Modelling agronomic images for weed detection and comparison of crop/weed discrimination algorithm performance

Abstract : A new method for weed detection based on modelling agronomic images taken from a virtual camera placed in a virtual field is proposed. The aim was to measure and compare the effectiveness of the developed algorithms. Two sets of images with and without perspective effects were simulated. For images with no perspective, based on Gabor filtering and on the Hough transform, the performance of two crop/inter-row weed discrimination algorithms were tested and compared. The method based on the Hough transform is, in any case, better than the one based on Gabor filtering. For images with perspective effects only, an algorithm based on the Hough transform was tested and an extension to real images is discussed. These tests were done by a comparison between the weed infestation rate detected by these algorithms and the true one. This evaluation was completed with a crop/weed pixel classification and it demonstrated that the algorithm based on a Hough transform gave the best results (up to 90%).
Complete list of metadatas

https://hal-agrosup-dijon.archives-ouvertes.fr/hal-01826398
Contributor : Administrateur Agrosupdijon <>
Submitted on : Friday, June 29, 2018 - 1:39:24 PM
Last modification on : Saturday, July 14, 2018 - 1:05:53 AM

Links full text

Identifiers

Citation

Gawain Jones, Christelle Gée, Frederic Truchetet. Modelling agronomic images for weed detection and comparison of crop/weed discrimination algorithm performance. Precision Agriculture, Springer Verlag, 2009, 10 (1), pp.1 - 15. ⟨10.1007/s11119-008-9086-9⟩. ⟨hal-01826398⟩

Share

Metrics

Record views

55