Aerial robotics sampling for agricultural scouting

用于农业侦察的空中机器人采样

• 批准号: RGPIN-2021-03933
• 负责人: LussierDesbiens, Alexis
• 金额: $ 2.33万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741814
• 关键词: Aerial; robotics; sampling; agricultural; scouting

Since 2004, food prices have been rising due to depleted food stocks resulting not only from increased demand, but also increased fertilizer/pesticide costs, oil prices, labor shortages and extreme weather. To optimize the production and health of these crops, current techniques involve regular "scouting" to visually inspect the crops for growth stage, nutrient deficiencies, injuries, disease, and fungus, but more importantly, to allow for the sampling of tissue, soil and insects. This allows the early detection of nutrient deficiency, of fungus infection or of insect infestations. Scouting leads to better productivity, reduces operating costs, and reduces the environmental impacts of fertilizers and pesticides. Unfortunately, these techniques require the seasonal help of a large number of skilled workers. As a result, these jobs are ranked in the top 2 in-demand positions and hardest. Accordingly, it is claimed that the main obstacle to farming is the large area of farmed land and low efficiency in crop monitoring. Currently, human scouts have to walk large areas. It is estimated that "traditional" scouting requires up to 5h per 100 acres for only a few data points, with critical repeatability issues. With the right technological developments, robotics could speed up operations, help fill the shortage of skilled workers, and increase sampling resolution and repeatability. Fast flying drones are already being used to monitor plant stress through multispectral imagery. However, the consensus is that on-the-ground scouting is required as plant stress than can be detected by imagery will have already decreased production below desirable economic levels. Furthermore, imagery does not clearly differentiate between the different plant stressors. Only preventive large-scale scouting, together with spatial and temporal analysis, can provide the data needed to reap the full benefits promised by precision agriculture. It is predicted that robotics scouting will drive the next agriculture revolution. This proposal thus aims at creating aerial sampling solutions that will enable fast scouting of large fields. However, due to the risks and difficulties involved with hardware development, particularly of in-contact aerial robotics, few efforts are currently devoted to the technologies required for aerial agricultural scouting. As such, the short-term objectives of this program focus on demonstrating the potential of novel hardware technologies, which include, full top-to-bottom plant imagery, plant tissues samplers, soil samplers and insect/fungus samplers. This research program will contribute to training 2 PhD, 2 MScA and 5 coop trainees in the field of aerial/agricultural robotics. They will publish in top ranked conferences and scientific journals while developing skills in high demand by the industry. Beside improving the worldwide food production, it is estimated that the total addressable market of drone powered solutions in agriculture is $32B.

自2004年以来，由于粮食库存耗尽，粮食价格一直在上涨，这不仅是由于需求增加，而且是化肥/农药成本增加、石油价格、劳动力短缺和极端天气造成的。为了优化这些作物的生产和健康，当前的技术包括定期“侦察”，目视检查作物的生长阶段、营养缺乏、损伤、疾病和真菌，但更重要的是，可以对组织、土壤和真菌进行采样。昆虫。这样可以及早发现营养缺乏、真菌感染或昆虫侵扰。侦察可以提高生产力，降低运营成本，并减少化肥和农药对环境的影响。不幸的是，这些技术需要大量熟练工人的季节性帮助。因此，这些工作被列为需求量最高的前 2 个职位，也是最难的职位。据此，据称，农业的主要障碍是耕地面积大和作物监测效率低。 目前，人类侦察兵必须在大范围内行走。据估计，“传统”侦察每 100 英亩需要长达 5 小时才能获得几个数据点，并且存在严重的重复性问题。通过正确的技术发展，机器人技术可以加快操作速度，帮助填补熟练工人的短缺，并提高采样分辨率和可重复性。快速飞行的无人机已被用于通过多光谱图像监测植物应激。然而，共识是需要进行实地勘察，因为通过图像检测到的植物胁迫已经导致产量低于理想的经济水平。此外，图像并不能清楚地区分不同的植物应激源。只有预防性大规模侦察，加上时空分析，才能提供获得精准农业所带来的全部效益所需的数据。据预测，机器人侦察将推动下一次农业革命。因此，该提案旨在创建空中采样解决方案，以实现对大面积区域的快速侦察。 然而，由于硬件开发，特别是接触式空中机器人的开发存在风险和困难，目前在空中农业侦察所需的技术方面投入很少。因此，该计划的短期目标侧重于展示新型硬件技术的潜力，其中包括完整的自上而下植物图像、植物组织采样器、土壤采样器和昆虫/真菌采样器。该研究项目将有助于在航空/农业机器人领域培训 2 名博士生、2 名硕士生和 5 名合作实习生。他们将在顶级会议和科学期刊上发表文章，同时培养行业急需的技能。除了提高全球粮食产量外，据估计，农业领域无人机动力解决方案的总潜在市场为 $32B。