SBIR Phase I: TerraSentia: Ultra-compact, Autonomous, Teachable Under-canopy Phenotyping Robot for Plant Breeders and Crop Scientists

SBIR 第一阶段：TerraSentia：面向植物育种者和作物科学家的超紧凑、自主、可教学的树冠下表型机器人

• 批准号: 1820332
• 负责人: Chinmay Soman
• 金额: $ 22.5万
• 依托单位: EarthSense, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1820332&HistoricalAwards=false
• 关键词: SBIR; Phase; TerraSentia; Ultra; compact

Broader Impacts: The broader impact of this Small Business Innovation Research (SBIR) project include improving food security, while at the same time enhancing the economic viability and environmental sustainability of large-scale production agriculture. In order to improve crop varieties, agricultural production, and sustainability of farming, there is an urgent need for better technologies to acquire under-canopy plant trait and health data. Examples of high-value under-canopy data include emergence, stem width, corn ear height, plant life-cycle events like flowering and fruiting, and symptoms of pathogens, diseases, and nutrient deficiency. Because these data cannot be obtained by aerial imaging, under-canopy data collection has dramatically greater actionability and value compared to aerial data. However no cost-effective, scalable ways of collecting this data are currently available. In fact, the state of the art is manual data collection by crop scientists (and their students or interns), agronomists, crop-scouts or farmers - an extremely labor intensive, and therefore expensive way of collecting this highly valuable data. Our work will greatly enhance the availability of under-canopy data from field crops. The commercial value of the field data for crop breeding is in excess of $50 Million/year for breeding major row-crops in the US.Intellectual Merits: This SBIR Phase I project will demonstrate the technical feasibility of autonomously collecting under-canopy data from field crops using TerraSentia, our low-cost ground robot. In preliminary work, we have built the robot hardware, demonstrated its ability to collect high-value plant data from row-crop fields, and analyze it to generate plant-trait information. In the proposed work, we will enable and demonstrate the ability of TerraSentia to collect data autonomously throughout the season. We will demonstrate the technical feasibility of fusing information from low-cost LIDAR, GPS, and vision. We will also demonstrate the feasibility using real-time control algorithms to adapt camera perspective and robot path in order to obtain the highest quality information from the complex and dynamic under- canopy field environments. These high-risk innovations will together enable long-term deployment of TerraSentia for effective data collection and phenotyping, benefiting crop scientists and agricultural product development professionals.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.

更广泛的影响：这一小型企业创新研究(SBIR)项目的更广泛影响包括改善粮食安全，同时提高大规模生产型农业的经济可行性和环境可持续性。为了改善作物品种、农业生产和耕作的可持续性，迫切需要更好的技术来获取冠层下植物特性和健康数据。高价值的林下数据包括出苗率、茎粗、玉米穗高度、开花和结果等植物生活史事件以及病原体、疾病和营养缺乏的症状。由于这些数据不能通过航空成像获得，因此，与航空数据相比，林下数据收集具有更大的可操作性和价值。然而，目前还没有成本效益高、可扩展的方式来收集这些数据。事实上，最先进的技术是由作物科学家(以及他们的学生或实习生)、农学家、作物侦察员或农民手动收集数据--这是一种劳动密集型的方式，因此收集这种高度有价值的数据的成本很高。我们的工作将极大地提高大田作物冠层下数据的可用性。用于作物育种的田间数据的商业价值超过5000万美元/年，用于育种美国的主要行作物。智力优势：这个SBIR第一阶段项目将展示使用我们的低成本地面机器人TerraSens自主收集大田作物树冠下数据的技术可行性。在前期工作中，我们已经建立了机器人硬件，展示了它从行作物田地收集高价值植物数据的能力，并对其进行分析以生成植物特征信息。在拟议的工作中，我们将启用并演示TerraSania在整个季节自主收集数据的能力。我们将论证融合来自低成本激光雷达、GPS和视觉的信息的技术可行性。我们还将论证使用实时控制算法来适应摄像机视角和机器人路径的可行性，以便从复杂和动态的树冠下田间环境中获得最高质量的信息。这些高风险的创新将使TerraSania的长期部署成为可能，以进行有效的数据收集和表型鉴定，使作物科学家和农产品开发专业人员受益。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。