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, Abdul
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=701244
• 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（Ground Truth Validation，GTV）是成功的特定地点农艺学建议（如变量配方）的主要组成部分。我们可以通过卫星和无人机图像计算地上生物量（AGB）和产量。然而，这些产品在地面上都不是高分辨率的。目前，农学家只去有限的几块田地，他们评估不同区域的作物和杂草的生长情况，进行植物生长计数，拍摄作物照片，并记录影响作物产量的因素，如作物生长的一致性。每年进行一次或两次，以尝试和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一个地区的生物量可能较低，生长不良，但它不能告诉它是否是由于太干，太湿，盐，植物生长不良，昆虫或肥力差。另一个问题是，人工侦察是主观的，耗时和昂贵的。为了解决这些问题，该研究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现场机械上。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。该项目将利用这一近距离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）确定均匀化管理区，3）作物种植的一致性，4）地肤杂草管理。 先进的机器学习、深度学习和统计工具将用于开发新的方法。该项目将有助于在加拿大大草原的规模上对作物进行特定地点的管理。该项目将使合作伙伴组织能够在每块田地收集20倍以上的验证样本，分析4倍以上的田地，并将100万英亩农业用地的成本降低一半。它还将有助于促进加拿大的环境友好型农业做法。