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

地面真实验证(GTV)是成功的特定地点农艺建议(如可变用量处方)的主要组成部分。我们可以从卫星和无人机图像中计算出地上生物量(AGB)和产量。然而，这些产品在地面上都不是高分辨率的。目前，农学家去有限的田地，他们评估不同地区的作物和杂草的生长，进行植物立地清点，拍摄作物的照片，并就影响作物产量的因素做笔记，如作物建立的一致性。这项工作每年进行一次或两次，以尝试评估影响作物季节收成的农艺因素。卫星或无人机的图像可以显示一个地区可能生物量低、生长不良的地方，但它无法判断是由于太干燥、太潮湿、盐碱化、植物林差、昆虫或肥力差。另一个问题是，人工侦察是主观的、耗时的和昂贵的。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，近距离传感器将安装在农业田间机械上。这些传感器将收集高分辨率图像、土壤电导率、水分含量和地形数据。该项目将利用这些近地遥感数据与遥感卫星数据相结合，以实现以下四个目标：1)高时空分辨率AGB估计和验证的混合方法；2)同质化管理区的确定；3)作物设施的一致性；4)Kochia杂草的管理。 将使用先进的机器学习、深度学习和统计工具来开发新的方法。该项目将帮助在加拿大大草原范围内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个田地收集20倍的验证样本，分析4倍以上的田地，并将100万英亩农业用地的成本削减一半。它还将有助于在加拿大推广环境友好型农业做法。

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.