6533b833fe1ef96bd129c7d0
RESEARCH PRODUCT
Weed detection by aerial imagery : toward weed management by UAV
Marine Louargantsubject
ModélisationAcquisition chainChaîne d’acquisitionSpatial and spectral discriminationWeed detectionMultispectral imageImage multispectraleModelDiscriminations spatiale et spectraleDétection d’adventices[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingdescription
The agricultural framework aims to reduce pesticide use on fields. Weed management, which is highly herbicide consuming, became a great issue. In order to develop a weed management service using UAV, this PhD dissertation studies how to adapt the acquisition system (UAV + multispectral camera) developed by AIRINOV to detect weeds in row crops. The acquisition chain was modeled to assess some of its parameters (optical filters and spatial resolution) impact on weed detection quality. Orthoimages and orthorectified images were created using a multispectral camera (4 to 8 filters) with 6 mm to 6 cm spatial resolutions. Several weed location methods were specifically developed to study multispectral images acquired by UAV. They are based on 1) the analysis of vegetation spatial distribution (row detection using the Hough transform and shape analysis), 2) spectral classification of pixels (supervised methods: LDA, QDA, Mahalanobis distance, SVM). In order to improve weed detection, a spectral classification based on training data deduced from spatial analysis was then proposed.Weed infestation maps and recommendation for spot spraying applications were then produced.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-01-01 |