发新的深度学习算法和计算机视觉方法，从而通过分析识别关键咖啡树压力条件的特征。 迄今为止，学术界开发的图像分析算法通常集中在实验室环境或受控研究地块上。本项目概述的任务侧重于条件更为苛刻的真实的世界咖啡园。 从图像的角度来看，挑战包括管理云和太阳角度对树冠的影响，捕捉树叶的角度和识别个别树木。 深度学习领域的挑战包括使用多光谱和热成像确定果园内不同咖啡品种、土壤和地形条件下的应力异常区域或应力增加区域。 从农艺学的角度来看，该项目将寻求帮助和/或将深度学习结果与土壤湿度传感器和其他地面实况数据相关联。 这些要素将构成低成本商业遥感和警报系统的核心，预计在不久的将来咖啡种植社区将可使用该系统，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估而获得支持。