Abstract
Site specific management rationalizes farm inputs and mitigate environmental impacts. Traditionally, low resolution satellite imagery and soil maps are employed for site specific decisions in large scale farms. However, these approaches are not good at sub-field level due to low spatial resolution. To overcome this problem, either manual scouting is employed or extensive high resolution data collection platforms are used. In both cases, the cost outweighs the expected returns. Consequently, variable rate applications are not preferred in large fields. Leaf Area Index (LAI) is a useful measure to monitor crop growth and health for site specific management. In this paper we propose an accurate and scalable process where multispectral remote sensing and proximal sensing data is used to estimate LAI. Crop LAI (CLAI) and Weed LAI (WLAI) are estimated from limited high resolution ground image samples using semantic segmentation. These limited LAIs are extended to the whole field using remote sensing and proximal sensing data. We find that LAIs are spatially related with Soil, Water and Topography (SWAT) maps and are field specific. With increasing weed population in the fields, correlation of WLAI with the SWAT zone increases. However, CLAI remains comparatively consistent across SWAT zones due to variable rate seeding and fertilizer application based on soil variance. Our results demonstrate that LAIs can be predicted accurately from limited high resolution ground imagery, satellite imagery, SWAT, and soil properties maps.
Highlights
Site Specific Management (SSM) is popular among small agricultural farm holdings
Correlational study demonstrates that Weed LAI (WLAI) is higher in wet, saline and low laying zones while it decreases in dry and high SWAT zones
It means that WLAI estimated from limited high resolution ground imagery using semantic segmentation can be extended to the whole field if SWAT zoning and soil properties are known
Summary
Site Specific Management (SSM) is popular among small agricultural farm holdings. It increases farmer profitability by increasing yield and rationalizing farm inputs. It mitigates the detrimental effects of agricultural practices on environment [1]. SSM is performed relatively easier by the farmers in smaller farms because they have access to every inch of the land. It helps them monitor crop health and deploy control measures. With the growing farm size, land and crop information is sparsely accessible, hampering informed decision making for SSM. The farmers’ decision of adopting SSM is largely determined by economic motives [2] which means that cost of data acquisition and processing should be lower than its potential benefits
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