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

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:
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
项目状态:
已结题
起止时间:
2022-01-01 至 2023-12-31

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中文摘要
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英文摘要
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.
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