Crop/weed discrimination in perspective agronomic images

Abstract : This paper presents a general method for weed infestation rate estimation for perspective wide-view images dedicated to real-time precision spraying. A colour camera was positioned above crop fields in order to get a wide angle view of crop rows in perspective. Before to test it on in-field images, the algorithm has been optimized on simulated images and its robustness face to different weed infestation rates is analysed. The proposed method can be divided into the two following steps. Firstly a crop row detection is performed from the identification of the vanishing point taking the opportunity of the perspective geometry of the scene. Hence, an algorithm based on a double Hough transform (DHT) is applied. Afterwards, the discrimination between crop and weeds is done by a region-based segmentation method using a blob colouring analysis.The DHT was proved to be applicable to different perspective angles and different spatial frequencies of crop seedlings. Based on the geometrical properties of the scene, the results showed that the DHT has been proved to be a reliable crop row detection method but the crop/weed discrimination algorithm needs to be optimized. The discussion focuses on the efficiency and the limits of this spatial method.
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Submitted on : Friday, June 29, 2018 - 1:53:49 PM
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Christelle Gée, Jérémie Bossu, Gawain Jones, Frederic Truchetet. Crop/weed discrimination in perspective agronomic images. Computers and Electronics in Agriculture, Elsevier, 2008, 60 (1), pp.49 - 59. ⟨10.1016/j.compag.2007.06.003⟩. ⟨hal-01826423⟩

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