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.

地面实况验证 (GTV) 是成功的特定地点农艺建议（例如可变比率处方）的主要组成部分。我们可以根据卫星和无人机图像计算地上生物量 (AGB) 和产量。然而，这些产品都不是地面高分辨率的。目前，农学家前往有限数量的田地，评估不同区域的作物和杂草的生长情况，进行植物株数，拍摄作物照片，并记录影响作物产量的因素，例如作物种植的一致性。每年进行一到两次，以尝试评估影响该季节作物的农艺因素。卫星或无人机图像可以显示某个区域生物量低且生长不良的区域，但无法判断是否是由于太干燥、太湿、盐碱地、植物生长不良、昆虫或肥力差所致。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，该研究合作伙伴提出了基于计算机视觉的 GTV。为此，近端传感器将安装在农田机械上。这些传感器将收集高分辨率图像、土壤电导率、含水量和地形数据。该项目将利用这种近端传感数据与遥感卫星数据相结合，以实现以下四个目标：1）高空间和时间分辨率AGB估计和验证的混合方法，2）均质化管理区域的识别，3）作物种植的一致性，4）地肤杂草管理。将使用先进的机器学习、深度学习和统计工具来开发新方法。该项目将有助于对加拿大大草原范围内的农作物进行特定地点的管理。该项目将使合作伙伴组织在每个田地中收集的验证样本增加 20 倍，分析的田地增加四倍，并将 100 万英亩农业用地的成本减少一半。它还将有助于促进加拿大的环境友好型农业实践。