Abstract
To reduce the amount of herbicides used to eradicate weeds and ensure crop yields, precision spraying can effectively detect and locate weeds in the field thanks to imaging systems. Because weeds are visually similar to crops, color information is not sufficient for effectively detecting them. Multispectral cameras provide radiance images with a high spectral resolution, thus the ability to investigate vegetated surfaces in several narrow spectral bands. Spectral reflectance has to be estimated in order to make weed detection robust against illumination variation. However, this is a challenge when the image is assembled from successive frames that are acquired under varying illumination conditions. In this study, we present an original image formation model that considers illumination variation during radiance image acquisition with a linescan camera. From this model, we deduce a new reflectance estimation method that takes illumination at the frame level into account. We experimentally show that our method is more robust against illumination variation than state-of-the-art methods. We also show that the reflectance features based on our method are more discriminant for outdoor weed detection and identification.
Highlights
We propose a reflectance estimation method that is robust to illumination variations during the multispectral image acquisition that was performed by the Snapscan camera
The parametric LightGBM (LGBM) and non-parametric Quadratic Discriminant Analysis (QDA) classifiers are applied for supervised weed detection and identification problems
This paper first proposes an original image formation model of linescan multispectral cameras, like the Snapscan. It shows how illumination variation during the multispectral image acquisition by this device impacts the measured radiance that is provided by a Lambertian surface element
Summary
During the last decade, sophisticated multispectral sensors have been manufactured and deployed in crop fields, leading to weed detection [1–3]
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