Ground Truth Validation of Crop Growth Cycle Using High Resolution Proximal and Remote Sensing

使用高分辨率近端和遥感对作物生长周期进行地面实况验证

• 批准号: 549723-2019
• 负责人: Bais, AbdulA
• 金额: $ 2.19万
• 依托单位: University of Regina
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752743
• 关键词: Ground; Truth; Validation; Crop; Growth

Ground Truth Validation (GTV) is a major component for successful site-specific agronomic recommendations like variable rate prescriptions. We can calculate Above Ground Biomass (AGB) and yield from satellite and drone imagery. However, none of these products are high resolution at the ground level. Currently, agronomists go to a limited number of fields and they assess the growth of crop and weeds in different zones, do plant stand count, take pictures of the crop, and make notes on things affecting crop yield like consistency of crop establishment. This is done once or twice per year to try and assess the agronomic factors influencing the crop during the season. A satellite or drone image can show where an area may be low in biomass and have poor growth, but it does not tell whether it is due to being too dry, too wet, saline, poor plant stand, insects, or poor fertility. Another issue is that manual scouting is subjective, time-consuming and costly. To address these issues, this research partnership proposes computer vision based GTV. For this purpose, proximal sensors will be mounted on agriculture field machinery. These sensors will collect high resolution imagery, soil electrical conductivity, water content and topography data. The project will use this proximal sensing data in combination with remote sensing satellite data to achieve the following four objectives: 1) A hybrid approach for high spatial and temporal resolution AGB estimation and validation, 2) Identification of homogenized management zones, 3) Consistency of crop establishment, 4) Kochia weed management.Advanced machine learning, deep learning and statistical tools will be used to develop novel methodologies. The project will help perform site specific management of crops at the scale of Canadian Prairies. This project will enable the partner organizations collect 20 times more validation samples per field, analyze four times more fields, and cut the costs in half for one million acres of agricultural land. It will also help promote environment friendly agriculture practices in Canada.

地面真相验证（GTV）是成功特定地点的农艺建议（例如可变费率处方）的主要组成部分。我们可以从卫星和无人机图像中计算上面的生物量（AGB）和产量。但是，这些产品在地面上都不是高分辨率。目前，农艺学家进入有限数量的领域，他们评估不同区域中农作物和杂草的生长，做工厂的数量，为农作物拍照，并记下影响作物产量的事物，例如作物建立的一致性。这是每年一次或两次，以尝试评估影响本季节作物的农艺因素。卫星或无人机图像可以显示一个区域可能较低的生物量且生长较差，但并不能说明它是否是由于太干，太湿，盐水，植物林，植物林，昆虫或生育不良。另一个问题是，手动侦察是主观的，耗时且昂贵的。为了解决这些问题，该研究合作伙伴关系提出了基于计算机视觉的GTV。为此，近端传感器将安装在农业场机械上。这些传感器将收集高分辨率图像，土壤电导率，水含量和地形数据。该项目将使用此近端传感数据与遥感卫星数据结合使用以下四个目标：1）用于高空间和时间分辨率AGB估计和验证的混合方法，2）识别均质管理区域，3）3）农作物建立，4）Kochia Weed Management。先进的机器学习，深度学习和统计工具将用于开发新颖的方法。该项目将有助于在加拿大大草原的规模上对农作物进行特定的现场管理。该项目将使合作伙伴组织每个领域收集20倍的验证样本，分析四倍的领域，并将成本减少到一百万英亩的农业土地上。它还将有助于促进加拿大的环境友好农业实践。